Continual learning for legal AI.

01 — The gap

Legal work spans tools. Its lessons stay scattered.

A legal team may use one AI tool to review a contract and another to draft a response. The decisions and corrections from that work can end up scattered across tools and documents.

That makes it harder to reuse what lawyers have learned, compare models on the firm’s own tasks and spot drops in quality after an update.

Public benchmarks can show general performance. Firms also need evidence from their own matters, documents and standards to decide which tools fit their work, budget and confidentiality requirements.

02 — The mechanism

A verified correction becomes a private test.

Caly fits into the firm’s existing legal and AI workflows and keeps the evidence each review already produces.

When a lawyer verifies a correction, Caly brings together the task, the source documents, the AI’s draft and the lawyer’s fix in a reusable evaluation case that stays private to the firm.

What a test contains

1. 01The task. What the lawyer asked the AI tool to do.
2. 02The sources. The documents the answer should rest on.
3. 03The draft. What the model actually produced.
4. 04The correction. What the lawyer changed and verified as right.
5. 05The test. A reusable case in the firm’s private evaluation set.

Illustrative example. An AI contract review misses an exception to a liability cap. A lawyer corrects and approves the answer. Caly turns the correction into a test the firm can run on other models or after an update. The lawyer’s reasoning can also inform the firm’s playbook.

How the firm improves its AI over time

1. 01Capture workflows. Record how legal tasks are done so the firm can reuse the steps and decisions behind the work.
2. 02Verify corrections. A lawyer reviews the task, source documents and AI output, then approves the correction for reuse.
3. 03Build evaluations and training data. Approved corrections become test cases. Selected examples of legal expertise form curated training data.
4. 04Compare and retest. Run the same evaluations on different models and after updates to see where quality improves or drops.
5. 05Improve AI over time. Use what the evaluations reveal to refine instructions, document retrieval and playbooks. Use curated examples to adapt models where useful.

03 — The judgment

Lawyers stay responsible. The errors stop repeating unseen.

Caly does not review legal work or replace professional judgment. Lawyers verify the work. Caly keeps a record of what their review finds.

With a private set of cases grounded in real matters, the firm can test the models it relies on. The tests surface recurring errors and catch regressions when a provider ships an update that breaks an answer that used to pass.

Lawyers contribute through the review they already do. Caly uses that evidence to build datasets and tests, so the firm can improve its AI without running a machine learning team.

04 — The horizon

Knowledge the firm owns, across providers.

The workflows and verified corrections your firm accumulates in Caly build a record of its expertise. The firm owns that knowledge, its evaluations and its curated training data.

Use the same evaluations to compare models and catch regressions after updates. When you switch providers, carry forward the knowledge, tests and training data that reflect how your firm works.