What We Build

Our work falls into three areas. They aren't independent; each one makes the others possible.

Product Engineering

End-to-end SaaS development. The same team owns everything from the interface to the deployment infrastructure.

• Interface layer. Interfaces built on a design system, with accessibility and performance targets defined up front.
• Application layer and API. Versionable, documented services with backwards compatibility thought through.
• Deployment and operations. Automated deployment pipelines, observability, and rollback paths.

Before we call something finished, we watch it run in production and check its error rate and latency profile.

AI and LLM Research

Systems built on large language models that run in production.

• Retrieval-augmented generation. Chunking strategy, embedding choice, reranking, and measuring retrieval quality.
• Agent architectures. Tool use, state management, fault tolerance, and loop control.
• Evaluation infrastructure. Writing the first version of an LLM system takes a week. Proving that version is better than last week's takes infrastructure. Evaluation sets and regression tracking are a separate line of work for that reason.
• Cost and latency optimization. Model selection, caching, prompt size reduction, and batching.

Data Infrastructure

The layer that feeds the products. The first thing to break under scale.

• Collection. Reliable, rate-limit-respecting ingestion from sources.
• Processing. Normalization, deduplication, and schema validation.
• Enrichment. Entity resolution and joining against external sources.

What We Don't Build

Structures nobody can take over. One-off code that no one else can read or change doesn't count as finished here.

Features where AI is the label. Our test is simple: would this work without AI? If the answer is yes, it's probably not our work.

Consulting whose output is a slide deck. Our output is working software.