Industry Legal

AI for legal — where every answer traces to the document, and privilege still holds.

A partner can't cite an answer she can't trace back to the source, and a privileged document can't leak because a model was careless with context. I build AI for that reality: source-cited answers over contracts and matters, for AmLaw firms, in-house counsel, and legal tech — confidentiality and privilege preserved, every answer traceable.

Source-cited Every answer traces to the clause
Privilege-aware Confidentiality preserved by design
Audit-ready Every action logged & provable

Legal work runs on a contradiction most AI vendors gloss over: the value of an associate's or an AI system's output is leverage — more documents reviewed, more precedent surfaced, faster — but the profession's entire liability model depends on confidentiality and privilege holding without exception. A tool that speeds up review but can't account for what it saw, stored, or logged isn't a shortcut. It's an exposure.

I build AI that holds both ends at once. Grounded retrieval so every answer comes from the actual document or matter file, not a model's approximation of one. Access controls and audit trails so you can show, precisely, what the system touched and what it didn't. Leverage where the work demands it, confidentiality everywhere else.

Where AI moves the number

The highest-leverage work in a firm or legal department is document-heavy, precedent-bound, and billed or budgeted by the hour — exactly where a well-built, source-cited system earns its keep.

  • Contract & document intelligence — contract review, clause extraction, redlining support, and due diligence synthesis, with every finding tied to the exact page and clause.
  • Research synthesis — case law and statute research, brief drafting support, and precedent summarization, source-cited so nothing goes in front of a judge or client unverified.
  • Matter & knowledge retrieval — matter history retrieval, internal know-how search, and prior-work reuse that turns years of filed work product into something the team can actually find.
In legal, an answer you can't trace back to the document is unusable — not because it's wrong, but because you can't prove it isn't. I build systems that show their work, every time.

Four places AI pays for itself on a legal matter.

Each one document-heavy, precedent-bound, and tracked to a number.
01 / Documents
Contract & document intelligence
Contract review, clause extraction, redlining support, and due diligence synthesis — every finding tied to the exact page and clause it came from.
02 / Research
Research synthesis
Case law and statute research, brief drafting support, and precedent summarization — source-cited so nothing reaches a filing or a client unverified.
03 / Matters
Matter & knowledge retrieval
Matter history retrieval, internal know-how search, and prior-work reuse that turns years of filed work product into something the team can find in seconds.
04 / Control
Confidentiality & audit
Access controls, privilege-aware retrieval boundaries, append-only audit logs, and on-prem inference for matters that can't leave your environment.
Source citationsPrivilege-awareConfidentialityOn-prem optionAudit trailsGrounded retrieval

From briefing to production, with privilege built in.

Fixed scope, fixed price, confidentiality from day one.
01Brief

Find the highest-leverage use case

Contract review, research synthesis, or matter retrieval — we pick the one that saves the most billable or budgeted hours and agree on the KPI up front.

02Architect

Design for citation and confidentiality together

Grounded retrieval, access boundaries, and the audit layer are designed as one system — fixed scope and price, no surprises.

03Build

Deploy into your environment

We build in sprints, deploy inside your controls — on-prem where confidentiality demands it — and harden until every answer holds up under scrutiny.

04Measure

Prove the value and the trail

We track the system against its KPI and confirm every answer is source-cited and every access logged — then tune until the value crosses the cost.

How I build it.

The services behind the legal work.
Service / 01
Enterprise AI agents
Agentic workflows designed around your actual processes, not a demo — built, deployed, and tuned until they hold up in production.
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Service / 05
Knowledge systems
Your documents, wikis, tickets, and contracts turned into one queryable, source-cited system that finally answers back — with the receipts.
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Service / 02
RAG systems
Retrieval pipelines that put your institutional knowledge to work. Grounded, source-cited answers on your own data — without leaking it.
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Service / 04
Compliance & governance
SOC 2 controls, HIPAA compliance, and deployments built for regulated industries — from day one, not bolted on later.
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Service / 03
AI infrastructure
Local model deployment and hardware builds for organizations that need their data to stay inside their walls. Nothing leaves the perimeter.
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Service / 06
Decision intelligence
Analytics, dashboards, and predictive models that drive real decisions — built by someone who spent 15 years attaching dollars to outcomes.
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Let's talk about where AI moves a number on your matters.

Contract review, research synthesis, matter retrieval — if it's document-heavy and it has to be traceable, that's my kind of problem. I'll tell you what it's worth before we build it.

Let's talk

Markets served.

Remote-first across the United States and internationally — including these markets.

