Grounded answers
Retrieval-augmented generation against your real documents and tickets, with citations and confidence scoring so the bot says I do not know when it should.
Customer-facing bots and internal knowledge assistants that actually work.
Retrieval-augmented generation against your real documents and tickets, with citations and confidence scoring so the bot says I do not know when it should.
Detects intent, sentiment, and uncertainty, and routes to the right human with context preserved. No more do-loops where the bot loses its place.
Automated test suite of real user questions runs on every change, so we catch regressions before they hit production.
We pick the right model for your ai chatbots build, then blend providers behind a single internal interface.
GPT and embeddings. Broad ecosystem, strong structured-output and tool use, the safest default for general production.
Anthropic's frontier model. Our default for agents and long-context work where reasoning matters more than raw speed.
Google's long-context multimodal family. Excellent for document and video pipelines, especially at scale.
xAI's model with live-web reasoning and a different blend of strengths. Useful for research-style and edge-case workloads.
Open-weight models with strong cost-to-performance. We use it self-hosted when residency or unit economics demand it.
Citation-grounded search API for live-web augmented agents. Drops cleanly into RAG pipelines that need fresh sources.
Retrieval-augmented generation against your real data, hard refusal when no relevant context is found, and a continuous evaluation harness running real questions on every change. Hallucinations are a system design problem, not a model problem.
Yes. We treat the underlying assistant as a service and deploy it to whatever channels matter. The brain is shared, the surfaces are interchangeable.
Your docs, tickets, knowledge base, product data, transcripts. We ingest, chunk, embed, and keep it in sync as your sources change. We do not rely on what the model already knows.
Two parts: model inference per conversation (usually pennies on modern models) and infrastructure for retrieval. We budget both upfront and tune as usage grows.
A 30-minute call to map the highest-impact automation and AI opportunities in your business. You leave with a prioritised list, whether you hire us or not.