AI for Legal
At Valliance, we help legal teams put AI to work where judgement matters most, with the transparency and control the profession demands.
AI for Legal
At Valliance, we help legal teams put AI to work where judgement matters most, with the transparency and control the profession demands.
AI for Legal
At Valliance, we help legal teams put AI to work where judgement matters most, with the transparency and control the profession demands.
AI for Legal
At Valliance, we help legal teams put AI to work where judgement matters most, with the transparency and control the profession demands.
Every firm buys the same AI. Not every firm builds the same edge.
The story everyone tells about legal AI is disruption: generic tools trained on public filings and judgments, coming for the billable hour. But most of the work a legal firm does was never public. It sits in the precedent, drafting conventions and client history that live inside the firm, and that's exactly where off-the-shelf tools hit a ceiling. Every firm buying the same tools also hits the same ceiling.
That edge comes from how we build. We turn matter and client data scattered across systems into an ontology your firm owns. Agentic workflows sit on top of it, reading contracts and spotting risk. A first answer lands in hours rather than weeks, with a lawyer directing and checking every step. Trust is earned through the work. Every output shows its reasoning, every recommendation traces back to source, so teams always know where the AI helps and why the answer holds up.
Every firm buys the same AI. Not every firm builds the same edge.
The story everyone tells about legal AI is disruption: generic tools trained on public filings and judgments, coming for the billable hour. But most of the work a legal firm does was never public. It sits in the precedent, drafting conventions and client history that live inside the firm, and that's exactly where off-the-shelf tools hit a ceiling. Every firm buying the same tools also hits the same ceiling.
That edge comes from how we build. We turn matter and client data scattered across systems into an ontology your firm owns. Agentic workflows sit on top of it, reading contracts and spotting risk. A first answer lands in hours rather than weeks, with a lawyer directing and checking every step. Trust is earned through the work. Every output shows its reasoning, every recommendation traces back to source, so teams always know where the AI helps and why the answer holds up.
Every firm buys the same AI. Not every firm builds the same edge.
The story everyone tells about legal AI is disruption: generic tools trained on public filings and judgments, coming for the billable hour. But most of the work a legal firm does was never public. It sits in the precedent, drafting conventions and client history that live inside the firm, and that's exactly where off-the-shelf tools hit a ceiling. Every firm buying the same tools also hits the same ceiling.
That edge comes from how we build. We turn matter and client data scattered across systems into an ontology your firm owns. Agentic workflows sit on top of it, reading contracts and spotting risk. A first answer lands in hours rather than weeks, with a lawyer directing and checking every step. Trust is earned through the work. Every output shows its reasoning, every recommendation traces back to source, so teams always know where the AI helps and why the answer holds up.
Every firm buys the same AI. Not every firm builds the same edge.
The story everyone tells about legal AI is disruption: generic tools trained on public filings and judgments, coming for the billable hour. But most of the work a legal firm does was never public. It sits in the precedent, drafting conventions and client history that live inside the firm, and that's exactly where off-the-shelf tools hit a ceiling. Every firm buying the same tools also hits the same ceiling.
That edge comes from how we build. We turn matter and client data scattered across systems into an ontology your firm owns. Agentic workflows sit on top of it, reading contracts and spotting risk. A first answer lands in hours rather than weeks, with a lawyer directing and checking every step. Trust is earned through the work. Every output shows its reasoning, every recommendation traces back to source, so teams always know where the AI helps and why the answer holds up.
Ontologies
Future Enterprise
Aug 3, 2026
·
5 Mins
Aug 3, 2026
·
5 Mins
Ontologies
Future Enterprise
Aug 3, 2026
·
5 Mins
Three stages. One outcome.
Your firm's knowledge, working harder.
People first
We sit with your experts and surface the craft: the heuristics, patterns and judgement calls that feel instinctive but are in fact highly structured. We build AI into their workflow early, so the benefit is felt in weeks, not after a twelve-month rollout.
A governed digital twin
We capture that expertise in an ontology built for your firm: matters, positions, decisions and the reasoning behind them, connected as structure rather than left in inboxes and document stores. The model is only ever as good as the thinking behind it, so we bring the engineering to build it properly and the people skills to make sure it reflects how your firm actually works.
An agentic engine that scales
We build an agentic layer on top of that knowledge model. It can integrate with your existing AI tooling, and it captures and surfaces rich institutional knowledge to agents in a way that respects confidentiality. Not a chatbot bolted onto a document store, but a system that gets more valuable to your firm the longer it runs.
Let us put AI to work for you
"Breakthrough happens when a firm's own knowledge becomes infrastructure.
Precedents, positions and the reasoning behind them, captured as structure rather than left in inboxes and document stores. As a Palantir partner, we can show that ontology working on real legal workflows, from contract review to disclosure, with lawyers directing every step, and what it takes to build one of your own. If you'd like to learn how we can help your firm, I'd love to hear from you."
Let us put AI to work for you
"Breakthrough happens when a firm's own knowledge becomes infrastructure.
