AI and agent evaluation
Custom evaluation environments built around your actual model, agent, workflow and the ways it is most likely to fail.
Available now. Our core engagement.
AI evaluation and expert data
We find where AI systems fail, and help make them better.
East Gate Labs evaluates advanced AI models and agents through custom benchmarks, expert review and real-world testing, then turns what we find into data you can use to improve them.
AI evaluationAgent evaluationExpert dataPost-trainingApplied AI research
The evaluation gap
Benchmarks tell you how a model scores. We tell you how it actually behaves.
Public benchmarks miss your use case
They rarely reflect your users, your data or the workflows your system runs in production.
Automated judges overlook subtle errors
LLM-as-a-judge scoring often misses mistakes only a domain specialist would catch.
In-house evaluation teams are expensive
Hiring and keeping a dedicated evaluation function takes time most AI teams don't have.
Generic annotators lack depth
Large crowd networks seldom have the specialist expertise that hard tasks demand.
East Gate Labs combines automated evaluation infrastructure with carefully selected domain experts, so AI systems are tested against the conditions that actually matter.
What we do
From finding failures to fixing them.
Four connected services. Each one stands on its own, and together they form a single improvement loop.
Expert evaluation
Domain specialists review outputs wherever automated scoring isn't enough. We start with a tightly managed regional network, which lets us vet and quality-control every reviewer before scaling internationally.
Available now. Matched per domain.
Training and improvement data
Every failure we find becomes something you can train on.
Available now. Built from your findings.
Post-training and continuous evaluation
Evaluation shouldn't end in a PDF. We close the loop so each round of feedback is measured against the last.
Evaluate›Find failures›Generate feedback›Improve›Re-evaluate
Expanding. Rolling out with early partners.
Our method
Six steps, one continuous loop.
Every engagement follows the same path, from understanding your system to proving that a fix actually worked.
- 01
Understand
Define the model, the production use case and the failure modes that matter most.
- 02
Design
Build evaluation scenarios and clear success criteria.
- 03
Evaluate
Run models and agents through automated and expert-reviewed testing.
- 04
Diagnose
Identify systematic failures, edge cases and capability gaps.
- 05
Improve
Generate expert feedback, datasets and post-training inputs.
- 06
Re-evaluate
Measure whether the change actually improved performance, then start the next cycle.
This loop is also the architecture of the platform we're building, so the process you work with today is the one it will automate.
Research
Independent research into how AI systems reason and fail.
We study how increasingly capable AI systems reason, fail, adapt and interact with real-world information, and publish what we learn.
STUDY 001In progress
When "I don't know" is the correct answer
Testing whether frontier AI models can recognize when the available evidence is insufficient to reach a scientifically valid conclusion.
Get the results when published
Expert network
Starting regionally.
Expert intelligence, not generic annotation.
AI evaluation increasingly needs expertise that automated systems can't reliably reproduce. We build evaluation teams from vetted specialists, matched to the domain and difficulty of each task.
Software engineering
Mathematics
Physics and science
Healthcare
Finance
Legal
Cybersecurity
Business and operations
Starting regionally.
Scaling globally.
Starting with a regional network isn't a limit on where we work. It's how we control quality: we know every reviewer, so we can stand behind every judgment they make.
- Vetted individuallyCredentials and domain skill are tested before anyone reviews client work.
- Matched to the taskReviewers are assigned by domain and difficulty, never treated as interchangeable.
- Managed directlyOur own team oversees review quality, with no subcontracted marketplace in between.
Why East Gate Labs
Built to find what others miss.
Research-led evaluation
We design experiments around your failure modes instead of running generic benchmarks.
Expert quality
Specialists are vetted and matched by domain competence, not pooled as interchangeable annotators.
From evaluation to improvement
Findings become expert feedback, datasets, training inputs and the next evaluation cycle.
Founder-led execution
You work directly with the people designing your evaluation system, not an account manager.
Who we work with
Built for teams pushing AI into production.
AI startups
Teams building models, agents and vertical AI products.
Model and agent developers
Teams that need custom capability and reliability testing.
Enterprise AI teams
Organizations deploying AI into high-value internal or customer-facing workflows.
AI infrastructure companies
Companies building tooling for training, orchestration or deployment.
Current focus
SoftwareFinanceHealthcareScienceLegalCybersecurity
and any domain where expertise decides the answer.
Work with us
Have an AI system you need to trust?
Tell us what you're building and what you need to measure. We'll help design an evaluation around it.
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