Your document AI demos great. In production, it’s quietly wrong.
LLM pipelines that read complex documents plateau around 80% and fail silently on the cases that cost the most. A wrong value that looks right, passes validation, and surfaces as a customer problem months later. We make them reliable enough to ship.
Golden datasets · evaluation harnesses · layer-by-layer accuracy baselining · regression testing. For insurance, legal, and compliance teams.
“It demos beautifully. Then it’s quietly wrong on the complex layouts, and you don’t find out for a quarter.”
“We plateaued at 80%. The errors are subtle: a transposed figure, a wrong date, impossible to catch with a rule.”
“How do we prove accuracy when we have no ground truth to measure against?”
“The demo used one clean document. Production sends us a thousand messy ones.”
The problem isn’t your model. It’s that nothing measures, per layer and on every change, whether the pipeline is actually right.
02The cost
Why “mostly accurate” won’t ship.
94%
field accuracy is 600 wrong fields per 10,000 documents, clustered on exactly the hard cases.
Wrong > blank
a wrong value is worse than a blank one. It passes your validation and propagates downstream silently.
1 claim
in insurance, legal and compliance, one bad extraction is a claim, a mispriced policy, or a compliance exception.
03Method
How we get a pipeline to production-trust.
We don’t rewrite your model. We build the measurement and correction layer around it, the same approach we use to take a plateaued document pipeline to production-trust accuracy.
01
Golden dataset.
A version-controlled, expert-validated set of real documents with correct answers. The ground truth everything is measured against.
02
Layer-by-layer baselining.
Your pipeline is many stages: parse, extract, classify, apply rules, report. We baseline each one, so you know which layer fails, not just that the final number is bad.
03
The evaluation harness.
Hundreds of checks that score the pipeline against the golden set, per layer, on every change. Deterministic checks plus LLM-as-judge where needed.
04
Loop engineering.
We fix the failing layers and regression-guard the rest, so improving one thing never silently breaks another.
05
Drift monitoring.
Golden sets and judges rot as models and documents change. We keep them honest over time.
You get a number you can defend, and visibility into exactly which outputs to trust.
04Proof, not promises
A financial-documents platform
Reads and checks values across long, messy financial documents.
Problem
A multi-stage extraction and rules pipeline where subtle errors on the hardest documents slipped through, and the team had no way to prove a build was clean before it shipped.
What we did
Built a version-controlled golden dataset and an evaluation harness that scores every layer on each change, baselined the pipeline stage by stage, and added regression checks.
Outcome
Per-layer quality reporting and an accuracy number the team can defend, so regressions surface before release instead of in front of a customer.
They came to us. This is the work we get hired to do.
Field accuracyBefore / After
▼ Before
70%silent errors
▲ After
90%+production-trust
05Engagements
Start small. Stop after any stage.
Fixed-price first stage. No lock-in. You own the code and the golden dataset from the first commit.
SOC 2 Type II · ISO 27001 · GDPR · On-prem on request · You own the code and data
07FAQ
Straight answers.
“We already use an evals tool (Braintrust, LangSmith).”
Those give you a harness you still have to fill. The hard part is the domain golden dataset and getting to the number. That is what we build, in your vertical.
“Won’t the base models just get better?”
They improve, but non-determinism is the cause of the problem, not a bug that gets patched. The durable asset is your domain golden set and error taxonomy, and it keeps paying off across model upgrades.
“Is our data safe?”
Sandboxed, read-only where possible, human-monitored, SOC 2-aligned. You own everything, on-prem on request.
“What if the audit says we’re fine?”
Then we tell you, and you spent little to find out. We would rather say no than sell you work you don’t need.
08Final step
Tell us where your pipeline breaks. We’ll tell you honestly if we can fix it.
A free 30-minute accuracy review. No pitch deck. We’ll ask about your documents, your current accuracy, and where it fails, then tell you what’s achievable and roughly what it costs.
