Use Cases
Every production AI system
needs a runtime.
Whether you are building customer support agents, healthcare AI, compliance workflows, or RAG platforms — production requirements converge on the same infrastructure needs. ElectriPy provides them.
Customer Support Agents
B2C · High VolumeProduction customer support agents require reliability under high request volume, session visibility, policy enforcement on sensitive data, and cost tracking across interactions.
Implementation Pattern
Circuit breakers and fallback routing prevent provider failures from surfacing to customers. Traces capture every tool call and decision. Policy gates enforce data access rules. Cost metadata enables per-interaction attribution.
Healthcare AI Systems
Healthcare · RegulatedHealthcare AI operates under strict compliance requirements. Every decision must be traceable, every action governed, and every piece of PHI handled with documented redaction.
Implementation Pattern
PolicyEngine gates high-risk actions and captures structured audit trails. DefaultRedactor automatically handles PHI in traces. Approval workflows require human review for consequential outputs.
Internal Copilots
Enterprise · Internal ToolsInternal AI tools serve diverse teams with varying permissions. Routing, skill management, and cost attribution per team or department require runtime infrastructure.
Implementation Pattern
WorkloadRouter directs requests to appropriate models based on capability and budget. Skills packaging manages reusable instruction assets. Cost metadata enables per-team attribution.
Compliance Workflows
Finance · Legal · RegulatedWorkflows that produce compliance-relevant outputs require evidence tracking, approval gates, audit trails, and violation logging — not ad hoc guardrails.
Implementation Pattern
PolicyGateway with evidence requirements ensures outputs meet compliance standards. AuditLogger produces structured records. ViolationTracker identifies patterns for review.
Knowledge Systems
Enterprise · RAG · SearchRAG and knowledge systems need retrieval quality measurement, regression prevention, and structured evaluation before and after every deployment.
Implementation Pattern
EvalService with RetrievalPrecisionScorer and AnswerCorrectnessScorer provides quantitative quality gates. CI integration catches regressions before they reach production.
Tool-Augmented Agents
AI Product · AutomationAgents that use tools require reliable tool integration, observability into tool calls, and policy enforcement on what tools can do — not just what models say.
Implementation Pattern
MCPClient and MCPServer provide typed tool integration with auth hooks and capability discovery. Span decorators capture tool call latency and results with full trace context.
Multi-Agent Systems
AI Product · Complex WorkflowsMulti-agent architectures require routing between agents, shared observability context, and governance across agent boundaries — not just within a single agent.
Implementation Pattern
WorkloadRouter handles inter-agent routing. Distributed trace context propagates across agent calls. PolicyEngine enforces behavioral boundaries between agent capabilities.
LLM Gateways
Platform · InfrastructureCentralizing LLM access requires provider abstraction, cost tracking, rate limiting, and routing based on model capability and latency — a runtime problem, not an application problem.
Implementation Pattern
LLMGateway with ProviderAdapter enables multi-provider routing. RateLimiter and CircuitBreaker prevent cascade failures. Token and cost metadata enables billing attribution.
RAG Platforms
AI Product · DataRAG pipelines require continuous quality measurement, retrieval scoring, regression prevention, and observability into both retrieval and generation quality.
Implementation Pattern
EvalService with retrieval and generation scorers provides systematic quality tracking. OpenTelemetry export enables integration with existing monitoring stacks.
AI Product Teams
Product · EngineeringTeams building AI products need a shared runtime foundation that is observable, governable, and testable — so they can ship reliably rather than rebuild infrastructure for every product.
Implementation Pattern
The full runtime stack gives teams consistent infrastructure across products. Incremental adoption lets teams start with one capability and expand as their systems mature.
Start Today
Your use case needs a runtime.
Start with the capability your system needs most. Adopt the rest as your production requirements evolve.