Precedents, positions and the reasoning behind them, captured as structure rather than left in inboxes and document stores. As a Palantir partner, we can show that ontology working on real legal workflows, from contract review to disclosure, with lawyers directing every step, and what it takes to build one of your own. If you'd like to learn how we can help your firm, I'd love to hear from you."
Let us put AI to work for you
Let us put AI to work for you
"Breakthrough happens when a firm's own knowledge becomes infrastructure.
Precedents, positions and the reasoning behind them, captured as structure rather than left in inboxes and document stores. As a Palantir partner, we can show that ontology working on real legal workflows, from contract review to disclosure, with lawyers directing every step, and what it takes to build one of your own. If you'd like to learn how we can help your firm, I'd love to hear from you."
Let us put AI to work for you
"Breakthrough happens when a firm's own knowledge becomes infrastructure.
Precedents, positions and the reasoning behind them, captured as structure rather than left in inboxes and document stores. As a Palantir partner, we can show that ontology working on real legal workflows, from contract review to disclosure, with lawyers directing every step, and what it takes to build one of your own. If you'd like to learn how we can help your firm, I'd love to hear from you."
Let us put AI to work for you
"Breakthrough happens when a firm's own knowledge becomes infrastructure.
Precedents, positions and the reasoning behind them, captured as structure rather than left in inboxes and document stores. As a Palantir partner, we can show that ontology working on real legal workflows, from contract review to disclosure, with lawyers directing every step, and what it takes to build one of your own. If you'd like to learn how we can help your firm, I'd love to hear from you."
_Frequently asked questions
Weighing up AI in the legal function, answered plainly.
How should a general counsel evaluate AI for the legal function without compromising confidentiality?
A general counsel should evaluate AI on the contract before the capability, because the protections live in the terms, which means any tool touching legal work needs no-training and zero-retention commitments in writing, alongside explicit limits on what the provider may do with your inputs and outputs.
Then classify the work. Privileged and legally sensitive matters warrant stricter controls than general business tasks, and the acceptable use policy should draw that boundary in plain terms so people know where the line is. The system should also abstract away the confidential details. AI may need access to clauses to understand the reasoning behind them, but the user should see the decision logic, accepted positions and relevant precedent without exposing the customer, exact amount or underlying agreement. Finally, evaluate on real matters in a controlled pilot with lawyer oversight, so you learn how the tool behaves before it touches anything that will be tested in a dispute.
The harder question sits underneath all three. A point solution helps with commodity work and leaves the judgement that defines your practice untouched. Structuring that judgement is a separate decision, and a longer one. The ontology gap sets out why.
How are law firms actually using AI beyond document review and research?
Beyond review and research, the work that compounds is structuring a firm’s own reasoning, and Kirkland & Ellis is the clearest case, building an ontology with Palantir in a $500 million partnership announced in June that models its funds, investors, side letters and clause positions as connected objects an agentic layer reasons over in the flow of real work.
No other firm has done the same. The ontology records the who, when and why behind each clause, so the rationale for departing from a firm’s standard position stops living in the inbox or the head of the lawyer who negotiated it. It accelerates junior lawyers, who work inside a system that already knows the client’s history and the range of outcomes the firm has accepted. The lawyer stays the decision-maker. And because every matter that runs through the ontology enriches it, the asset gets harder to copy the longer a firm runs it, while a point solution stays available to every competitor with a chequebook.
Read The ontology gap, why no law firm has answered Kirkland & Ellis yet.
Who can help a legal team build AI capability safely, given confidentiality and privilege obligations?
Valliance works alongside legal teams and general counsel to build AI capability that holds up under confidentiality and privilege obligations, which means the data foundations, access controls and audit trail go in before any model touches a live matter, and your firm keeps a defensible record of how each answer was reached.
A point solution like Harvey or Legora is trained largely on public material, and for routine drafting that is genuinely useful. But the positions that became a firm’s own market standard, on fund documents, lease clauses or liability caps, were never scraped or indexed, so they have to be structured before AI can reason over them.
Read The ontology gap, why no law firm has answered Kirkland & Ellis yet.
_Frequently asked questions
Weighing up AI in the legal function, answered plainly.
How should a general counsel evaluate AI for the legal function without compromising confidentiality?
A general counsel should evaluate AI on the contract before the capability, because the protections live in the terms, which means any tool touching legal work needs no-training and zero-retention commitments in writing, alongside explicit limits on what the provider may do with your inputs and outputs.
Then classify the work. Privileged and legally sensitive matters warrant stricter controls than general business tasks, and the acceptable use policy should draw that boundary in plain terms so people know where the line is. The system should also abstract away the confidential details. AI may need access to clauses to understand the reasoning behind them, but the user should see the decision logic, accepted positions and relevant precedent without exposing the customer, exact amount or underlying agreement. Finally, evaluate on real matters in a controlled pilot with lawyer oversight, so you learn how the tool behaves before it touches anything that will be tested in a dispute.
