expertise / contract law

Contract AI training data, read like a careful lawyer.

A contract model that labels the limitation of liability and misses the carve-out two sections later has not read the agreement. eeko commissions admitted transactional attorneys and in-house counsel to label clauses in context, redline against a playbook, grade model analysis, and check each other's work. Every item fingerprinted, second-reviewed, and licensed.

Attorney-made labels Second-attorney review Tagged by governing law Reply inside one business day
Hero image · 840 × 1050
attorneys on every item
2min.
the author plus a second reviewer
reply to a dataset request
1bd
a real person, with questions
delivery formats
6
JSON Lines, CSV, Parquet, WebDataset, COCO, Croissant
minimum commitment for attorneys
0hr
fits around a practice

What is contract AI training data?

Contract AI training data is the labeled legal material a contract model learns from and is tested against: a clause classified with its defined terms and cross-references resolved, a redline with the reason for every change, an agreement reviewed against a negotiation playbook with each deviation flagged, a model summary graded for the risk it missed. If that material is labeled sentence by sentence by people who have never negotiated a deal, the model learns to read contracts the way a search engine does.

The failures are predictable. A capitalized term read without its definition, US law assumed in an English-law agreement, a suggested edit that quietly moves liability to the wrong party. eeko's data practice commissions credential-checked transactional attorneys to label agreements as a whole, then has a second attorney confirm every item before it leaves.

This page belongs to eeko's human-made AI training data practice, built because the document systems we deliver for law firms and legal departments (see our legal AI work) need data nobody has published. See also training data for legal and how eeko verifies human data.

Labeling workflow image · 2100 × 900
Labeled by one attorney, confirmed by a second, delivered with fingerprint and licence attached.

Where contract models go wrong, and what fixes it.

Three failure patterns from contract review evaluations, and the labeling decision behind each fix.

01 / contextRISK
The clause lives somewhere else
What a clause means often depends on a definition twenty pages earlier or a carve-out buried in a schedule. Our attorneys label with the whole agreement open, and the label records what it relied on.
02 / jurisdictionRISK
One legal system assumed
Enforceability and drafting conventions change with governing law. Every item is tagged by jurisdiction, and matched cross-jurisdiction sets make the differences visible to the model.
03 / riskRISK
The one-word redline
An edit can read well and still shift the deal. A second attorney reviews every markup for changes that move liability, and the rationale on each change makes the intent checkable.

Attorney annotation and contract datasets, to your spec.

Six request types we fill for legal AI teams, each one starting with a labeling guide, then a pilot, at a fixed per-document price.

01 / clausesCORE
Clause extraction and classification
Every provision classified against your taxonomy: indemnity, liability caps, termination rights, assignment, governing law, and the rest, with defined terms resolved.
02 / redlinesCORE
Redlines with rationale
Attorney markups made from a given playbook or side of the table, each change explained in a sentence or two of plain language.
03 / playbooksCORE
Playbook review data
Agreements reviewed against a negotiation playbook, flagging each deviation, its risk level, and the fallback an attorney would accept.
  • Your playbook or one we draft
  • Deviation and fallback labels per clause
04 / jurisdictionsCORE
Cross-jurisdiction sets
Matched items under different governing laws, so a model learns where enforceability and drafting practice diverge instead of assuming one system.
  • US state, English, EU, and other systems
  • Jurisdiction tag on every item
05 / gradingRLHF
Graded model analysis
Attorneys grade a model's summaries, extractions, and proposed edits on correctness, completeness, and the risk it failed to see, with a written rationale and preference pairs.
06 / draftingCORE
Drafted agreements and clause libraries
New contracts and clause banks written for training, which sidesteps the confidentiality problem of real client documents entirely.

From labeling guide to signed delivery.

Pilot before volume, fixed price per document, and an attorney on both ends of every item.

STEP 01
Labeling guide
You bring the agreement types, governing law, taxonomy or playbook, and acceptance criteria. We draft the guide with you and quote a fixed price for a pilot batch.
STEP 02
Matched attorneys label
Jobs go to attorneys admitted and experienced in that combination. Each document gets its fingerprint on arrival and is compared with the rest of the set for duplicates.
STEP 03
Second-attorney review
A different attorney confirms or corrects every label and redline. Provenance records who made it, what a model suggested, and who signed it off.
STEP 04
Verified delivery
The batch arrives in your format with a signed receipt naming each item by ID and hash, alongside a hash-chained audit trail that legal and procurement can inspect.

Every agreement, provable.

Legal AI gets questioned by procurement, by clients, and sometimes by a court. The data behind it should hold up.

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One agreement, one record: source, author, reviewer, licence, and the hash proving it is unchanged.

