Architecture & Transparency

A direct description of how CommonBench is built. Not a marketing page. If something here is unclear or you want a deeper view of a specific component, email cases@commonbench.ai.

What the system is

CommonBench is a server-rendered chat application that runs your legal question through a structured prompt pipeline against a frontier large language model, augmented with a curated database of authorities across five common law jurisdictions. The output is post-processed for citation verification, structural quality, and disclosure compliance before it reaches you.

The corpus

• ~2,000 hand-curated and scraper-augmented case authorities across UK, US, HK, SG, AU.
• Each row carries: id, name, citation, year, court, jurisdiction, topic, principle, optional ratio, summary_one_line, key_paragraphs, doctrinal_status, leading_case, cited_by_count, last_verified.
• SQLite-backed with FTS5 full-text search; doctrinal columns populated by a separate tagging pipeline that uses the model to enrich each row with structured metadata.
• The "Corpus as of" line beneath every chat response is the maximum last_verified across the table — a real signal of when the corpus was last refreshed.

The retrieval

• Each user message is classified into a complexity level (L1 / L2 / L3) by a server-side heuristic that looks at length, multi-issue indicators, and document attachment.
• L1 queries get a focused doctrinal lookup; L2 / L3 queries trigger a richer retrieval pass that pulls authorities from the FTS index and re-ranks for jurisdiction match and doctrinal weight.
• The selected authorities are inlined into the model's system prompt as structured context. The model is instructed never to invent citations.

The model

• Anthropic Claude is the underlying frontier model (model id is visible in the per-response "Source" panel inside the chat).
• Prompts are tier- and jurisdiction-specific; the static portion is cached at the provider layer to reduce cost on repeat queries.
• L3 queries get extended thinking budget so the model can reason through multi-issue analysis before composing the response.

Citation verification

• After the model finishes streaming, every recognised case citation in the response is matched against our database and against external sources (BAILII, CourtListener, AustLII, HKLII, vLex where applicable).
• Citations that match cleanly get a green tick. Citations that do not match are flagged as unverified. Citations that pattern-match a fabricated form are stripped before the response is committed to the page.
• The verification result is streamed back as a separate frame and the badges are applied in-place.

What we store

• Saved chats: stored in your browser's localStorage, not on our servers. Closing your browser cache erases them.
• Account and subscription state (email, Stripe IDs, usage counters): stored server-side in encrypted-at-rest data files.
• Anonymised telemetry: latency, error rates, feedback submissions, error reports. No prompt content in telemetry.
• Document uploads for review (Advocate / Chambers): processed in memory, not persisted after extraction.

What we don't do

• We don't train a model on your conversations.
• We don't sell or share your queries.
• We don't claim to provide legal advice, and we don't pretend that machine output replaces a qualified practitioner.
• We don't quietly disable the disclosure that the output is machine-generated.

Open questions

The corpus is a partial coverage of each jurisdiction. The retrieval pass is good but not perfect. The model is fast but can still generate analysis that misses a recent first-instance decision. The error-report button on every response feeds a moderation queue that we read. Tell us when we're wrong.

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