The AI Platform That Powers Every THB Product
Every vendor says "AI-native." Here is what we mean: a single runtime that executes every AI interaction -- chat, voice, WhatsApp, autonomous background jobs -- through the same pipeline. The LLM proposes. A policy layer validates. Only approved actions execute. Every decision is logged. No exceptions.
AI Architecture: From LLM to Patient
Every AI interaction -- whether a receptionist chatting in the CRM or a background agent analyzing care gaps overnight -- flows through the same validated architecture.
Platform Capabilities
The infrastructure that makes healthcare AI safe, auditable, and production-ready -- not a chatbot bolted onto a website.
Pluggable LLM Backend
Swap foundation models without changing a single agent. Multi-provider support with automatic fallback -- if the primary provider fails, the system switches seamlessly. No vendor lock-in on the AI layer.
Policy Engine
The LLM never acts directly on patient data. Every proposed action passes through a deterministic policy layer that enforces permissions, validates parameters, prevents unsafe mutations, and checks rate limits before execution.
Trust & Safety
Input sanitization against prompt injection attacks, toxicity filtering on every interaction, and per-agent topic boundaries that prevent scope creep. The agent handles its domain and nothing else.
Clinical Knowledge Grounding
Agents reason over clinician-reviewed protocols and verified clinical data -- not training data. Hybrid search retrieves the most relevant clinical context for every query. Responses are grounded, not hallucinated.
Auto-Generated Tools
Every entity in the Application Engine automatically becomes a set of tools the AI can use -- query, create, update, trigger workflows. No manual tool definitions. The AI capabilities grow as the schema evolves.
Intent Routing
Inbound queries are classified and routed to the right specialist agent automatically. Goal-shift detection catches when a conversation changes topic mid-stream and re-routes accordingly.
Conversation Memory
Context persists across sessions. The agent remembers what was discussed, what actions were taken, and what the patient or staff member was trying to accomplish -- even across channel switches.
Cost Budgets
Per-agent cost controls prevent runaway LLM spending. Set token budgets, monitor consumption, and get alerts before agents exceed their allocation. Enterprise cost governance built into the platform.
Complete Audit Trail
Every AI interaction is fully logged: what was asked, what the LLM proposed, what the policy layer approved or rejected, what executed, and what resulted. Complete traceability from prompt to patient action.
Agent Builder: No-Code Agent Creation
Hospitals can build their own agents without an engineering sprint. Define the behavior, select the capabilities, deploy to any channel.
Define Behavior
Plain language instructions
Write the agent's instructions in plain language. Include clinical context, safety rules, and persona. Version-controlled and auditable.
Select Capabilities
Schema-generated tools
Choose from auto-generated tools based on your entity schemas. Test each capability independently before wiring it into the agent.
Deploy & Monitor
Any channel
Deploy to any mode: chat, voice, WhatsApp, or autonomous background. Monitor via complete audit trail and action history.
Four Deployment Modes, One Runtime
Same agent. Same tools. Same safety rules. Whether it's a live chat, a phone call, a WhatsApp thread, or a background job running overnight.
Chat
AI embedded directly in the CRM. Agents read, write, and navigate patient records alongside the staff. Tool results rendered as interactive UI components, not just text.
Specialist agents with intelligent routing -- the right agent handles the right query.
Voice
Patients call. The AI picks up. Natural conversations in Hindi and English. Books appointments, answers questions about lab results, escalates emergencies to human operators.
Real-time speech processing with natural turn-taking. No awkward pauses.
Patients message on WhatsApp. The AI responds with full clinical context -- appointment booking, lab results, medication refills. Safety guardrails on every interaction.
Never diagnoses. Always escalates emergencies. Messaging compliance built in.
Autonomous
Agents that run without human interaction. Nightly care gap analysis, follow-up prioritization, data quality monitoring. Staff arrives in the morning to a prepared brief with recommended actions.
Scheduled and event-driven execution with reliability guarantees.
Safety at Every Layer
Healthcare AI needs predictability, auditability, and fail-safe behavior. An AI that is right 95% of the time and wrong 5% of the time is dangerous in a clinical setting.
Topic Boundaries
Scope enforcement
Each agent has a defined scope. Ask it something outside that scope and it redirects or escalates. No creative improvisation on clinical topics.
Injection Protection
Input sanitization
Every input is sanitized against prompt injection attacks before reaching the LLM. Adversarial inputs are detected and blocked automatically.
Output Validation
Schema-enforced
Every LLM output is validated against strict schemas before execution. Malformed or out-of-bounds responses are rejected and retried within constraints.
Clinical Grounding
Verified protocols
All clinical responses are grounded in verified protocols and pre-computed metrics. System prompts are version-controlled and clinician-reviewed. Never hallucinated from training data.
See AI in Action. Not on a Slide.
Cohort building, patient messaging, autonomous care gap analysis -- all running on real clinical data. Watch the agents work together.