Your agent sounded right. It did the wrong thing.
Run 88213 passed every check in the stack: 200 OK on every tool call, a 1.2-second reply, 4.6 out of 5 from the LLM judge. It also changed a customer’s address before checking who was calling.
Vaicore puts expert reviewers on the whole run — every step, tool call and spoken turn — and turns what they find into tests your next release has to pass.
Every dashboard passed it. A reviewer caught it on the first pass.
Tracing shows latency and tokens. LLM judges grade the last sentence. Labeling tools see one string at a time. None of them follow the run from the first word to the last side effect. Vaicore does, so step 3 has nowhere to hide.
Review the run, not the reply.
Every user turn, tool call, argument, return value and spoken reply sits on one timeline, with the audio. Reviewers see what the agent did, in the order it did it.
Observation first. Judgment second.
Reviewers can’t score until every check is pinned to evidence in the run: a step, a timestamp, a payload. Five opinions become five facts you can audit.
Test the rubric before you pay a human to use it.
Thirty simulated reviewers — strict, lenient, new, expert — run your rubric on sample runs first. Ambiguous questions and dead branches surface in minutes, not after a week of disagreement.
Experts who agree, and the maths to prove it.
Reviewer → QA → sign-off, with agreement scored on every reviewer pair and hidden gold runs in every queue. Anyone who drifts below 90% is pulled and their reviews re-checked.
Every failure becomes a test your next release must pass.
Adjudicated failures graduate into a versioned gold suite. Your CI replays it on every release candidate, and a regression blocks the deploy. Reliability is scored with pass^k, as in τ-bench.
And the data behind your agents isn’t a template either.
Scanned contracts with nested tables. Dashcam footage at night. Support calls in two languages. Model answers that sound right and aren’t. The same studio that reviews your agents annotates the data they learn from, set up around yours.
Agents that listen need data that hears everything.
The same studio annotates the audio your agents learn from: diarization through cross-talk, Devanagari and Roman side by side, English tokens tagged, bad audio rejected at ingest.
Agents that read need ground truth that doesn’t guess.
OCR pre-fills every field. Annotators confirm key-value links and table cells, and PII is masked before anything trains.
Agents that see need your ontology, one click per object.
SAM 2 draws the mask from a single click. Attributes follow your hierarchy, and track IDs carry across frames.
Your rubric. Side by side. Hallucinations tagged.
Evaluators compare A and B on your dimensions and highlight the exact words that contradict the image. Preferences export as DPO, ORPO or KTO pairs.
Your models do the first pass.
Connect any internal model or API. Confident predictions go straight through; people only review and correct the rest, typically cutting turnaround by 40–70%.
From your first 50 runs or files to a working studio in about four weeks.
Your tools, your policies, your ontology, your rubric. Our engineers fit the studio to your agent and your data instead of handing you a template.
- Send real samplesday 0
- We fit the studioweek 1
- Pilot batchweeks 2–3
- Scale and gateweek 4+
Traces and call recordings never have to leave your VPC.
Same studio either way. When your data can’t leave, Vaicore runs next to your trace store, storage and models, and reads media through short-lived links. Nothing is copied out.
What trajectory review found in one month of production calls.
A voice-support team ran 12,000 production calls through Vaicore review after their dashboards and LLM judge had passed them.
Dashboards tell you it ran. Vaicore tells you it was right.
| Dashboards & LLM judges | Template labeling tools | Vaicore | |
|---|---|---|---|
| Sees | Logs, latency, the final answer | One string or image at a time | The whole run: turns, tools, audio, outcome |
| Decides | A model grading a model | Whoever read the guideline | Calibrated experts, evidence first |
| Step-3 failures | Passed | Not modelled | Caught and cited |
| Produces | A dashboard | Labels | A gold suite your CI runs |
| Fits you | Generic metrics | Their templates | Your schema, policies, deployment |