Evaluation
Your accuracy number is not an evaluation
A single accuracy figure averages away the cases a review board actually asks about. What replaces it is a breakdown by decision, not a bigger test set.
July 14, 2026
Data infrastructure for AI in regulated environments
From theory to practice.
Theory AI builds the data infrastructure that lets you deploy AI into your ecosystems — pipelines, ground truth, evaluation, and the operations underneath. One team, from scope to production.
Trust and compliance
- SOC 2 Type II
- In process
- SOC 1 Type I
- In process
- FedRAMP Moderate
- In process
- Data residency
- US · EU · on-prem
Why data
The distance between a pilot and production is data work.
A demonstration runs on curated data. Production runs on what the organization actually holds: inconsistent, unlabeled, ungoverned. Closing that distance is the work, and it is most of the work.
It is people work as much as engineering, so the people doing it are permanent employees rather than a task queue. The same team stays with the data as it matures, which is why quality holds after the first delivery.
What we do
Four ways we deliver for clients
All services →01
AI Integrations
Production AI wired into the systems you already run — retrieval over your own corpus, model orchestration, and evaluation so outputs hold up to review, not just demos.
02
Software & Data Engineering
The pipelines and infrastructure underneath: ingestion, transformation, and indexing built to move sensitive data reliably across secure environments.
03
Analytics & Science
Turning that data into decisions — measurement, ground-truth datasets, and applied research that quantify what a model gets right and where it fails.
04
App Development
The interfaces that put it all in front of users — internal tools and applications delivered by the same team that built the data layer.
How the work runs
One team, from scope to production
Every engagement follows the same path, and the same people walk it end to end. The final stage is not a hand-off — evaluation routes back into ground truth, so the loop keeps tightening while the system is live.
Scope the use case
We map what you are trying to put into production against the data you actually hold, and name the gap between them before any build starts.
Build ground truth
Permanent annotators and domain reviewers produce the reference data, with quality measured per person rather than per batch.
Develop and evaluate
Pipelines and models are built and scored against evaluation sets drawn from your real traffic, not curated demonstrations.
Operate and feed back
In production we track drift, cost and refusal behaviour — and route what we learn back into ground truth so the system keeps improving.
Stage 04 feeds back into stage 02 — evaluation is continuous, not a final checkpoint.
Who we serve
Built for environments where the record is scrutinized
We work with teams that answer to auditors, review boards and contracting officers. These are the engagement types we are built for, described by class rather than name.
Federal health agency
Multilingual intake and case documentation where every record has to stand up to audit as well as read accurately.
DoD component
Evaluation for decision-support systems — refusal behaviour, edge cases, and the failure modes a review board will probe before accreditation.
Prime contractors
Ground truth and data operations delivered inside your existing task order, on your reporting cadence and security posture.
Commercial enterprise
Retrieval and annotation over regulated internal corpora, with data residency and access controls scoped per engagement.
Global delivery
Headquartered in DC, delivering worldwide
Theory AI is registered and led from Washington, DC, and operates globally. Our working day overlaps Europe, the Gulf and the US east coast. The team is international by design: we hire wherever the specialism is and train in-house.
- 10
- Global professionals
- 3
- Languages
- 5+
- Programs live
- DC
- Washington HQ
Insights
Notes from the data layer
- Data operations
Per-annotator quality beats per-batch QA
Sampling a batch tells you a dataset has errors. Measuring the people who made it tells you which errors will keep arriving, and lets a correction actually stick.
June 23, 2026
- Retrieval
Retrieval is a data problem before it is a model problem
Most retrieval systems that fail in regulated environments fail before the model is involved. The corpus, not the embedding, is usually the thing that needs work.
May 19, 2026
For agencies and primes
Bring us the use case
Tell us what you are trying to deploy and what data you hold. We will tell you what is feasible, including when the answer is not yet.
Newsletter, coming soon
We are putting together occasional notes from the data layer — evaluation, ground truth, and what deploying AI in regulated environments actually takes. Leave your address and we will tell you when the first one is ready.