AI Services — Foundation
A generic chatbot isn't the hard part. Grounding it in your data is.
Any team can wire up an API key to a large language model in an afternoon. What takes real engineering is making sure it answers from your actual policies, your actual patient records, your actual room inventory — reliably, with the ability to say “I don't know” instead of confidently making something up.
How we run it
Assess, build, scale
LLM Feasibility Check
- Data readiness review — what knowledge base, documents, or records the model would need access to, and what shape they're in today.
- Working proof of concept on a sample of your real content.
- Grounding approach recommendation — retrieval-augmented generation, fine-tuning, or a hybrid, whichever fits your data and budget.
- Cost model — a realistic estimate of ongoing API usage at your expected volume, not a guess.
Production Integration
- Retrieval pipeline built against your actual knowledge base, policy documents, or records system — kept current as content changes.
- Trust-focused UX — showing where an answer came from, flagging uncertainty honestly, and routing to a human when confidence is low.
- Guardrails so the model stays inside its intended scope — a guest FAQ assistant shouldn't attempt clinical advice, and a clinical documentation tool shouldn't invent patient history.
- Testing against real queries, not just happy-path examples.
- Deployment and handoff documentation.
Ongoing Accuracy Management
- Content sync — as your knowledge base, policies, or menus change, the model's grounding updates with it.
- Usage and cost monitoring, so API spend stays predictable.
- Quarterly accuracy review against real user queries and edge cases surfaced in production.
In practice
What this looks like in practice
Hospitality
A guest-facing assistant that answers from your property's actual amenities, policies, and local recommendations; automatic summarization of guest feedback across review platforms into a weekly digest for management.
Healthcare
Draft clinical notes generated from consultation transcripts, reviewed and signed off by the clinician, never auto-submitted; a staff-facing assistant that answers policy and procedure questions from your actual SOP documents instead of institutional memory.
Internal operations
Searchable, natural-language access to maintenance logs, incident reports, or SOPs across an IoT-connected facility, instead of staff digging through folders.
FAQ
Common questions
How do you stop the model from making things up?
By grounding every answer in retrieval from your actual data rather than relying on the model's general knowledge, and by building in honest uncertainty — the system says “I don't know” or escalates rather than guessing, particularly for anything clinical.
Can this work with our existing PMS or HIS without replacing it?
Yes, in almost every case — the integration reads from your existing system via API rather than requiring a platform change.
What about patient data privacy?
Data handling, access scope, and retention are defined during the Assess phase specifically for your regulatory environment before any build starts.
How much does ongoing usage typically cost?
It depends heavily on query volume and document size — we model this against your expected usage during Assess so there are no surprises once it's live.