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A lawyer receives an argument map with exact citations.

Every result opens the full judgment. When the material offers no basis, the system says so clearly. Interpretation and argument choice remain with the lawyer.

Case-law researchDemonstration systemReview one legal research question

Problem, solution, and result

  1. The problem

    Case-law research requires opening many records and comparing their reasoning. Much of the time goes into finding passages that may matter to the case.

  2. How the system works

    The system searches an approved set and returns propositions linked to exact source passages. Each citation opens the full judgment. If the material offers no support, the result states that clearly.

  3. What we tested

    The demonstration returned citations that matched the stored text and refused questions outside its scope. It does not assess the legal relevance of an argument or performance on the firm’s full corpus. A lawyer must confirm both during a pilot.

For whom

This is a good automation candidate when lawyers repeatedly search the same corpus and spend more time locating passages than evaluating them.

Question → passages → propositions → sources

  1. 01Every proposition requires an exact citation
  2. 02The citation opens the full SAOS record
  3. 03The lawyer interprets and chooses the argument
Business type
Law firms and in-house legal teams
Input
A lawyer’s research question and 40 SAOS ruling excerpts with source links and retrieval times
Boundary
A proposition without an exact citation span fails validation; a request for advice ends in a refusal
Cost
PLN 0 in external API fees for the recorded local run covering 10 questions.
Security
An answer without exact support in an approved source is held back. The system refuses requests for legal advice.
Speed
We measure retrieval time in a pilot using the firm’s corpus and questions.
Trace
Each proposition retains its source, exact passage, and any refusal decision.
Similar system build
from €6,000 net (approx. $6,500) · 4–10 weeks

Where automation stops

The lawyer interprets the result

The system stops at propositions and citations. The lawyer checks the full reasoning, the current state of the law, and whether an argument fits the matter. A request for legal advice ends in a refusal. The public version replays 10 saved questions and accepts no visitor text.

Boundary
A proposition without an exact citation span fails validation; a request for advice ends in a refusal
Cost
PLN 0 in external API fees for the recorded local run covering 10 questions.
Security
An answer without exact support in an approved source is held back. The system refuses requests for legal advice.
Speed
We measure retrieval time in a pilot using the firm’s corpus and questions.

Why we show the lower score

The first setup scored 0.950 because it promoted the topic used to create the labels. Reading all 40 excerpts exposed the issue, and the honest hybrid score was 0.735. We removed that rule. The next run produced 0.870 for hybrid search and 0.894 for BM25. Three of 10 questions had no responsive excerpt, and the system reported that. Legal validation requires a larger lawyer-labeled set.

Estimated impact

Calculate the impact at your volume

This estimate uses the stated volume. Enter your own numbers to assess the possible impact in your company. The result is confirmed only during a pilot.

  1. Today

    280 h

  2. With the system

    90 h

  3. Time or cost saved

    190 h/mo

Volume
600 questions/mo
Formula
(600 × (28 - 9)) / 60 = 190 h/mo
Calculation status
modelled scenario

Data in the screenshots. The names, amounts, and documents shown in the screenshots are synthetic. Client data remains private. Measurements describe the demonstrator and are kept separate from production outcomes.

Working surfaces

The lawyer evaluates the citation, the current law, and the argument to use in the matter.

The lawyer starts with propositions, citations, and gaps in the material. Competing views stay side by side. Full reasoning still needs legal review. Quality on firm data is measured on a larger question set labeled by lawyers.

Three views for case-law research

Question list

Saved questions and whether the corpus contains relevant material.

Argument map

Propositions, competing views, and exact quotations side by side.

Source record

Full judgment context, official URL, and provenance data.

System screens

See how the system works in practice

These desktop and mobile screenshots come from the running application. They show the process and the points where a person makes the decision.

Screens
16
px
1440 · 390
011440×1100
A question with propositions and exact citations.
390390×844
  1. 021440×1702
    A citation expanded to its context and full source record.
  2. 031440×1100
    A clear result when the limited corpus contains no support.
Open the remaining screen archive (13)
  1. 041440×1100
    A split in the corpus
  2. 051440×1177
    Both views and the lawyer's gate
  3. 061440×1100
    Research questions
  4. 071440×1100
    A question with no responsive excerpt
  5. 081440×1100
    Cited spans
  6. 091440×1100
    Rulings in the corpus
  7. 101440×1100
    A selected excerpt and its SAOS record
  8. 111440×1100
    Questions sent back to a lawyer
  9. 121440×1100
    Saved queries
  10. 131440×1100
    Research history
  11. 141440×1100
    Measurement method and ranking correction
  12. 151440×1100
    Retrieval rules
  13. 161440×1100
    Read labels and the review boundary

Technology stack

Retrieval runs locally, and a citation is required

BM25, multilingual-e5-small, and a reranker retrieve passages without an external API. CitedProposition requires at least one CitationSpan.

Next.js
a read-only public view with the analysis and cited passages
FastAPI
retrieval and the response contract with its required CitationSpan
BM25 + multilingual-e5-small + cross-encoder
hybrid ranking of excerpts, fully offline
PostgreSQL
documents, indexes, and the run trace
Docker Compose
API, database, and retrieval on the internal network; only the replay faces outward

The client receives the code, index, test data, and documentation. Production use requires an approved source corpus and legal evaluation.

Technical details and measurements

The working loop

From question to a passage that can be checked

The system searches, ranks, and validates citation spans. A proposition without a matching source passage cannot appear in the result.

01

Research question

02

Search in an approved corpus

03

Propositions with exact citations

No support → clear statement

04

Lawyer checks the full judgment

System architecture

Frozen corpus, local retrieval, and citation validation

PostgreSQL stores public records. Local models retrieve passages, and the data contract requires an exact citation span for every proposition.

  1. 01

    Corpus

    A frozen set of rulings with proof of origin.

    Every excerpt enters the corpus with its URL, retrieval time, and SHA-256 content hash, so every answer can be traced back to its source.

  2. 02

    Retrieval

    BM25, multilingual-e5-small, and a cross-encoder, all local.

    Search combines lexical and semantic matching with reranking, because a legal question can be a paraphrase and a case signature is a string of characters. With topic promotion removed, hybrid scores 0.870 and BM25 0.894 against read labels. Queries never leave the environment.

  3. 03

    Citation

    CitedProposition requires a non-empty CitationSpan.

    A proposition without an exact span in the source text fails schema validation and never reaches the interface. The no-invented-sources guarantee lives in the data type, so it does not depend on prompt discipline.

  4. 04

    Refusal and audit

    Research has an explicit end.

    A request for advice, document drafting, missing material, or sensitive data ends in a refusal. The public screen is read-only. The API, database, and search stay on the internal network.

One process rather than a conversation

The system holds no conversation and improvises nothing. It performs one process: find the excerpts, build propositions with spans, show the sources, and refuse where research ends. The simpler design is deliberate: there is no agent loop and no generative summarising here, because the citation guarantee comes from a simpler mechanism, a data contract checked before anything renders.

  • A proposition is formed only from a passage present in the verified corpus
  • Documents and queries stay in the local environment
  • Requests for legal advice end in an explicit refusal
  • The current run's metrics and limitations are visible in the interface

Want to assess a similar process in your company?

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  • A written summary of what to automate first and the likely cost range.
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