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AI reliability & improvement

Make existing AI systems dependable.

We improve retrieval, grounding, evaluation, performance, cost and maintainability in AI systems that already exist.

When it fits

Is this the right starting point?

You already have an AI assistant or workflow, but it gives wrong or unsupported answers, is slow or costly, or nobody trusts it.

Scope

Retrieval & grounding

Better chunking, hybrid keyword + vector search and source handling.
Evaluation & testing

Test sets and scoring so quality is measured, not guessed.
Prompt & model tuning

Clearer instructions, structured outputs and the right model for the job.
Performance & cost

Find where time and tokens are spent, and reduce both.
Architecture review

Data flow, failure handling and maintainability.
Failure analysis

Trace the questions it gets wrong back to their causes.

Findings report: What is going wrong, why, and the evidence for each finding.
Prioritized fixes: A ranked list of changes, with quick wins separated from bigger work.
Evaluation set: A reusable set of test cases to check future changes against.

A focused diagnostic engagement that identifies the highest-leverage reliability improvements.

It starts with a short call to confirm scope. You then receive a written proposal tailored to the work.

Related work

Founder-built systems, described without client names or unpublished results.

Customer support Hybrid search Built

Problem: General-purpose chatbots gave confident but wrong answers and could not keep each organization’s rules and documents separate.
Approach: Strict tenant isolation, hybrid retrieval (keyword + dense vector search) and local re-ranking, so answers come only from the right organization’s documentation.
System: A support platform that turns each organization’s documentation into grounded answers and hands complex requests to people.

Education LLM-as-judge checks Built

Problem: Teachers spend a lot of time writing lesson plans and assessment material that match the curriculum.
Approach: Generate lesson plans and assessments from curriculum data, then run an automated review step (LLM-as-judge) that checks each output against the syllabus before it is shown.
System: A lesson-planning platform for secondary teachers that drafts curriculum-aligned plans and assessments for teacher review.

Tell us what you are trying to improve. We reply within two business days.