Solutions
Common production AI problems, mapped to a clear workflow: trace → evaluate → control.
RAG reliability
Typical production pain → platform workflow
Problem
Retrieval changes silently. Users see inconsistent answers, missing citations, and hard-to-reproduce failures.
Recommended modules
Infrand TraceInfrand EvalsInfrand Guardrails
How Infrand helps
- •Trace retrieval queries, top-K, document IDs, and citations alongside prompts and outputs
- •Evaluate groundedness and citation coverage with scorecards
- •Enforce citation policies and safe fallbacks at runtime
Agentic workflows
Typical production pain → platform workflow
Problem
Multi-step runs fail in the gaps: tool timeouts, retries, partial completions, and ambiguous state.
Recommended modules
Infrand TraceInfrand EvalsInfrand GuardrailsInfrand Cost
How Infrand helps
- •Trace tool-call timing, errors, and step-level lineage across a run
- •Evaluate task success and safety with regression suites
- •Control routing, retries, and budgets to reduce incident blast radius
Customer-facing AI
Typical production pain → platform workflow
Problem
You need consistent policies for high-risk requests, safe escalation paths, and evidence for investigations.
Recommended modules
Infrand GuardrailsInfrand RegistryInfrand Audit
How Infrand helps
- •Enforce guardrails and escalation paths across routes and environments
- •Maintain provenance of prompt/policy changes for incident response
- •Audit critical actions and share evidence for reviews
Model migrations and routing
Typical production pain → platform workflow
Problem
Switching providers/models changes behavior and cost. Teams need measurable tradeoffs before moving traffic.
Recommended modules
Infrand EvalsInfrand CostInfrand GuardrailsInfrand Trace
How Infrand helps
- •Compare changes with side-by-side eval scorecards
- •Attribute cost and latency by route and provider
- •Control routing and fallbacks with budgets and policies
Outcomes
What teams get after adopting Infrand
Infrand is designed to make AI development measurable and production operations predictable.
- •Reduce time-to-debug by making failures explainable with lineage
- •Prevent regressions with measurable gates and drift monitoring
- •Control spend and tail latency with budgets tied to routes and releases
- •Strengthen governance with provenance and auditability