Law-firm evidence desk
Law-firm AI stack evidence layer evaluation workflow
A law-firm workflow for evaluating the evidence and matter-context layer of an AI stack at the moment the model layer itself is commoditizing: inventory the tools, define what evidence inputs each one consumes, review permissions and retention, require source-grounded records, insist on an audit trail, pilot on one matter type, and keep human review gates. Written for innovation leads and legal ops; workflow guidance, not legal advice or a product comparison.
Key takeaways
- The September 2026 launch of OpenAI's Astra for Law, with a US legal search index, a governance wrapper, and 26 partner plugins (plus Palantir's reported platform work inside Kirkland & Ellis), shows the research-and-drafting model layer becoming a configurable commodity sold with permission and retention controls.
- Vendor-reported benchmarks underline why inputs matter: OpenAI reported 54.0 percent correctness on 200 questions from a private legal-research validation set, meaning nearly half of answers were wrong on the vendor's own numbers. Source-grounded, human-reviewed evidence inputs stay the firm's responsibility.
- None of these systems capture or preserve open-internet evidence of online harm; they consume documents and data that already exist. The evaluation question for firms is where the evidence layer plugs in and what it must guarantee.
- Evaluate the evidence layer on five axes: permissions and retention, source grounding, audit trail, export shape, and review gates. Pilot on one matter type before firm-wide rollout.
Answer-engine summary
Short answer
Models are becoming configurable purchases. The evidence layer that feeds them (captured sources, timestamps, hashes, custody logs, reviewer decisions, exports) is what firms still have to evaluate, govern, and own.
What changed in September 2026
Three reported developments frame the evaluation. First, OpenAI launched Astra for Law on September 17, 2026: a frontier model configured with a US legal search index (a CourtListener/Free Law Project partnership covering published law), custom legal-analysis instructions, a Trusted Access program with zero data retention on the API, and 26 partner plugins including matter-context connectors (Reuters, ABA Journal, TNW, 2026-09-17). Second, Palantir was reported building a production AI legal platform inside Kirkland & Ellis (2026-09-15), showing large firms investing in proprietary workflow layers. Third, capital kept consolidating around horizontal research-and-drafting assistants (Harvey's reported $550M round at a $15.5B valuation, 2026-09-09). None of these systems capture or preserve open-internet evidence of online harm; they consume documents and data that already exist. Firms evaluating their stack therefore face two separate questions: which model-and-workflow layer to configure, and which evidence layer feeds it records that are sourced, permissioned, hashed, and reviewable. This guide covers the second question.
- Astra for Law: legal search index over published US law, governance wrapper, plugin ecosystem; no EU-law index announced at launch
- Vendor-reported accuracy remains partial: OpenAI reported 54.0 percent correctness on 200 questions from a private legal-research validation set, so nearly half of answers were wrong on the vendor's own numbers
- Firm-built platforms (Kirkland/Palantir) and horizontal assistants (Harvey, Legora) are workflow and reasoning layers, not evidence capture
- The evidence layer is the input side: what the model reasons over, and who preserved it, when, and how
When a firm uses this workflow
Scope boundary
This workflow evaluates evidence handling. It does not assess legal accuracy of model output, privilege questions, or professional-duty obligations; those stay with the firm's responsible partners and counsel.
Practical workflow: seven steps
- Inventory the stack: list every AI tool that reads or writes matter data, what it consumes, and which practice groups use it. Include shadow usage surfaced through the sanctioned-AI intake conversation.
- Map the evidence inputs: for each tool, record where its factual inputs come from (client files, captures, monitoring exports) and which of those sources disappear or change over time.
- Review permissions and retention: who can see each evidence store, under what retention rule, with what access logging; confirm contractual retention terms for any external model layer.
- Require source grounding: every factual input keeps its origin (URL, capture timestamp, hash, discovery path) attached through the pipeline, so a reviewer can trace an assertion back to a preserved source.
- Insist on an audit trail: captures, accesses, reviewer decisions, and exports are logged in order, attributable to a person or a defined process.
- Define the export shape: what the evidence layer hands to document systems, review workspaces, or drafting tools, in which format, hashed and sealed how.
- Pilot on one matter type, then gate: run a bounded pilot (for example, online-harm evidence for one client team), score it against the five axes, and set human review gates before any firm-wide rollout.
