AI & Machine Learning

We wire models and agents into products people actually use, not into a notebook. That means retrieval pipelines that stay accurate as your data changes, evaluation harnesses that catch regressions before users do, and guardrails that keep the model on task.

Send a brief

Node.jsGoPythonClaudeOpenAILangChain

Backend·AI

The gap between a model that works in a notebook and a feature that survives production traffic is where most AI projects actually die. Retrieval that stays accurate as your underlying data changes, an evaluation harness that catches a regression before a user does, and guardrails that keep an agent from doing something expensive and wrong — that's the part we build.

We don't start from a blank model. Most engagements begin with retrieval against your existing data, because it ships faster and is easier to explain to a customer than a fine-tuned one — fine-tuning comes later, only if the evaluation numbers actually justify it.

You need this if

—You want an AI feature that answers questions about your own data, not generic chat
—You've shipped an LLM feature and it works until it doesn't, with no way to tell why
—You're evaluating whether an agent can safely take actions, not just draft text

What this covers

LLM & RAG pipelines
Agent orchestration
Evaluation & guardrails

Typical stack

Node.jsGoPythonGraphQLgRPCRustClaudeOpenAILangChainpgvectorPyTorchModal

Questions

Do you fine-tune models or use existing ones?

Existing models with retrieval, in almost every case. Fine-tuning is slower, more expensive, and rarely the actual bottleneck — we'll tell you if your project is the exception.

How do you stop an AI feature from hallucinating in production?

An evaluation harness that runs against real examples before every change ships, plus guardrails that constrain what the model is allowed to do rather than trusting it to behave.

Can you work with our existing data infrastructure?

Yes — a retrieval pipeline is only as good as what it retrieves from, so we usually start by auditing what you already have before adding anything new.

Often paired with

Cloud & DevOps Data Engineering

At a glance

Time to live MVP72 hrs

Code & IP ownership100%

Core stack surfaces12

Taking projects for next quarter

Let us build somethingworth maintaining

Tell us what you are building and what is in the way. You will speak to an engineer, not an account manager, and leave the call with a straight answer.