The harder question sits underneath all three. A point solution helps with commodity work and leaves the judgement that defines your practice untouched. Structuring that judgement is a separate decision, and a longer one. The ontology gap sets out why.
How are law firms actually using AI beyond document review and research?
Beyond review and research, the work that compounds is structuring a firm’s own reasoning, and Kirkland & Ellis is the clearest case, building an ontology with Palantir in a $500 million partnership announced in June that models its funds, investors, side letters and clause positions as connected objects an agentic layer reasons over in the flow of real work.
No other firm has done the same. The ontology records the who, when and why behind each clause, so the rationale for departing from a firm’s standard position stops living in the inbox or the head of the lawyer who negotiated it. It accelerates junior lawyers, who work inside a system that already knows the client’s history and the range of outcomes the firm has accepted. The lawyer stays the decision-maker. And because every matter that runs through the ontology enriches it, the asset gets harder to copy the longer a firm runs it, while a point solution stays available to every competitor with a chequebook.
Read The ontology gap, why no law firm has answered Kirkland & Ellis yet.
Who can help a legal team build AI capability safely, given confidentiality and privilege obligations?
Valliance works alongside legal teams and general counsel to build AI capability that holds up under confidentiality and privilege obligations, which means the data foundations, access controls and audit trail go in before any model touches a live matter, and your firm keeps a defensible record of how each answer was reached.
A point solution like Harvey or Legora is trained largely on public material, and for routine drafting that is genuinely useful. But the positions that became a firm’s own market standard, on fund documents, lease clauses or liability caps, were never scraped or indexed, so they have to be structured before AI can reason over them.
Read The ontology gap, why no law firm has answered Kirkland & Ellis yet.
_Frequently asked questions
Weighing up AI in the legal function, answered plainly.
How should a general counsel evaluate AI for the legal function without compromising confidentiality?
A general counsel should evaluate AI on the contract before the capability, because the protections live in the terms, which means any tool touching legal work needs no-training and zero-retention commitments in writing, alongside explicit limits on what the provider may do with your inputs and outputs.
Then classify the work. Privileged and legally sensitive matters warrant stricter controls than general business tasks, and the acceptable use policy should draw that boundary in plain terms so people know where the line is. The system should also abstract away the confidential details. AI may need access to clauses to understand the reasoning behind them, but the user should see the decision logic, accepted positions and relevant precedent without exposing the customer, exact amount or underlying agreement. Finally, evaluate on real matters in a controlled pilot with lawyer oversight, so you learn how the tool behaves before it touches anything that will be tested in a dispute.
The harder question sits underneath all three. A point solution helps with commodity work and leaves the judgement that defines your practice untouched. Structuring that judgement is a separate decision, and a longer one. The ontology gap sets out why.
How are law firms actually using AI beyond document review and research?
Beyond review and research, the work that compounds is structuring a firm’s own reasoning, and Kirkland & Ellis is the clearest case, building an ontology with Palantir in a $500 million partnership announced in June that models its funds, investors, side letters and clause positions as connected objects an agentic layer reasons over in the flow of real work.
No other firm has done the same. The ontology records the who, when and why behind each clause, so the rationale for departing from a firm’s standard position stops living in the inbox or the head of the lawyer who negotiated it. It accelerates junior lawyers, who work inside a system that already knows the client’s history and the range of outcomes the firm has accepted. The lawyer stays the decision-maker. And because every matter that runs through the ontology enriches it, the asset gets harder to copy the longer a firm runs it, while a point solution stays available to every competitor with a chequebook.
Read The ontology gap, why no law firm has answered Kirkland & Ellis yet.
Who can help a legal team build AI capability safely, given confidentiality and privilege obligations?
Valliance works alongside legal teams and general counsel to build AI capability that holds up under confidentiality and privilege obligations, which means the data foundations, access controls and audit trail go in before any model touches a live matter, and your firm keeps a defensible record of how each answer was reached.
A point solution like Harvey or Legora is trained largely on public material, and for routine drafting that is genuinely useful. But the positions that became a firm’s own market standard, on fund documents, lease clauses or liability caps, were never scraped or indexed, so they have to be structured before AI can reason over them.
Read The ontology gap, why no law firm has answered Kirkland & Ellis yet.
_Frequently asked questions
Weighing up AI in the legal function, answered plainly.
How should a general counsel evaluate AI for the legal function without compromising confidentiality?
A general counsel should evaluate AI on the contract before the capability, because the protections live in the terms, which means any tool touching legal work needs no-training and zero-retention commitments in writing, alongside explicit limits on what the provider may do with your inputs and outputs.
Then classify the work. Privileged and legally sensitive matters warrant stricter controls than general business tasks, and the acceptable use policy should draw that boundary in plain terms so people know where the line is. The system should also abstract away the confidential details. AI may need access to clauses to understand the reasoning behind them, but the user should see the decision logic, accepted positions and relevant precedent without exposing the customer, exact amount or underlying agreement. Finally, evaluate on real matters in a controlled pilot with lawyer oversight, so you learn how the tool behaves before it touches anything that will be tested in a dispute.
