Do you build agents that can use our APIs?

Yes. We design tool using agents with structured prompts, tracing, and a hard coded eval harness. That way you can ship with confidence and roll back individual capabilities without taking the whole agent down.

How do you handle hallucinations?

Three layers of defence. Retrieval grounding with citations so the model has facts to lean on. Structured output through JSON schemas so the response shape is predictable. And an eval harness that gates every release. Plus a content safety guardrail at runtime.

Can you work in our cloud?

Yes. We deploy in AWS Bedrock, Azure OpenAI, GCP Vertex, or self hosted Llama and Mistral. Whatever your data residency and compliance posture requires.

AI / ML

AI features that survive contact with users.

We build RAG pipelines over your data, fine tune task specific models, and ship agentic workflows. Then we do the unglamorous part: evals, guardrails, and observability that keep them honest once real users get involved.

Start a project All services

0.88

RETRIEVAL RECALL IN OUR TRIP PLANNER

Eval gated

EVERY RELEASE

Claude · GPT-4

SHIPPED IN PRODUCTION

WHAT WE SHIP

How ai / ml engagements actually work.

RAG and retrieval pipelines

Semantic search, hybrid retrieval, rerankers, and grounded generation with citations. Built on pgvector, Pinecone, or whichever warehouse you already trust.

Custom model fine tuning

Task specific models trained on your data. Think classification, extraction, structured output. Trained, evaluated, versioned, and shipped through MLflow.

Agentic workflows

Tool using agents for ops, support, and research. We build them with rigorous evals so failures stay safe and predictable instead of mysterious.

MLOps, evals, and guardrails

The unglamorous part. Eval harnesses, drift monitoring, prompt versioning, content safety, and audit logs that keep compliance happy.

THE STACK

Tools we reach for.

Claude, GPT-4, Llama, Mistralpgvector, Pinecone, WeaviateLangGraph, DSPy, Cohere rerankMLflow, Weights & Biases, BraintrustModal, Replicate, Bedrock

ENGAGEMENT MODELS

Pick the shape that fits.

Discovery sprint
Embedded ML squad
Fixed scope pilot
Long haul partnership

FAQ

Questions we get asked first.

Do you build agents that can use our APIs?: Yes. We design tool using agents with structured prompts, tracing, and a hard coded eval harness. That way you can ship with confidence and roll back individual capabilities without taking the whole agent down.
How do you handle hallucinations?: Three layers of defence. Retrieval grounding with citations so the model has facts to lean on. Structured output through JSON schemas so the response shape is predictable. And an eval harness that gates every release. Plus a content safety guardrail at runtime.
Can you work in our cloud?: Yes. We deploy in AWS Bedrock, Azure OpenAI, GCP Vertex, or self hosted Llama and Mistral. Whatever your data residency and compliance posture requires.

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