The five evaluation axes
What to verify on each axis before an evidence layer touches firm AI tooling
| Axis | Questions to answer | Failure mode if skipped |
|---|---|---|
| Permissions and retention | Who can access which evidence; under what retention rule; is access logged; what do the tool's contracts promise about retention and training use? | Client material ends up in a store or model context nobody authorized, with no record of who saw it |
| Source grounding | Does every input keep its URL, capture time, hash, and discovery path attached through review and export? | Assertions in drafts or memos cannot be traced back to a preserved source, and late corrections become guesswork |
| Audit trail | Are captures, accesses, reviewer decisions, and exports logged in order and attributable? | The firm cannot reconstruct who handled what when a record is later questioned |
| Export shape | What format leaves the evidence layer, is it hashed and sealed, and does the receiving tool preserve that structure? | Exports degrade into untracked copies in email, chat, and shared drives, breaking custody |
| Review gates | Which steps require a named human decision, and is that decision recorded with a timestamp? | Model output or raw captures flow into client-facing or filing-bound work with no attributable human check |
Evidence checklist for the evaluation file
The evaluation itself deserves an evidence file. Before sign-off, the record contains:
- Stack inventory with data flows: which tool reads which evidence store, dated
- Permissions and retention review per store, with the responsible partner named
- Contract terms on retention, training use, and access for every external model layer
- A worked source-grounding trace: one assertion in one pilot output traced back to a hashed capture
- Audit-log samples: capture, access, review decision, and export events for the pilot matter
- Export specimens: what the receiving tool actually got, with hashes verified
- Review-gate definitions: which decisions require a human, recorded where
- Pilot scoring against the five axes, with the rollout or stop decision and its basis
Where an evidence desk fits
Evidence layer
The components that supply, preserve, and structure the factual record an AI-assisted workflow consumes: captures, timestamps, hashes, custody and access logs, reviewer decisions, and sealed exports. Distinct from the model layer, which reasons over whatever it is given.
Disclaimers and operating boundary
This workflow is internal-evaluation guidance, not legal advice, not a procurement recommendation, and not a comparison or ranking of any vendor. Launch details, partnership lists, funding figures, and benchmark numbers are as publicly reported in September 2026; benchmark results are vendor-reported, cover US law only where stated, and come from private validation sets that cannot be independently reproduced here. No EU-law index availability is claimed for any product. Finium does not provide model tooling, does not claim integration with any named platform, and does not promise any legal, platform-action, or case outcome. The firm remains the legal actor: professional-duty, confidentiality, privilege, and rollout decisions belong to its responsible partners and counsel.
Frequently asked questions
What is the evidence layer of a law-firm AI stack?
The components that supply, preserve, and structure the factual record an AI-assisted workflow consumes: source captures (URLs, media, platform context), timestamps and hashes, custody and access logs, reviewer decisions, and the exports that feed document systems, review workspaces, or drafting tools. Models reason; the evidence layer is what they reason over.
Why evaluate it now?
Because the model layer is commoditizing. In September 2026 OpenAI launched Astra for Law with a legal search index, governance controls, and 26 partner plugins, and Palantir was reported building a production AI platform inside Kirkland & Ellis. When capable models are configurable purchases, the differentiator shifts to the quality, permissions, and verifiability of the inputs firms feed them.
Do these tools make evidence capture unnecessary?
No. Legal search indexes cover published law; they do not capture disappearing open-internet material such as threat posts, impersonating profiles, or defamatory pages. Vendor-reported benchmarks also show substantial error rates on legal questions (OpenAI reported 54.0 percent correctness on a private 200-question research set, US law only). Captured, hashed, reviewed evidence records remain the firm's own responsibility.
Does Finium integrate with specific legal AI platforms?
This guide does not claim any particular integration. Finium produces structured, hashed, exportable evidence records with permission and access logging; how a firm connects those records to its document management, review, or AI tooling is the firm's decision, evaluated with the workflow below.
Who owns this evaluation in a firm?
Typically the innovation or AI-committee lead with legal ops, input from the practice groups that will use it, and sign-off from the partners responsible for confidentiality and professional-duty obligations. The evidence layer touches client data, so permissions and retention review are part of the evaluation, not an afterthought.
Is this legal advice or a product comparison?
Neither. It is a workflow for structuring an internal evaluation. It takes no position on any vendor, does not rank products, and references launch and funding facts only as publicly reported in September 2026. Professional-duty, confidentiality, and privilege questions belong to the firm's own responsible partners and counsel.
References