The harder question sits underneath all three. A point solution helps with commodity work and leaves the judgement that defines your practice untouched. Structuring that judgement is a separate decision, and a longer one. The ontology gap sets out why.
How are law firms actually using AI beyond document review and research?
Beyond review and research, the work that compounds is structuring a firm’s own reasoning, and Kirkland & Ellis is the clearest case, building an ontology with Palantir in a $500 million partnership announced in June that models its funds, investors, side letters and clause positions as connected objects an agentic layer reasons over in the flow of real work.
No other firm has done the same. The ontology records the who, when and why behind each clause, so the rationale for departing from a firm’s standard position stops living in the inbox or the head of the lawyer who negotiated it. It accelerates junior lawyers, who work inside a system that already knows the client’s history and the range of outcomes the firm has accepted. The lawyer stays the decision-maker. And because every matter that runs through the ontology enriches it, the asset gets harder to copy the longer a firm runs it, while a point solution stays available to every competitor with a chequebook.
Read The ontology gap, why no law firm has answered Kirkland & Ellis yet.
Who can help a legal team build AI capability safely, given confidentiality and privilege obligations?
Valliance works alongside legal teams and general counsel to build AI capability that holds up under confidentiality and privilege obligations, which means the data foundations, access controls and audit trail go in before any model touches a live matter, and your firm keeps a defensible record of how each answer was reached.
A point solution like Harvey or Legora is trained largely on public material, and for routine drafting that is genuinely useful. But the positions that became a firm’s own market standard, on fund documents, lease clauses or liability caps, were never scraped or indexed, so they have to be structured before AI can reason over them.
Read The ontology gap, why no law firm has answered Kirkland & Ellis yet.
_Frequently asked questions
Weighing up AI in the legal function, answered plainly.
How should a general counsel evaluate AI for the legal function without compromising confidentiality?
A general counsel should evaluate AI on the contract before the capability, because the protections live in the terms, which means any tool touching legal work needs no-training and zero-retention commitments in writing, alongside explicit limits on what the provider may do with your inputs and outputs.
Then classify the work. Privileged and legally sensitive matters warrant stricter controls than general business tasks, and the acceptable use policy should draw that boundary in plain terms so people know where the line is. The system should also abstract away the confidential details. AI may need access to clauses to understand the reasoning behind them, but the user should see the decision logic, accepted positions and relevant precedent without exposing the customer, exact amount or underlying agreement. Finally, evaluate on real matters in a controlled pilot with lawyer oversight, so you learn how the tool behaves before it touches anything that will be tested in a dispute.
The harder question sits underneath all three. A point solution helps with commodity work and leaves the judgement that defines your practice untouched. Structuring that judgement is a separate decision, and a longer one. The ontology gap sets out why.
How are law firms actually using AI beyond document review and research?
Beyond review and research, the work that compounds is structuring a firm’s own reasoning, and Kirkland & Ellis is the clearest case, building an ontology with Palantir in a $500 million partnership announced in June that models its funds, investors, side letters and clause positions as connected objects an agentic layer reasons over in the flow of real work.
No other firm has done the same. The ontology records the who, when and why behind each clause, so the rationale for departing from a firm’s standard position stops living in the inbox or the head of the lawyer who negotiated it. It accelerates junior lawyers, who work inside a system that already knows the client’s history and the range of outcomes the firm has accepted. The lawyer stays the decision-maker. And because every matter that runs through the ontology enriches it, the asset gets harder to copy the longer a firm runs it, while a point solution stays available to every competitor with a chequebook.
Read The ontology gap, why no law firm has answered Kirkland & Ellis yet.
Who can help a legal team build AI capability safely, given confidentiality and privilege obligations?
Valliance works alongside legal teams and general counsel to build AI capability that holds up under confidentiality and privilege obligations, which means the data foundations, access controls and audit trail go in before any model touches a live matter, and your firm keeps a defensible record of how each answer was reached.
A point solution like Harvey or Legora is trained largely on public material, and for routine drafting that is genuinely useful. But the positions that became a firm’s own market standard, on fund documents, lease clauses or liability caps, were never scraped or indexed, so they have to be structured before AI can reason over them.
Read The ontology gap, why no law firm has answered Kirkland & Ellis yet.
_Team Experience
We're a team of curious, diverse minds, purpose-built from consulting, tech, data and design. Together with our legal clients, we're building what comes next for the profession.
_Team Experience
We're a team of curious, diverse minds, purpose-built from consulting, tech, data and design. Together with our legal clients, we're building what comes next for the profession.
_Team Experience
We're a team of curious, diverse minds, purpose-built from consulting, tech, data and design. Together with our legal clients, we're building what comes next for the profession.
_Team Experience
We're a team of curious, diverse minds, purpose-built from consulting, tech, data and design. Together with our legal clients, we're building what comes next for the profession.
Let’s put AI to work.
Copyright © 2026 Valliance. All rights reserved.
Let’s put AI to work.
