AI that belongs in your product—not a demo
Teams rush to add chat widgets without retrieval, logging, or fallback paths. We integrate large language models into your existing workflows: support copilots with citations, document search, sales assistants, and internal ops tools. Each feature is designed with human handoff, audit trails, and cost controls.
Our engineers implement embeddings pipelines, vector stores, and prompt templates versioned alongside application code so improvements are repeatable and measurable.
Retrieval-augmented generation (RAG) done right
We chunk, embed, and index your knowledge base with metadata filters so answers stay relevant to the user's role and account. Responses include source links where appropriate, reducing hallucination risk and building user trust.
Evaluation suites track accuracy, latency, and regression as you update models or content. We monitor token usage and set budgets to keep operating costs predictable.
Streaming UX and production observability
Users expect responsive interfaces. We stream tokens safely, handle cancellation, and surface errors gracefully. Logs capture prompts, retrieved context hashes, and feedback signals for continuous improvement.
Security, compliance, and responsible AI
PII redaction, role-based access to knowledge, and policy prompts align with your industry constraints. We document data flows for security reviews and help you choose models and hosting regions appropriate to your customers.