The autonomous AI engineer that never leaves production
Beaker watches every correction your users make, runs the experiments a human engineer would, and ships the fix — so your model improves without you touching a prompt.
Three steps, running on a loop
Beaker is a standing autonomous AI engineer — it never stops testing ways to make your model better.
Capture failure cases
Every thumbs-down, manual edit, or override is a labeled failure case. Beaker listens on your existing feedback channel and turns corrections into structured signal — no annotation team required.
Try solutions
Beaker runs the loop an engineer would: few-shot rewrites, retrieval tweaks, prompt search, DSPy-style optimization. Each candidate is scored against your real corrections before anything ships.
Auto-adapt
When a fix depends on missing context, Beaker infers the rule behind the correction, drafts it in plain language, and routes it to your end client to confirm. Confirmed rules apply automatically from then on.
See every fix before it ships
The Beaker dashboard gives full visibility into captured failures, running experiments, and confirmed rules.
| Case | Vertical | Status | Confidence |
|---|---|---|---|
| Order status misread as "delivered" | Supply Chain | Adapted | 96% |
| Missing sales-tax exemption clause | Finance | Testing fix | 71% |
| Wrong dosage unit in summary | Healthcare | New | — |
| Redline on indemnification clause | Legal | Testing fix | 64% |
| Tone mismatch on refund reply | Customer Support | Adapted | 91% |
from beaker import Client client = Client(api_key="sk_live_...") client.capture( input=user_query, output=model_response, correction=user_edit, )
Drop it into your feedback path
Runs alongside your existing model — no retraining pipeline
Proposes fixes only once confidence clears your bar
Every accepted fix is versioned and reversible
Built for teams shipping AI that has to be right
Correct it once — Beaker learns the carrier's format for good.
Clinician edits to a summary become the style your model applies next time.
A single override on a risk memo turns into a rule applied automatically.
Redlines from reviewers teach the model what your firm actually wants.
Agent edits to drafted replies sharpen adherence with every shift.
Rejected diffs and manual fixes teach the agent how your team ships.
Start free. Scale when it's working.
For prototyping and small workloads
For models already in production
Stop fixing the same failure twice.
Get an API key and capture your first correction in the next five minutes.