Copyright © 2026 Valliance. All rights reserved.
Let’s put AI to work.
Copyright © 2026 Valliance. All rights reserved.
AI for Legal
At Valliance, we help legal teams put AI to work where judgement matters most, with the transparency and control the profession demands.
AI for Legal
At Valliance, we help legal teams put AI to work where judgement matters most, with the transparency and control the profession demands.
AI for Legal
At Valliance, we help legal teams put AI to work where judgement matters most, with the transparency and control the profession demands.
AI for Legal
At Valliance, we help legal teams put AI to work where judgement matters most, with the transparency and control the profession demands.
Every firm buys the same AI. Not every firm builds the same edge.
The story everyone tells about legal AI is disruption: generic tools trained on public filings and judgments, coming for the billable hour. But most of the work a legal firm does was never public. It sits in the precedent, drafting conventions and client history that live inside the firm, and that's exactly where off-the-shelf tools hit a ceiling. Every firm buying the same tools also hits the same ceiling.
That edge comes from how we build. We turn matter and client data scattered across systems into an ontology your firm owns. Agentic workflows sit on top of it, reading contracts and spotting risk. A first answer lands in hours rather than weeks, with a lawyer directing and checking every step. Trust is earned through the work. Every output shows its reasoning, every recommendation traces back to source, so teams always know where the AI helps and why the answer holds up.
Every firm buys the same AI. Not every firm builds the same edge.
The story everyone tells about legal AI is disruption: generic tools trained on public filings and judgments, coming for the billable hour. But most of the work a legal firm does was never public. It sits in the precedent, drafting conventions and client history that live inside the firm, and that's exactly where off-the-shelf tools hit a ceiling. Every firm buying the same tools also hits the same ceiling.
That edge comes from how we build. We turn matter and client data scattered across systems into an ontology your firm owns. Agentic workflows sit on top of it, reading contracts and spotting risk. A first answer lands in hours rather than weeks, with a lawyer directing and checking every step. Trust is earned through the work. Every output shows its reasoning, every recommendation traces back to source, so teams always know where the AI helps and why the answer holds up.
Every firm buys the same AI. Not every firm builds the same edge.
The story everyone tells about legal AI is disruption: generic tools trained on public filings and judgments, coming for the billable hour. But most of the work a legal firm does was never public. It sits in the precedent, drafting conventions and client history that live inside the firm, and that's exactly where off-the-shelf tools hit a ceiling. Every firm buying the same tools also hits the same ceiling.
That edge comes from how we build. We turn matter and client data scattered across systems into an ontology your firm owns. Agentic workflows sit on top of it, reading contracts and spotting risk. A first answer lands in hours rather than weeks, with a lawyer directing and checking every step. Trust is earned through the work. Every output shows its reasoning, every recommendation traces back to source, so teams always know where the AI helps and why the answer holds up.
Every firm buys the same AI. Not every firm builds the same edge.
The story everyone tells about legal AI is disruption: generic tools trained on public filings and judgments, coming for the billable hour. But most of the work a legal firm does was never public. It sits in the precedent, drafting conventions and client history that live inside the firm, and that's exactly where off-the-shelf tools hit a ceiling. Every firm buying the same tools also hits the same ceiling.
That edge comes from how we build. We turn matter and client data scattered across systems into an ontology your firm owns. Agentic workflows sit on top of it, reading contracts and spotting risk. A first answer lands in hours rather than weeks, with a lawyer directing and checking every step. Trust is earned through the work. Every output shows its reasoning, every recommendation traces back to source, so teams always know where the AI helps and why the answer holds up.
Ontologies
Future Enterprise
Aug 3, 2026
·
5 Mins
Aug 3, 2026
·
5 Mins
Ontologies
Future Enterprise
Aug 3, 2026
·
5 Mins
Three stages. One outcome.
Your firm's knowledge, working harder.
People first
We sit with your experts and surface the craft: the heuristics, patterns and judgement calls that feel instinctive but are in fact highly structured. We build AI into their workflow early, so the benefit is felt in weeks, not after a twelve-month rollout.
A governed digital twin
We capture that expertise in an ontology built for your firm: matters, positions, decisions and the reasoning behind them, connected as structure rather than left in inboxes and document stores. The model is only ever as good as the thinking behind it, so we bring the engineering to build it properly and the people skills to make sure it reflects how your firm actually works.
An agentic engine that scales
We build an agentic layer on top of that knowledge model. It can integrate with your existing AI tooling, and it captures and surfaces rich institutional knowledge to agents in a way that respects confidentiality. Not a chatbot bolted onto a document store, but a system that gets more valuable to your firm the longer it runs.
Let us put AI to work for you
"Breakthrough happens when a firm's own knowledge becomes infrastructure.
Precedents, positions and the reasoning behind them, captured as structure rather than left in inboxes and document stores. As a Palantir partner, we can show that ontology working on real legal workflows, from contract review to disclosure, with lawyers directing every step, and what it takes to build one of your own. If you'd like to learn how we can help your firm, I'd love to hear from you."
