Legal AI training data. Every citation checked by a lawyer.
Contract review, drafting, research, and litigation models fail in ways only a practitioner notices: a case that does not exist, a carve-out that got skipped, an answer that holds in Delaware and fails in Texas. eeko commissions practicing attorneys to write, label, and grade legal AI training data, then delivers every item with personal details masked and a signed provenance record attached.
What is legal AI training data?
Legal AI training data is the material a model learns from and is measured against when its job is to read, draft, or reason about the law: contracts labeled clause by clause, memos and redlines written by lawyers, step-by-step analyses under a stated jurisdiction, and model answers graded against a rubric. It differs from a scraped corpus of court opinions in one decisive way. Someone with a bar card made it, checked it, and signed off on where it came from.
The need is practical. Legal products summarize agreements, flag risky terms, draft correspondence, answer research questions, and prepare matters for trial. Each task has a signature failure, and public data does not teach a model to avoid it. A convincing but fictional citation, an indemnity exception nobody surfaced, advice that quietly assumes the wrong state: these are errors of judgment, and judgment is what the training set has to carry.
eeko builds production AI for law firms and legal departments through eeko's legal AI systems work, and the data practice exists because those systems kept needing material nobody had published. We now commission attorneys, paralegals, and legal translators to make it to a written specification, run every item through the same verification pipeline as the rest of the eeko training data practice, and deliver it under a licence your general counsel can read in one sitting.
Where legal models go wrong, and what the data has to teach.
Four failure modes show up in nearly every legal LLM evaluation. Each is fixable with the right examples.
Six kinds of legal training data, built to your specification.
Every engagement opens with a written spec and acceptance criteria. These are the most frequent requests.
- Your taxonomy, piloted before volume
- Rationales on request
- Disputed labels go to a second attorney
- Realistic fictional fact patterns
- Matched prompt and completion pairs
- House style guides honored
- Jurisdiction on every item
- Authorities cited and checked
- Alternative outcomes flagged
- Pairwise or absolute rubrics
- Written rationale per grade
- Usable for RLHF and for evals
- Coding to your protocol
- Reviewer agreement measured
- Privilege-style handling
- Never released, never reused
- Exclusive licence
- Refreshed as your product changes
Who makes lawyer-labeled datasets at eeko.
We staff from the eeko expert network by practice area, not from a general labeling pool.
Experts, data types, and the record behind each label
- Contract attorneys — clause annotation, redlines, and deviation labels across commercial agreement types.
- Litigators — reasoning traces, issue coding, and the grading of research answers where procedure matters.
- Paralegals — document structuring, citation checks, and first-pass coding under attorney supervision.
- Compliance specialists — regulatory question sets and policy-document annotation.
- Legal translators — multilingual contracts and correspondence, with a second linguist checking terminology.
Data types we deliver: contracts, legal memos, correspondence, clause annotations, graded answers, and reasoning traces. Every label records who produced it and who reviewed it, so your team can weight it when training.
From practice area to delivered dataset.
Fixed scope and fixed price once the spec is written. A pilot batch before any volume commitment, every time.
Legal AI training data questions.
What is legal AI training data?
Legal AI training data is the labeled, written, or graded material used to train and evaluate models that read contracts, draft documents, answer research questions, or support litigation. In practice it includes clause-level contract annotations, attorney-written drafting examples, step-by-step reasoning under a named jurisdiction, and model answers scored against a rubric. What separates usable legal data from a scraped corpus is who made it and what record travels with it: practicing attorneys, personal data removed, and a licence and provenance record on every item.
Who creates and reviews the legal data at eeko?
Credential-checked attorneys, paralegals, compliance specialists, and legal translators from the eeko expert network, chosen for the practice area your specification names. Contract annotation is done by contract lawyers, litigation coding by people with review experience, and graded research answers by lawyers who practice in the stated jurisdiction. Every label records its source, disputed items go to a second attorney, and your own legal team scores the pilot batch before we move to volume.
Is attorney-written training data legal advice?
No. The material our attorneys produce is training and evaluation content for your models. It is not advice to you, your users, or anyone else, and drafting demonstrations are built from fictional matters rather than live client work. Whether a dataset and its licence suit the product you are building is a question for your own counsel. We supply the specification, provenance records, and licence terms they will need to answer it.
How do you keep confidential client information out of a legal dataset?
Two ways. Where a dataset is drafted from invented fact patterns, no client information ever enters it. Where real documents are used, every document and table is screened for personal data such as names, addresses, and account identifiers, and that data is masked before delivery. Personal and location metadata is stripped from every file. Project-specific confidentiality terms are settled while we scope, and the signed receipt lists each delivered item so your records are complete.
Can you cover more than one jurisdiction or language?
Yes, within limits we state up front. Jurisdictions are named in the spec because the same question can have a different answer across state or national lines, and experts write and grade in the jurisdiction they practice in. Where we have few qualified experts for a jurisdiction, we tell you before you commit rather than after. Legal translators handle multilingual contracts and correspondence, with a second linguist checking terminology against your glossary.
How much does legal AI training data cost?
Price depends on the practice area, the seniority required, the time each item takes, how deep the review goes, and the licence you want. A full redline with a written rationale costs more than a single clause tag, exclusive licences and second-attorney review add to the unit price, and volume or standing orders bring it down. We quote once the specification and acceptance criteria are agreed, and the quote is fixed scope and fixed price.
What formats does a legal dataset arrive in?
Annotations, graded answers, and reasoning traces ship in JSON Lines or Parquet, tabular labels come as CSV, and source documents stay in their original formats where your pipeline needs them. Croissant metadata describes the set for your data catalog. Each delivery also includes the per-item provenance records and a signed receipt naming each item by identifier and hash, and large or recurring deliveries can run on a standing order.
Solutions legal AI teams pair with this.
Related experts and industries.
Tell us what your legal model keeps getting wrong.
Send the practice area, the task, and the failure you have seen. A data lead answers inside one business day with questions, a sourcing plan, and a pilot proposal. No deck. A ranked list.
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