Will you lock us into a vendor?

No. We recommend tools on fit. The rubric, the set, and the gates are yours. If a platform is the right harness today, it is still a choice, not a dependency we profit from.

Can LLM-as-judge be the whole system?

Not for high-stakes dimensions. Judges are fast and useful once calibrated. Policy, leakage, and the cases that would end a programme still need code checks and humans.

What does “calibrated” mean here?

That the judge’s scores track human scores on a held-out sample, with the disagreement characterised — not that a prompt looked reasonable in a notebook.

Solutions

Automated evaluation harnesses

A harness makes evaluation a regression signal: the same dimensions, run before merge, nightly, and before release. Judges are calibrated against humans. The gate is a decision, not a dashboard.

For platform and applied-AI teams who already generate outputs in CI, and who need quality to fail a build the way a unit test does — without marrying a single vendor.

Tool-neutral, on purpose

DeepEval, Braintrust, Arize Phoenix, LangSmith, RAGAS, Vertex Evaluate, custom Python: we select and configure what fits the failure class and your stack. Independence means no preferential relationship. The architecture should still stand if the tool changes.

Deterministic checks belong in code. Qualitative known failures belong to an LLM-as-judge, itself monitored for drift. Trajectories belong to an agent-judge or a human. Matching the evaluator to the failure is the work; the library is an implementation detail.

We will not pretend that a single framework covers faithfulness, policy, and tool-use equally well. The harness is a composition. If a vendor later wraps the same composition, you still own the gates.

Calibrate the judge

An uncalibrated judge is another fluent system. We design the judge prompt, measure it against human scores, and keep measuring it. When the judge drifts, the harness has failed even if the candidate model has not.

Calibration is versioned with the rest of the stack. A judge prompt change is a model change. It goes through the same gate it is meant to enforce, against a frozen human sample.

In the loop

Pre-merge for cheap checks, nightly for the full golden set, pre-release for the gate that leadership actually owns. Dashboards and alerts exist so a miss is an incident, not a surprise in production. Cost and latency sit next to quality; a cheaper model that fails the rubric is not cheaper.

The first working gate is usually a pilot: one use case, one golden set, one release policy. Full build is that gate made ordinary — several use cases, CI that developers actually run, a scorecard a risk reader can open.

Where the harness runs

Cadence is a starting point. Risk sets the clock.

Pre-merge

Deterministic checks and cheap judges on touched paths

Nightly

Full golden set, slice report, judge health

Pre-release

The written threshold: ship or hold

Judge monitor

Agreement with humans; drift of the evaluator itself

Gate	What it is for
Pre-merge	Deterministic checks and cheap judges on touched paths
Nightly	Full golden set, slice report, judge health
Pre-release	The written threshold: ship or hold
Judge monitor	Agreement with humans; drift of the evaluator itself

Engagement

Typical shape: Framework + Pilot, or Full Build (harness + datasets), typically 6–10 weeks for a first working gate; 3–5 months for a production stack. Duration is typical, not contractual. Formats and deliverables are written on the evaluation ledger.

Questions

Will you lock us into a vendor?: No. We recommend tools on fit. The rubric, the set, and the gates are yours. If a platform is the right harness today, it is still a choice, not a dependency we profit from.
Can LLM-as-judge be the whole system?: Not for high-stakes dimensions. Judges are fast and useful once calibrated. Policy, leakage, and the cases that would end a programme still need code checks and humans.
What does “calibrated” mean here?: That the judge’s scores track human scores on a held-out sample, with the disagreement characterised — not that a prompt looked reasonable in a notebook.