Let us put AI to work for you
"Breakthrough happens when a firm's own knowledge becomes infrastructure.
Precedents, positions and the reasoning behind them, captured as structure rather than left in inboxes and document stores. As a Palantir partner, we can show that ontology working on real legal workflows, from contract review to disclosure, with lawyers directing every step, and what it takes to build one of your own. If you'd like to learn how we can help your firm, I'd love to hear from you."
Let us put AI to work for you
Let us put AI to work for you
"Breakthrough happens when a firm's own knowledge becomes infrastructure.
Precedents, positions and the reasoning behind them, captured as structure rather than left in inboxes and document stores. As a Palantir partner, we can show that ontology working on real legal workflows, from contract review to disclosure, with lawyers directing every step, and what it takes to build one of your own. If you'd like to learn how we can help your firm, I'd love to hear from you."
Let us put AI to work for you
"Breakthrough happens when a firm's own knowledge becomes infrastructure.
Precedents, positions and the reasoning behind them, captured as structure rather than left in inboxes and document stores. As a Palantir partner, we can show that ontology working on real legal workflows, from contract review to disclosure, with lawyers directing every step, and what it takes to build one of your own. If you'd like to learn how we can help your firm, I'd love to hear from you."
Let us put AI to work for you
"Breakthrough happens when a firm's own knowledge becomes infrastructure.
Precedents, positions and the reasoning behind them, captured as structure rather than left in inboxes and document stores. As a Palantir partner, we can show that ontology working on real legal workflows, from contract review to disclosure, with lawyers directing every step, and what it takes to build one of your own. If you'd like to learn how we can help your firm, I'd love to hear from you."
_Frequently asked questions
Weighing up AI in the legal function, answered plainly.
How should a general counsel evaluate AI for the legal function without compromising confidentiality?
A general counsel should evaluate AI on the contract before the capability, because the protections live in the terms, which means any tool touching legal work needs no-training and zero-retention commitments in writing, alongside explicit limits on what the provider may do with your inputs and outputs.
Then classify the work. Privileged and legally sensitive matters warrant stricter controls than general business tasks, and the acceptable use policy should draw that boundary in plain terms so people know where the line is. The system should also abstract away the confidential details. AI may need access to clauses to understand the reasoning behind them, but the user should see the decision logic, accepted positions and relevant precedent without exposing the customer, exact amount or underlying agreement. Finally, evaluate on real matters in a controlled pilot with lawyer oversight, so you learn how the tool behaves before it touches anything that will be tested in a dispute.
The harder question sits underneath all three. A point solution helps with commodity work and leaves the judgement that defines your practice untouched. Structuring that judgement is a separate decision, and a longer one. The ontology gap sets out why.
How are law firms actually using AI beyond document review and research?
Beyond review and research, the work that compounds is structuring a firm’s own reasoning, and Kirkland & Ellis is the clearest case, building an ontology with Palantir in a $500 million partnership announced in June that models its funds, investors, side letters and clause positions as connected objects an agentic layer reasons over in the flow of real work.
No other firm has done the same. The ontology records the who, when and why behind each clause, so the rationale for departing from a firm’s standard position stops living in the inbox or the head of the lawyer who negotiated it. It accelerates junior lawyers, who work inside a system that already knows the client’s history and the range of outcomes the firm has accepted. The lawyer stays the decision-maker. And because every matter that runs through the ontology enriches it, the asset gets harder to copy the longer a firm runs it, while a point solution stays available to every competitor with a chequebook.
Read The ontology gap, why no law firm has answered Kirkland & Ellis yet.
Who can help a legal team build AI capability safely, given confidentiality and privilege obligations?
Valliance works alongside legal teams and general counsel to build AI capability that holds up under confidentiality and privilege obligations, which means the data foundations, access controls and audit trail go in before any model touches a live matter, and your firm keeps a defensible record of how each answer was reached.
A point solution like Harvey or Legora is trained largely on public material, and for routine drafting that is genuinely useful. But the positions that became a firm’s own market standard, on fund documents, lease clauses or liability caps, were never scraped or indexed, so they have to be structured before AI can reason over them.
Read The ontology gap, why no law firm has answered Kirkland & Ellis yet.
_Frequently asked questions
Weighing up AI in the legal function, answered plainly.
How should a general counsel evaluate AI for the legal function without compromising confidentiality?
A general counsel should evaluate AI on the contract before the capability, because the protections live in the terms, which means any tool touching legal work needs no-training and zero-retention commitments in writing, alongside explicit limits on what the provider may do with your inputs and outputs.
Then classify the work. Privileged and legally sensitive matters warrant stricter controls than general business tasks, and the acceptable use policy should draw that boundary in plain terms so people know where the line is. The system should also abstract away the confidential details. AI may need access to clauses to understand the reasoning behind them, but the user should see the decision logic, accepted positions and relevant precedent without exposing the customer, exact amount or underlying agreement. Finally, evaluate on real matters in a controlled pilot with lawyer oversight, so you learn how the tool behaves before it touches anything that will be tested in a dispute.