What travels with each item

  • Fingerprint — a cryptographic hash taken on arrival and compared across the set for duplicates.
  • Signed provenance — who labeled it, whether a model proposed any part, and which attorney confirmed it.
  • Independent timestamp — proof of when the item existed, from a source we do not control.
  • Consent and licence — recorded per document, whether sourced from a consenting party or drafted new.
  • Privacy and origin checks — personal information screened and masked; generator signatures and C2PA or IPTC fields detected.
  • Audit trail and receipt — hash-chained history, and a signed receipt naming every item in the batch.

It is the same discipline behind our own compliance and governance work.

Paid remote AI work for transactional attorneys.

Work in the agreements you already draft, from anywhere, with the rate on the job before you take it. No clients, no advice, nothing published under your name.

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Redlining a SaaS agreement against a playbook, between client matters.

The work itself

  • Label clauses — identify and classify provisions with their defined terms and cross-references resolved.
  • Redline to a playbook — mark up an agreement from one side's position and give a short reason for every change.
  • Grade model analysis — score a model's summaries and proposed edits on correctness, overlooked risk, and jurisdictional slips.
  • Second-review a peer — confirm or correct another attorney's labels and redlines before delivery.

Jobs come to you when your verified jurisdiction and agreement types fit, and every one states its scope, deadline, and rate. Start at apply as an expert, or read how the program works on expert data annotation jobs.

who we verify
Attorneys the network accepts

Identity and credentials are confirmed once, before any job is offered. After that, a peer reviews your work, not a generalist QA team.

  • Degree JD, LLB, or equivalent
  • Admission Admitted in at least one jurisdiction
  • Practice Years of transactional or in-house contracts work
  • Agreement types SaaS, supply, licensing, employment, M&A, or similar
  • Paralegals Experienced contract managers and paralegals, for certain extraction jobs
how it pays
Rate first, then the job

You see the rate and the scope, then decide. Approved work is credited and paid out with an itemized record.

  • Transparent Rate on every job before you accept
  • Remote Your schedule, inside the deadline
  • Reviewed Second attorney approves, payment releases
  • Private Never named on delivered data

Contract AI training data questions.

What is contract AI training data?

Contract AI training data is the labeled legal material a contract model learns from and is tested against: clauses classified in the context of the whole agreement, redlines with the reason for each change, agreements reviewed against a negotiation playbook, and model summaries graded for missed risk. At eeko it is made by admitted attorneys, checked by a second attorney, and delivered with a fingerprint, a provenance record, and a licence on every item.

Where do the contracts come from?

Two places: parties who have consented to licensing their agreements, with that consent attached to each document, or attorneys drafting new agreements for the purpose. We never use scraped contracts or anything a client gave a lawyer in confidence. Each document is screened for personal data and masked as needed, gets its fingerprint on arrival, is compared against the set for duplicates, and carries signed provenance, so every item traces back to its source.

Will I have an attorney-client relationship with anyone?

No. You are producing and checking data for software. You advise nobody, represent nobody, and what you write becomes labeled training or evaluation material and nothing else. The agreements you mark up are training documents, not live deals. Every listing says what the data is for before you take it, and your name is never attached to delivered data.

Is bar admission required to join the network?

Most contract jobs require a law degree plus admission somewhere, and we confirm both together with your identity before any work is offered. Some extraction and classification jobs accept experienced contract managers and paralegals. Every listing spells out which credentials and which jurisdiction apply, and you only see work matching your verified profile.

How much does attorney annotation cost?

Pricing is per document or per item and depends on agreement length, the depth of labeling, the governing law, and how much second-attorney review the task needs. A clause classification pass costs far less than a full playbook review with redlines and rationale. We quote a fixed price for a pilot batch after reading your brief, so you can check the labeling guide and the standard before scaling. Exclusive licences are priced separately.

Which jurisdictions and agreement types can you cover?

We scope each request by governing law, agreement type, and which side you want represented, then route it to attorneys admitted and experienced in that combination. The most common requests cover SaaS, licensing, supply, and employment agreements, general commercial terms, and M&A documents, under US state, English, and other common-law and civil-law systems. If you need parallel items under several governing laws, we build them as a matched set so the differences are explicit.

Contract data that reads the whole agreement.

Send the agreement types, governing law, and the labels you need. A real person replies inside one business day with questions and a pilot price. Attorneys can apply in minutes. No deck. A ranked list.

Experienced within

  • SOC 2 Type II
  • ISO 27001
  • HIPAA
  • HITRUST r2
  • ISO 27701
  • PCI DSS
  • NIST CSF 2.0
  • CMMC
  • FedRAMP
  • GDPR

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)

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