Generative AI development: LLM-powered apps, copilots, and content systems grounded in your data, with evaluation built in from day one.
A great demo and a reliable product are different things
Generative AI features are easy to prototype and hard to trust in production the same prompt that worked in a demo drifts, hallucinates, or breaks on an edge case nobody tried. Most generative AI projects fail after launch, not at the demo stage, because evaluation and monitoring were never built in.
What we build
- LLM-powered copilots and assistants embedded in your product or internal tools
- Content and drafting systems grounded in your brand voice and real examples
- RAG pipelines so generated output is grounded in your data, not general knowledge
- Evaluation suites that catch regressions before your users do
- Cost and latency tuning so generative features stay affordable at scale
How we work
Prototype fast to validate the idea, but treat the prototype as disposable
Build the evaluation set before the production pipeline, from real examples
Ground every generation in retrieved, real data wherever accuracy matters
Monitor output quality continuously after launch models and data both drift
Typical stack
Frequently asked questions
Anyone can call a model API. Generative AI development is the engineering around the model that makes it reliable in production: retrieval grounding, prompt and output evaluation, monitoring, cost control, and fallback behavior when the model gets it wrong. That layer is most of the actual work.
You cannot eliminate it, but you can make it rare and catchable: ground generation in retrieved real data via RAG rather than relying on the model's training knowledge, constrain outputs to a defined format where possible, and run continuous evaluation against known-correct answers to catch drift.
We stay model-agnostic and pick per use case Anthropic's Claude, OpenAI's GPT models, and open-weight models like Llama each have different strengths on cost, latency, and reasoning. The retrieval and evaluation layer we build around the model matters more than which one you start with.