New York City, New York (NY)

Los Angeles, California (CA)

Chicago, Illinois (IL)

Houston, Texas (TX)

Phoenix, Arizona (AZ)

Philadelphia, Pennsylvania (PA)

San Antonio, Texas (TX)

San Diego, California (CA)

Dallas, Texas (TX)

San Jose, California (CA)

Austin, Texas (TX)

Jacksonville, Florida (FL)

Fort Worth, Texas (TX)

Columbus, Ohio (OH)

Charlotte, North Carolina (NC)

Indianapolis, Indiana (IN)

San Francisco, California (CA)

Seattle, Washington (WA)

Denver, Colorado (CO)

Washington, District of Columbia (DC)

Boston, Massachusetts (MA)

El Paso, Texas (TX)

Nashville, Tennessee (TN)

Detroit, Michigan (MI)

Oklahoma City, Oklahoma (OK)

Portland, Oregon (OR)

Las Vegas, Nevada (NV)

Memphis, Tennessee (TN)

Louisville, Kentucky (KY)

Baltimore, Maryland (MD)

Milwaukee, Wisconsin (WI)

Albuquerque, New Mexico (NM)

Tucson, Arizona (AZ)

Fresno, California (CA)

Sacramento, California (CA)

Kansas City, Missouri (MO)

Atlanta, Georgia (GA)

Miami, Florida (FL)

Colorado Springs, Colorado (CO)

Raleigh, North Carolina (NC)

Omaha, Nebraska (NE)

Long Beach, California (CA)

Virginia Beach, Virginia (VA)

Oakland, California (CA)

Minneapolis, Minnesota (MN)

Tulsa, Oklahoma (OK)

Arlington, Texas (TX)

New Orleans, Louisiana (LA)

Wichita, Kansas (KS)

Cleveland, Ohio (OH)

Tampa, Florida (FL)

Bakersfield, California (CA)

Aurora, Colorado (CO)

Honolulu, Hawaii (HI)

Anaheim, California (CA)

Santa Ana, California (CA)

Corpus Christi, Texas (TX)

Riverside, California (CA)

Lexington, Kentucky (KY)

St. Louis, Missouri (MO)

Stockton, California (CA)

Pittsburgh, Pennsylvania (PA)

Saint Paul, Minnesota (MN)

Cincinnati, Ohio (OH)

Greensboro, North Carolina (NC)

Anchorage, Alaska (AK)

Plano, Texas (TX)

Lincoln, Nebraska (NE)

Orlando, Florida (FL)

Irvine, California (CA)

Newark, New Jersey (NJ)

Toledo, Ohio (OH)

Durham, North Carolina (NC)

Chula Vista, California (CA)

Fort Wayne, Indiana (IN)

Jersey City, New Jersey (NJ)

St. Petersburg, Florida (FL)

Laredo, Texas (TX)

Madison, Wisconsin (WI)

Chandler, Arizona (AZ)

Buffalo, New York (NY)

Lubbock, Texas (TX)

Scottsdale, Arizona (AZ)

Reno, Nevada (NV)

Glendale, Arizona (AZ)

Gilbert, Arizona (AZ)

Winston-Salem, North Carolina (NC)

North Las Vegas, Nevada (NV)

Norfolk, Virginia (VA)

Chesapeake, Virginia (VA)

Fremont, California (CA)

Garland, Texas (TX)

Richmond, Virginia (VA)

Baton Rouge, Louisiana (LA)

Boise, Idaho (ID)

San Bernardino, California (CA)

Spokane, Washington (WA)

Des Moines, Iowa (IA)

Modesto, California (CA)

Birmingham, Alabama (AL)

Tacoma, Washington (WA)

Fontana, California (CA)

Oxnard, California (CA)

Fayetteville, North Carolina (NC)

Huntsville, Alabama (AL)

Moreno Valley, California (CA)

Rochester, New York (NY)

Glendale, California (CA)

Yonkers, New York (NY)

Augusta, Georgia (GA)

Amarillo, Texas (TX)

Little Rock, Arkansas (AR)

Akron, Ohio (OH)

Shreveport, Louisiana (LA)

Grand Rapids, Michigan (MI)

Mobile, Alabama (AL)

Salt Lake City, Utah (UT)

Huntsville, Texas (TX)

Tallahassee, Florida (FL)

Overland Park, Kansas (KS)

Knoxville, Tennessee (TN)

Worcester, Massachusetts (MA)

Brownsville, Texas (TX)

New Port Richey, Florida (FL)

Jackson, Mississippi (MS)

Providence, Rhode Island (RI)

Fort Lauderdale, Florida (FL)

Sioux Falls, South Dakota (SD)

Tempe, Arizona (AZ)

Cape Coral, Florida (FL)

Springfield, Missouri (MO)

Pembroke Pines, Florida (FL)

Eugene, Oregon (OR)

Peoria, Arizona (AZ)

Corona, California (CA)

Lancaster, California (CA)

Rockford, Illinois (IL)

Salinas, California (CA)

Palmdale, California (CA)

Springfield, Massachusetts (MA)

Charleston, South Carolina (SC)

Duluth, Minnesota (MN)

London, England (ENG)

Dublin, Ireland (IRE)