An eval program begins with release decisions, not with a scoring library. We identify the changes your team makes to prompts, models, tools, retrieval, policies, and workflow logic, then ask what evidence is needed before each change reaches users. The answer becomes a failure taxonomy tied to user harm, product expectations, and operational risk. Generic helpfulness scores are included only when they support a real decision.
AI Reliability Engineering
Stop learning about AI regressions from customer complaints.
LLM evaluation services for production AI. Build golden datasets, calibrated scorers, trace-linked failures, and CI gates your engineering team can own.
Book a 30-minute consultation
30 minutes. No deck. Leave with a clear next step.
System-level interventionProduction-shaped diagnosisInspectable verification
The production trigger
Recognize the failure before naming the service.
Build production-shaped cases, calibrated scorers, and a CI release gate.
A one-line prompt or model change shipped cleanly. Latency stayed green. Errors stayed flat. Response quality fell anyway, because the product had no release gate for the behavior users actually experience.
Trigger detail 1We build the harness around your data, connect it to traces and CI, and make failures reviewable by engineers and domain experts.
Trigger detail 2System diagnosis
Find where the failure actually lives.
The visible symptom is rarely the whole problem. The first pass follows it through the production path until the controllable boundary is clear.
We inspect the evidence already available in traces, support records, incident notes, product analytics, manual test sheets, and domain-review conversations. These sources often reveal repeated failure shapes that have never been named: omitted constraints, wrong tool choice, malformed structured output, unsafe action, poor refusal, broken tone, or a workflow that appears successful but does not complete the user's task. Naming and slicing those failures is the foundation of a useful dataset.
The readiness review covers operating responsibility as well as data. Engineers can maintain code-based checks, but domain experts may need to define rubrics and resolve ambiguous examples. Product leads decide acceptable tradeoffs. We set review paths that match those roles and the team's release cadence. This service fits a live or late-stage product with representative behavior; a pre-product team is usually better served by lightweight manual tests until usage creates meaningful cases.
The intervention
Change the critical path, not the surrounding theatre.
Each workstream targets a different failure boundary. Together they connect product behavior, infrastructure, controls, and verification.
Failure taxonomy and risk slices
Translate product expectations into observable failure classes, severity levels, and critical cohorts. Each class includes a definition, examples, counterexamples, likely evidence source, and the release action it can trigger. Slices may reflect workflow, user role, language, tool, data sensitivity, or model path, ensuring the harness can answer where behavior changed instead of returning only a blended score.
Golden dataset construction
Create versioned cases from real traces, known incidents, expert-authored boundaries, and synthetic variations reviewed for realism. Cases store inputs, context, expected properties, forbidden outcomes, metadata, and provenance. Sensitive production data is minimized or transformed according to your policy. We reserve evaluation splits where useful so tuning does not merely memorize the visible set.
Scorers and calibration
Combine exact checks, schema validation, executable assertions, rubric graders, pairwise comparison, and human review according to the failure type. Model-based judges are calibrated against labeled examples and tested for position, verbosity, and self-preference bias. Disagreements are surfaced for review. A scorer ships with enough rationale that the team can challenge and improve it.
CI and review workflow
Run the right subset on material changes, report failed examples in a readable form, and preserve links to traces and experiment metadata. Thresholds distinguish hard release blockers from warnings and scheduled review. We define who can update a case, approve a changed expectation, or override a gate, including the record required when speed justifies a temporary exception.
Inspectable changes
See what changes in the system.
- Failure taxonomy tied to product risk
- Golden dataset from production-shaped examples
- Deterministic checks and rubric-based graders
- Human-review workflow for ambiguous cases
- Prompt and model comparison runner
- CI thresholds with pull-request reporting
- Dashboard of failure slices and trends
Engineering judgment
The decisions that determine whether the change holds.
What becomes a gate
Select checks that are stable, decision-relevant, and fast enough for the release path. High-impact deterministic failures can block immediately; noisy rubric scores may require repeated runs or manual review. We prevent an ambitious suite from becoming ignored by separating pull-request checks, scheduled evaluations, production monitoring, and deeper experiments.
Judge reliability
Decide where a model judge is fit for purpose and how its output is audited. Calibration includes reviewed labels, disagreement analysis, clear rubrics, and version pinning. When the judge cannot reliably distinguish acceptable cases, the answer may be a simpler assertion, a pairwise review, or a human queue rather than a more elaborate prompt.
Dataset governance
Define provenance, access, versioning, retention, and refresh rules for eval examples. Production-derived cases can contain sensitive content, while expected answers can go stale as policy and product behavior change. Named review roles and refresh triggers keep the dataset trustworthy and prevent unexplained edits from moving the release baseline.
Strong fit
- You have production traffic and customer-visible AI behavior.
- Prompt or model changes can ship without a quality gate today.
Probably not the right fit
- You do not have production traffic to write evals against yet.
- You want offline scores that never gate a release.
Scope, timing, and ownership
Know the commercial shape before the call.
A bounded engagement with a service-specific delivery sequence, an agreed price, and artifacts that stay under your control.
Days 1-2
Failure taxonomy
Define the user-visible failures, high-risk slices, and release decisions the harness must support.
Days 3-5
Dataset and scorers
Build representative cases, deterministic checks, rubric graders, and human-review paths.
Days 6-8
Trace and CI integration
Connect production examples, experiment runs, thresholds, and pull-request reporting.
Days 9-10
Calibration and handoff
Calibrate false positives, document extension workflows, and train the owning engineer.
Typical fixed scope
Mid-four to low-five figures
Most engagements fall in this planning range. Your exact fixed price is agreed in writing after we review the production surface, access needs, and success criteria. The range is guidance, not a quote.
Commercial terms
No open-ended consulting meter.
- Fixed scope agreed in writing
- Fixed timeline agreed in writing
- Fixed price agreed before work starts
- Client-owned deliverables from day one
- No hourly meter or surprise overages
Client ownership
The implementation remains yours.
- Your repository
- Your infrastructure
- Your evals and evidence
- Yours from commit one
Book a 30-minute consultation
30 minutes. No deck. Leave with a clear next step.
LLM Evals
Questions worth settling before the call
Technical, commercial, and handoff questions answered before you book.
Put customer-visible AI behavior behind a release gate.
Bring the failing workflow, current evidence, and the constraints your engineers cannot ignore.
Book a 30-minute consultation
30 minutes. No deck. Leave with a clear next step.