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Infrand

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