ai training services
data and evaluation that survive contact with a real model.
two things that are hard to buy well: the training data a model learns from, and the
evaluation that tells you honestly whether it improved. you get the scores, the evidence,
and the harness behind every number we hand you.
two tracks
the discipline is the same. the deliverable is not. pick the track that matches what you
are actually trying to move.
track a
for frontier labs
you are training or grading a model and need data and verdicts at a quality bar your
own team would defend in review.
- expert training corpora. authored and captured datasets across code, expert documents, and specialist domains, delivered fine-tune-ready with per-record scores attached.
- evaluation rubrics. weighted, multi-dimension rubrics with written anchors per score point, plus hard auto-fails for the patterns a naive quality score would otherwise rank highest.
- LLM-as-judge harnesses. judges that must reason before they score, tuned and validated against human adjudication rather than trusted out of the box.
- head-to-head benchmarking. competing systems driven inside the real software they ship in, instrumented for output, latency, and telemetry, scored blind to each other.
- human adjudication at volume. ground-truth-first worksheets, gated self-audit, and an independent second reviewer on every verdict.
- RLHF and preference data. ranked pairs and graded completions with the reasoning that justified each ranking.
track b
for enterprise
you are putting AI into a product or a process and need it to work on your domain,
measurably, without a research team of your own.
- domain fine-tuning. we build the corpus from your documents, tickets, transcripts, and code, then post-train and measure the lift against a held-out set.
- your own eval suite. the thing most teams skip. a benchmark for your use case so model upgrades and prompt changes become a number, not an argument.
- agentic systems in production. harnesses that drive real desktop and web apps, browser agents, retrieval, and real-time voice, integrated and kept alive.
- evidence-grounded verification. answer-checking pipelines that cite verbatim source text and refuse to bridge a gap with model prior knowledge.
- model selection. a decision backed by your data, latency, and cost envelope, not a leaderboard screenshot.
- honest scoping. including the part where we tell you a problem is not ready for AI yet.
what that has meant in practice
recent programs, described as far as NDA allows. the numbers are counted from the work
itself, not estimated.
600+
expert verdicts adjudicated
~700
instrumented capture sessions
100+
codebases instrumented
one program benchmarked frontier AI coding assistants head to head inside the editors they
ship in. custom instrumentation captured every suggestion, its latency, and the telemetry
behind it, across more than a hundred purpose-built task repositories.
another verified AI answers against cited evidence in expert documents across medicine, law,
finance, science, technical documentation and more. every verdict is backed by verbatim
quotes checked against the source page, then re-checked by a second reviewer. client and
product specifics stay under NDA.
how we grade data
quality data is a pipeline, not a purchase. ours turns raw examples into a quality-ranked
training set you can fine-tune on directly.
01
ingest
normalize raw examples into a fine-tune-ready schema: messages plus full tool-call and function signatures.
02
clean & safety-filter
dedupe, strip low-signal and generic responses, and remove unsafe content before a single dollar is spent on grading.
03
score on a weighted rubric
every record graded on multiple weighted dimensions by an LLM-as-judge that must reason first, then score, with hard auto-fail criteria for the failure modes that matter.
04
rank & select
read the full score distribution, keep the top band, and hand back a quality-ranked set with the per-record scores that got it there.
how we run evals
a benchmark you can trust is run in the environment the model actually works in, then
adjudicated twice.
01
ground truth first
domain experts author the expected answer before any model output is seen, and the timestamp is checked. scoring happens against a spec, not a vibe.
02
run in real software
custom harnesses drive the actual editors, browsers, and platforms under test, capturing every output, its latency, screen recording, and the telemetry behind it.
03
score on anchored rubrics
every dimension is scored against written anchors, competing systems are scored independently and never see each other's results, and judges justify before they score.
04
verify twice
a gated self-audit, then an independent second reviewer who re-checks every verdict against the evidence, quoted verbatim and matched to the cited source page. nothing ships on a single opinion.
what you actually receive
no black boxes. every engagement hands back the artifact and the machinery that made it.
the dataset
fine-tune-ready JSONL, quality-ranked, with per-record dimension scores and the judge's reasoning attached.
the rubric
written anchors per dimension per score point, auto-fail criteria, and the scoring guide a new reviewer could pick up cold.
the harness
the code that ran the benchmark, so you can re-run it on the next checkpoint without us.
the evidence
raw captures, telemetry, recordings, and verbatim quotes behind every verdict, so any score can be traced back to what produced it.
how engagements start
scoping call
free · 30 minutes
you describe the problem, we tell you which of the two tracks fits, what it would take, and whether you should hire us at all.
pilot
fixed scope · fixed price
a small graded slice or a single benchmark run against your real target. you get the rubric, the data, and the harness. if it is not useful, it ends there.
program
ongoing
continuous data generation, benchmark cycles per checkpoint, or an embedded engineering engagement. scaled to your release cadence.
tell us what you are training.
lab, enterprise, or startup. we will tell you which of the two tracks fits, what it would
take, and whether you should hire us at all.