AI features that earn their place in production.
We add language-model capabilities to real products — chat interfaces over your own data, structured extraction from unstructured input, agent workflows, and augmented internal tools. The goal is measurable business value, not novelty.
Workflow first, model second.
The best AI features come from mapping a specific human workflow first, then finding the exact step where a language model can compress or improve it. Sprinkling AI onto a product for its own sake produces demos, not durable features.
For most projects, retrieval-augmented generation over your own data does more than fine-tuning ever could — at a fraction of the cost and iteration time. We start there unless the evidence points elsewhere.
Every AI feature we ship comes with an evaluation set, structured outputs where possible, and monitoring so drift is visible when it happens. LLM features that no one can measure are LLM features no one can trust.
Real integrations, with cost and quality in mind.
- 01OpenAI, Anthropic, or open-model integrations selected for the task
- 02Retrieval pipelines with vector search over your documents or database
- 03Structured outputs, JSON mode, and function calling for reliable downstream use
- 04Prompt evaluation suite covering the cases that matter to your business
- 05Cost and latency budgets built in from the first sprint
- 06Monitoring, logging, and observability for LLM calls in production
What clients ask.
Which LLM providers do you work with?
OpenAI and Anthropic most commonly. We use open-weight models (Llama, Mistral) when the use case requires on-premises deployment or when open models have a clear advantage on cost and quality.
Do you handle fine-tuning?
We can, but we usually recommend retrieval-augmented generation first. Fine-tuning is expensive to iterate on and often unnecessary. If your project genuinely benefits from it, we will say so.
How do you handle data privacy?
We design for it from the start. Sensitive data can be scoped so it never reaches third-party APIs, or the project can run against on-premises models. Every architecture decision starts with the data classification.
Can you audit an existing AI feature we shipped?
Yes. AI audits cover prompt design, retrieval quality, cost per interaction, output structure, and monitoring gaps. Deliverable is a written report with prioritized recommendations.
Ready to start?
Send a short description of what you're building. If it's a fit, we'll schedule a discovery call within a few days.
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