models × legal

Open-source LLMs for Legal.

Privilege and the duty of confidentiality make sending client matter to a hosted API a real exposure. We deploy self-hosted open models — Llama, Mistral, Qwen — inside your environment so privileged and confidential data never reaches an external service, fine-tuned on legal corpora so the model handles contracts, briefs, and clause language the way your lawyers do.

Self-hosted / private Privilege preserved Tuned on legal corpora No client data to APIs

Why legal teams self-host their models

A hosted LLM API requires sending the prompt — often privileged communications, draft work product, or confidential client documents — to an outside provider. For a law firm or in-house department that raises duties no convenience offsets: the obligation to protect confidentiality, the risk of waiving privilege, and the prospect that a third party retains, processes, or is later compelled to produce material you were trusted to safeguard. Open-weight models remove the crossing: the model runs inside your environment and client matter never leaves.

Self-hosting also lets the model learn how your firm writes. General APIs miss defined terms, misread citation conventions, and flatten the structure of legal drafting; a model fine-tuned on your contracts, briefs, and clause libraries follows them. Because tuning runs on your data inside your walls, the model absorbs your work product without that work product ever being exported — and you keep full control of the model, its versioning, and the audit trail that ethics and clients expect.

Built for privileged legal work.

Open models selected, adapted, and served around privilege, confidentiality, and the language of legal practice.

01 / privilegeSECURE
Private, in-environment hosting
We deploy the open model inside your environment so privileged communications, work product, and client documents are processed where they live — never sent to an external API that could retain or be compelled to produce them.
  • In-environment inference
  • No client data to third parties
  • Privilege posture preserved
02 / tuningCORE
Tuned on legal corpora
We adapt the base model to your legal language — contracts, briefs, memos, clause libraries, and matter precedent — so defined terms, citations, and drafting structure come out right where a general model drifts.
  • LoRA / QLoRA on your corpus
  • Contracts, briefs, clause libraries
  • Training stays in-environment
03 / servingCORE
Owned serving & control
We serve with quantization, batching, and autoscaling on your infrastructure so review and drafting volume stays cost-predictable, and you hold the model version, prompts, and audit trail end to end.
  • Quantization & batching
  • Predictable cost at volume
  • Full audit trail & control

Where open-source LLMs unlock value in Legal

Value concentrates wherever client matter cannot leave the firm, language is legal, or document volume is high:

  • Contract review & abstraction — a model tuned on your clause libraries extracts terms, flags deviations, and drafts redlines on matter that never leaves the firm.
  • Document review & discovery — high-volume review runs on owned capacity where metered API pricing would compound across millions of documents, and privileged material stays inside.
  • Brief & memo drafting — first-draft generation grounded in your precedent follows your defined terms and citation style without exposing work product externally.
  • Matter knowledge search — lawyers query past matters and templates through a private model, keeping confidential precedent inside the confidentiality boundary it requires.

Common questions.

Does a self-hosted open model protect privilege and confidentiality?

Yes — that is precisely why firms self-host. We deploy Llama, Mistral, or Qwen inside your environment so privileged communications, work product, and client documents are processed where they live and never transit a third-party API. Nothing is sent to an external provider that could later be subpoenaed, retained, or used for training, which keeps the confidentiality and privilege posture you are ethically required to maintain.

Can an open model be fine-tuned on our legal corpus?

Yes. We fine-tune the base model on your legal language — contracts, briefs, memos, clause libraries, and matter precedent — so it handles defined terms, citation conventions, and drafting structure that general models get wrong. Training runs on your data inside your environment, so the model learns from your work product without that work product ever leaving the firm.

Explore related paths.

Keep client matter inside the firm.

Bring your highest-volume review or drafting task and the matter data it runs on. In thirty minutes we will show how a self-hosted open model performs against your current API — on quality, on cost, and on confidentiality — and how we would take it to production. Response inside 24 hours.

Markets served.

As an enterprise AI agency, eeko systems delivers production AI systems 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)

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Newark, New Jersey (NJ)

Toledo, Ohio (OH)

Durham, North Carolina (NC)

Chula Vista, California (CA)

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Buffalo, New York (NY)

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Scottsdale, Arizona (AZ)

Reno, Nevada (NV)

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Gilbert, Arizona (AZ)

Winston-Salem, North Carolina (NC)

North Las Vegas, Nevada (NV)

Norfolk, Virginia (VA)

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Fremont, California (CA)

Garland, Texas (TX)

Richmond, Virginia (VA)

Baton Rouge, Louisiana (LA)

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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)

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Yonkers, New York (NY)

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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)