Without evals, every prompt change is a guess and every regression is found by a customer. We build a labelled set from your actual traffic rather than invented examples, pick metrics that match the task instead of a generic score, and wire the suite into CI so a quality drop blocks the release. The point is not a dashboard; it is that a bad change stops before it ships.
01 / EVALS
For teams shipping on opinion. A labelled set from your real traffic, metrics that match the task, and a gate in CI so a quality drop blocks a release.
Usually shipped with
Not a bundle to buy. Whichever you start from, the engagement covers what the build actually needs.
02 / Scope
03 / Outcomes
04 / Deliverables
05 / Stack
06 / Why us
Public benchmarks measure a task you do not have. On Brandiligence, five-part template adherence went from 40% to 98% against a held-out set of the firm's own matters, with strict section checks.
A dashboard tells you quality dropped. A CI gate stops the release that dropped it. We build the second, because only one of them changes what reaches a customer.
An eval that only ever confirms the change was good is not an eval. The comparison is run and reported either way, including when it says the work was not worth it.
Week 1
Fifty to two hundred real inputs, labelled with what a correct output looks like. Synthetic prompts measure a system nobody uses.
Week 1-2
Extraction wants field accuracy; drafting wants structure adherence; retrieval wants recall. A single quality score hides the failure you care about.
Week 2-3
The suite runs in CI and blocks a merge when quality drops. An eval nobody enforces is a report nobody reads.
Week 3-6
Providers change models underneath you. The set gets refreshed from new traffic, and drift is tracked rather than discovered.
Legal & IPIn production
A fine-tuned LLM with retrieval over a curated trademark and IP library, built so the model can only cite firm-approved authorities and every draft holds a fixed legal structure an attorney can review and sign.
ESG & sustainabilityIn production
An ESG-intelligence pipeline that ingests dense, unstructured filings at scale, extracts framework-aligned metrics with full source lineage, connects them across reports and years, and serves them through a governed analytics layer analysts can actually trust.
Market intelligenceIn production
A reasoning-first prospect intelligence engine that turns a LinkedIn or company URL into a structured, intent-aware profile. Multi-source ingestion and hybrid RAG uncover ICP fit, strategy, and risks, with every claim locked to a real source rather than invented.
A versioned suite of cases runs against the whole system — prompt, model, tools and retrieval together, because evaluating the model alone tells you very little about the product. Judges combine deterministic rules, a rubric and a model, and the model judge is itself calibrated against human labels, without which the score drifts for reasons no one can trace. The regression gate lives in CI rather than in a document: a build that scores worse than the last one fails. Production traffic is sampled back into the suite, because the cases worth having are the ones that already went wrong once.
Production-proven
Built by engineers who've already shipped this in production.
Fewer than teams expect. Fifty well-chosen real cases beat a thousand synthetic ones, because the fifty contain the failures you actually have.
For some tasks, and we validate that it agrees with human judgment before trusting it. Where it does not agree, humans grade and we say so.
Then that is the first problem, and it is worth more than the eval. Without ground truth there is no accuracy number, and the work is faith rather than engineering.
It slows down the bad ones. The suite runs in minutes; the regressions it catches would have cost days.
Evals, routing, and audit trails for teams whose demo cannot survive real traffic.
View serviceTracing and monitoring for LLM and agent systems, for teams who cannot explain what happened.
View serviceFine-tuning and adapters, for behavior a prompt cannot hold.
View serviceTell us where you are and what you're trying to ship. We'll come back with a concrete plan, the right engineers, and a path to production, not a generic pitch.