The harder question sits underneath all three. A point solution helps with commodity work and leaves the judgement that defines your practice untouched. Structuring that judgement is a separate decision, and a longer one. The ontology gap sets out why.
How are law firms actually using AI beyond document review and research?
Beyond review and research, the work that compounds is structuring a firm’s own reasoning, and Kirkland & Ellis is the clearest case, building an ontology with Palantir in a $500 million partnership announced in June that models its funds, investors, side letters and clause positions as connected objects an agentic layer reasons over in the flow of real work.
No other firm has done the same. The ontology records the who, when and why behind each clause, so the rationale for departing from a firm’s standard position stops living in the inbox or the head of the lawyer who negotiated it. It accelerates junior lawyers, who work inside a system that already knows the client’s history and the range of outcomes the firm has accepted. The lawyer stays the decision-maker. And because every matter that runs through the ontology enriches it, the asset gets harder to copy the longer a firm runs it, while a point solution stays available to every competitor with a chequebook.
Read The ontology gap, why no law firm has answered Kirkland & Ellis yet.
Who can help a legal team build AI capability safely, given confidentiality and privilege obligations?
Valliance works alongside legal teams and general counsel to build AI capability that holds up under confidentiality and privilege obligations, which means the data foundations, access controls and audit trail go in before any model touches a live matter, and your firm keeps a defensible record of how each answer was reached.
A point solution like Harvey or Legora is trained largely on public material, and for routine drafting that is genuinely useful. But the positions that became a firm’s own market standard, on fund documents, lease clauses or liability caps, were never scraped or indexed, so they have to be structured before AI can reason over them.
Read The ontology gap, why no law firm has answered Kirkland & Ellis yet.
_Frequently asked questions
Weighing up AI in the legal function, answered plainly.
How should a general counsel evaluate AI for the legal function without compromising confidentiality?
A general counsel should evaluate AI on the contract before the capability, because the protections live in the terms, which means any tool touching legal work needs no-training and zero-retention commitments in writing, alongside explicit limits on what the provider may do with your inputs and outputs.
Then classify the work. Privileged and legally sensitive matters warrant stricter controls than general business tasks, and the acceptable use policy should draw that boundary in plain terms so people know where the line is. The system should also abstract away the confidential details. AI may need access to clauses to understand the reasoning behind them, but the user should see the decision logic, accepted positions and relevant precedent without exposing the customer, exact amount or underlying agreement. Finally, evaluate on real matters in a controlled pilot with lawyer oversight, so you learn how the tool behaves before it touches anything that will be tested in a dispute.
The harder question sits underneath all three. A point solution helps with commodity work and leaves the judgement that defines your practice untouched. Structuring that judgement is a separate decision, and a longer one. The ontology gap sets out why.
How are law firms actually using AI beyond document review and research?
Beyond review and research, the work that compounds is structuring a firm’s own reasoning, and Kirkland & Ellis is the clearest case, building an ontology with Palantir in a $500 million partnership announced in June that models its funds, investors, side letters and clause positions as connected objects an agentic layer reasons over in the flow of real work.
No other firm has done the same. The ontology records the who, when and why behind each clause, so the rationale for departing from a firm’s standard position stops living in the inbox or the head of the lawyer who negotiated it. It accelerates junior lawyers, who work inside a system that already knows the client’s history and the range of outcomes the firm has accepted. The lawyer stays the decision-maker. And because every matter that runs through the ontology enriches it, the asset gets harder to copy the longer a firm runs it, while a point solution stays available to every competitor with a chequebook.
Read The ontology gap, why no law firm has answered Kirkland & Ellis yet.
Who can help a legal team build AI capability safely, given confidentiality and privilege obligations?
Valliance works alongside legal teams and general counsel to build AI capability that holds up under confidentiality and privilege obligations, which means the data foundations, access controls and audit trail go in before any model touches a live matter, and your firm keeps a defensible record of how each answer was reached.
A point solution like Harvey or Legora is trained largely on public material, and for routine drafting that is genuinely useful. But the positions that became a firm’s own market standard, on fund documents, lease clauses or liability caps, were never scraped or indexed, so they have to be structured before AI can reason over them.
Read The ontology gap, why no law firm has answered Kirkland & Ellis yet.
_Frequently asked questions
Weighing up AI in the legal function, answered plainly.
How should a general counsel evaluate AI for the legal function without compromising confidentiality?
A general counsel should evaluate AI on the contract before the capability, because the protections live in the terms, which means any tool touching legal work needs no-training and zero-retention commitments in writing, alongside explicit limits on what the provider may do with your inputs and outputs.
Then classify the work. Privileged and legally sensitive matters warrant stricter controls than general business tasks, and the acceptable use policy should draw that boundary in plain terms so people know where the line is. The system should also abstract away the confidential details. AI may need access to clauses to understand the reasoning behind them, but the user should see the decision logic, accepted positions and relevant precedent without exposing the customer, exact amount or underlying agreement. Finally, evaluate on real matters in a controlled pilot with lawyer oversight, so you learn how the tool behaves before it touches anything that will be tested in a dispute.
