Retrieval over your content
Ingestion, chunking, embedding and refresh pipelines that keep answers grounded in current sources with citations.
Model calls are the easy part. The engineering is around them.
Retrieval pipelines, tool layers, structured output and evaluation harnesses — the scaffolding that turns a model call into a system you can depend on.
In this stack
The stack
Written as a reason rather than a feature list, because the reason is what tells you whether it applies to your project.
Our usual choice for long-context reasoning, careful instruction following and tool use.
Used for its embedding models, structured output modes and where latency or cost profile fits better.
Used selectively for orchestration and retrieval plumbing, not as a framework we build the whole system inside.
The standard we use to expose systems to assistants as typed tools and resources.
Embedding, chunking and reranking tuned for the shape of the source documents, not a default setting.
Schema-constrained responses validated with Pydantic, so downstream code gets data rather than prose.
Graded test sets from real inputs, run on every prompt or model change to catch regressions.
Per-request token and timing data, so tuning decisions rest on numbers.
Applied
The kinds of systems this area of the stack is actually used to build.
Ingestion, chunking, embedding and refresh pipelines that keep answers grounded in current sources with citations.
Typed tool definitions with permission boundaries, letting an agent look things up and act within hard limits.
Extraction into validated schemas with confidence scoring and review queues for uncertain cases.
Accuracy baselines, regression runs and dashboards covering cost, latency and fallback rate.
Where it is applied
Support agents, document processing, lead qualification and multi-step workflows — built on retrieval over your own content, with evaluation and escalation designed in.
02System prompt architecture, structured output contracts and graded evaluation sets — so a prompt change is a measured improvement rather than a hopeful edit.
03Custom Model Context Protocol servers that expose your data and actions as typed tools — with authentication, scoped permissions and audit logging built in from the start.
Also in our stack
The language most of our backend work is written in.
Three tools that make a Python API predictable.
Server-rendered by default, interactive where it matters.
The connective tissue between the tools you already pay for.
Tell us what you are building and where it is getting difficult. You will get a straight technical answer before any commercial conversation.