HoverBot Technical Overview
Architecture, data flow, and service-level objectives for production chatbot deployments.
System architecture at a glance
HoverBot uses a policy-first architecture: requests pass compliance and safety gates before model inference, then flow through validation and analytics loops.
Architecture layers
Layer 1
Ingress and policy gate
Requests enter through channel adapters, rate limiting, tenant policy checks, and auth controls.
Layer 2
PII and safety preprocessing
Sensitive entities are detected and redacted before retrieval and generation when policies are enabled.
Layer 3
Retrieval and context assembly
Grounded documents are selected and scored using intent and confidence-aware routing.
Layer 4
Model routing and generation
Conversation requests route to configured model tiers based on complexity and latency targets.
Layer 5
Validation and escalation
Responses pass compliance checks and can escalate to humans on low confidence or safety triggers.
Layer 6
Audit and analytics loop
All critical events are logged to support incident response and weekly optimization cycles.
Data flow
- User message -> channel adapter -> tenant auth and policy checks
- PII classifier -> redaction policy -> safe payload assembly
- Retriever -> ranked context pack -> prompt builder
- Model router -> generation tier selection -> response candidate
- Response validator -> compliance checks -> escalation if needed
- Audit log + analytics events -> weekly optimization backlog
Operational targets
| Target | Objective | Notes |
|---|---|---|
| End-user response latency (p95) | < 2.5s | Varies by channel network and retrieval depth |
| Production availability | 99.9% monthly target | SLA commitments available on enterprise contracts |
| Sustained request throughput | 120 req/s per region target | Autoscaling profile tuned per tenant tier |
Targets are published SLO objectives and reviewed during monthly reliability operations.
Modeling and control notes
- Retrieval-augmented generation is used for grounded domain responses.
- Confidence-based model routing balances latency, cost, and response quality.
- Deterministic fallback and escalation paths are used for sensitive intents.
- Closed-loop review process uses unresolved conversations to improve weekly releases.