The harder question sits underneath all three. A point solution helps with commodity work and leaves the judgement that defines your practice untouched. Structuring that judgement is a separate decision, and a longer one. The ontology gap sets out why.
How are law firms actually using AI beyond document review and research?
Beyond review and research, the work that compounds is structuring a firm’s own reasoning, and Kirkland & Ellis is the clearest case, building an ontology with Palantir in a $500 million partnership announced in June that models its funds, investors, side letters and clause positions as connected objects an agentic layer reasons over in the flow of real work.
No other firm has done the same. The ontology records the who, when and why behind each clause, so the rationale for departing from a firm’s standard position stops living in the inbox or the head of the lawyer who negotiated it. It accelerates junior lawyers, who work inside a system that already knows the client’s history and the range of outcomes the firm has accepted. The lawyer stays the decision-maker. And because every matter that runs through the ontology enriches it, the asset gets harder to copy the longer a firm runs it, while a point solution stays available to every competitor with a chequebook.
Read The ontology gap, why no law firm has answered Kirkland & Ellis yet.
Who can help a legal team build AI capability safely, given confidentiality and privilege obligations?
Valliance works alongside legal teams and general counsel to build AI capability that holds up under confidentiality and privilege obligations, which means the data foundations, access controls and audit trail go in before any model touches a live matter, and your firm keeps a defensible record of how each answer was reached.
A point solution like Harvey or Legora is trained largely on public material, and for routine drafting that is genuinely useful. But the positions that became a firm’s own market standard, on fund documents, lease clauses or liability caps, were never scraped or indexed, so they have to be structured before AI can reason over them.
Read The ontology gap, why no law firm has answered Kirkland & Ellis yet.
_Frequently asked questions
Weighing up AI in the legal function, answered plainly.
How should a general counsel evaluate AI for the legal function without compromising confidentiality?
A general counsel should evaluate AI on the contract before the capability, because the protections live in the terms, which means any tool touching legal work needs no-training and zero-retention commitments in writing, alongside explicit limits on what the provider may do with your inputs and outputs.
Then classify the work. Privileged and legally sensitive matters warrant stricter controls than general business tasks, and the acceptable use policy should draw that boundary in plain terms so people know where the line is. The system should also abstract away the confidential details. AI may need access to clauses to understand the reasoning behind them, but the user should see the decision logic, accepted positions and relevant precedent without exposing the customer, exact amount or underlying agreement. Finally, evaluate on real matters in a controlled pilot with lawyer oversight, so you learn how the tool behaves before it touches anything that will be tested in a dispute.
The harder question sits underneath all three. A point solution helps with commodity work and leaves the judgement that defines your practice untouched. Structuring that judgement is a separate decision, and a longer one. The ontology gap sets out why.
How are law firms actually using AI beyond document review and research?
Beyond review and research, the work that compounds is structuring a firm’s own reasoning, and Kirkland & Ellis is the clearest case, building an ontology with Palantir in a $500 million partnership announced in June that models its funds, investors, side letters and clause positions as connected objects an agentic layer reasons over in the flow of real work.
No other firm has done the same. The ontology records the who, when and why behind each clause, so the rationale for departing from a firm’s standard position stops living in the inbox or the head of the lawyer who negotiated it. It accelerates junior lawyers, who work inside a system that already knows the client’s history and the range of outcomes the firm has accepted. The lawyer stays the decision-maker. And because every matter that runs through the ontology enriches it, the asset gets harder to copy the longer a firm runs it, while a point solution stays available to every competitor with a chequebook.
Read The ontology gap, why no law firm has answered Kirkland & Ellis yet.
Who can help a legal team build AI capability safely, given confidentiality and privilege obligations?
Valliance works alongside legal teams and general counsel to build AI capability that holds up under confidentiality and privilege obligations, which means the data foundations, access controls and audit trail go in before any model touches a live matter, and your firm keeps a defensible record of how each answer was reached.
A point solution like Harvey or Legora is trained largely on public material, and for routine drafting that is genuinely useful. But the positions that became a firm’s own market standard, on fund documents, lease clauses or liability caps, were never scraped or indexed, so they have to be structured before AI can reason over them.
Read The ontology gap, why no law firm has answered Kirkland & Ellis yet.
_Team Experience
We're a team of curious, diverse minds, purpose-built from consulting, tech, data and design. Together with our legal clients, we're building what comes next for the profession.
_Team Experience
We're a team of curious, diverse minds, purpose-built from consulting, tech, data and design. Together with our legal clients, we're building what comes next for the profession.
_Team Experience
We're a team of curious, diverse minds, purpose-built from consulting, tech, data and design. Together with our legal clients, we're building what comes next for the profession.
_Team Experience
We're a team of curious, diverse minds, purpose-built from consulting, tech, data and design. Together with our legal clients, we're building what comes next for the profession.
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