AI Services — Build & Automate
One AI assistant is useful. Several coordinating on a whole workflow is a different engineering problem
A single chatbot answering guest FAQs is a good start. A system where one agent checks room readiness, another updates housekeeping, and a third notifies the guest — coordinating automatically, escalating to a human only when it should — needs real architecture, not just an API key.
Where this gets stuck
Why agentic projects stall before production
- Nobody's defined where a human has to step in. Autonomous decisions are the point — but when a booking gets cancelled incorrectly or a clinical flag gets missed, “the agent handled it” isn't good enough without an audit trail.
- Costs get unpredictable fast. Every agent call adds up, and a pipeline with several agents running in sequence can turn a manageable bill into a surprise one if the cost architecture wasn't designed up front.
- Agents don't coordinate cleanly. Passing context between agents, handling one agent's failure without breaking the whole chain — this is a coordination problem, not a prompting problem.
- Prototypes that worked on clean test data break on real guest, patient, or sensor input. Messy real-world data is where agentic systems actually get tested.
How we run it
Assess, build, scale
Agent Prototype
- A working prototype on your real data — not a synthetic demo.
- Stakeholder-ready walkthrough your ops or clinical leadership can evaluate directly.
- Architecture recommendation — single agent, multi-agent pipeline, or a simpler automation, whichever the use case actually needs.
- Cost and effort estimate for the next phase.
Agent or Agent Pipeline
- Purpose-built agents with defined scope and a fallback path for when they can't complete a task.
- Reliable coordination between agents where more than one is involved, so one failure doesn't silently break the whole chain.
- Human-in-the-loop checkpoints designed in from the start, at the points where a mistake actually costs something.
- Deployment and documentation so your team can maintain and extend it.
- Cost architecture designed to keep usage predictable as volume grows.
Agentic Platform Retainer
- New workflows added to the existing architecture as more of your operation gets automated.
- Governance and audit trails kept current for compliance review.
- Anomaly monitoring — failures and cost spikes flagged before they become incidents.
- Quarterly architecture review against how your business has actually grown.
Real workflow shapes
Real workflow shapes we build
Hospitality
Reservation confirmed → housekeeping notified → room-readiness verified via IoT sensors → guest pre-arrival message sent, with a human notified only if a room isn't ready on schedule.
Healthcare
Vitals reading logged → risk-scoring agent checks against thresholds → nurse notified for any flagged reading, routine readings logged silently → shift handoff summary generated automatically.
Facilities / IoT
Sensor anomaly detected → diagnostic agent checks device history → work order created and routed to the right technician → follow-up scheduled automatically.
FAQ
Common questions
How is this different from a single chatbot or assistant?
A single agent handles one task. A multi-agent pipeline coordinates several specialised agents across a multi-step workflow, passing context and handling failures between them — genuinely a different build.
How do you keep this safe for patient-facing workflows?
Human checkpoints are placed at the specific points where a wrong decision has real consequences, defined during the Assess phase before any code is written — not left to the model's judgement.
Will this replace staff roles?
The workflows we build are designed to remove repetitive coordination work — checking, updating, notifying — so staff spend time on guest and patient interaction, not system-hopping. Scope and framing of any role impact is a conversation we have with you directly during Assess.