AI Service Router
Match intake requirements to the right AI service path.
How to route an AI request
Start with intent, then add constraints: data sensitivity, latency target, compliance scope, and fallback behavior. If the request is customer-facing and time-sensitive, prefer smaller, guarded models with caching. If the request is internal and analytical, prefer extraction and reasoning models with audit logging.
Support and operations
Use assistants, knowledge-base retrieval, and ticket routing when volume is high and response time matters.
See services →Analytics and decisioning
Use structured extraction, forecasting, and dashboard automation when the goal is faster decisions from messy data.
See services →Security and compliance
Use policy-grounded models and audit-ready logging when data is confidential or regulated.
See services →Low-latency customer experience
Use small specialized models, caching, and guardrails when users expect sub-second responses.
See services →Not sure which path fits?
Tell us your use case and constraints. We will return a short list of best-fit services with estimated ROI and timeline.