Product
Instrument once, then trace, evaluate, and control every AI interaction—across dev, staging, and production.
Capture prompts, tool calls, retrieval, and model outputs with timing, metadata, and search—built for debugging and incident response.
- • Prompt + response lineage with metadata
- • Tool-call timing breakdowns and error attribution
- • Retrieval observability (query, top‑K, document IDs, citations)
Turn AI quality into an engineering discipline with versioned datasets, scorecards, CI gating, and drift monitoring in production.
- • Dataset and rubric versioning
- • Scorecards for quality, safety, and task success
- • CI integration and release gating
Enforce consistent policies and constraints at runtime—so risky requests, unsafe outputs, and budget blowups are handled predictably.
- • Policy engine for allow/deny and transformations
- • Budget caps and rate limits by team/route/env
- • Routing rules and fallbacks for degradation modes
Designed for modern AI systems
Infrand integrates by instrumenting your application. It captures the full AI interaction graph, runs evaluations, and enforces runtime policies—without requiring you to rewrite your stack.
Capture traces, metadata, and events for prompts, retrieval, tool calls, and outputs.
Manage policies, budgets, datasets, and releases with provenance and approvals.
Apply guardrails, routing, and cost controls consistently across environments.
Trace → Evaluate → Control
Product teams move fast; platform teams keep systems stable. Infrand connects the two: every change is measurable, explainable, and reviewable.
Add a lightweight SDK to capture prompts, retrieval, tool calls, and outputs with consistent metadata.
Understand failures with end-to-end lineage, timing, and searchable traces across multi-step runs.
Gate changes with scorecards in CI and detect drift after release using production monitoring.
Enforce runtime policies, budgets, and routing so production behavior remains predictable.
Modules
Adopt one module, then expand. Each module is designed to work standalone and as part of Infrand Platform.
Infrand Trace
Capture prompts, tool calls, retrieval, and model outputs with timing, metadata, and search—built for debugging and incident response.
- •Prompt + response lineage with metadata
- •Tool-call timing breakdowns and error attribution
- •Retrieval observability (query, top‑K, document IDs, citations)
- •Session reconstruction across multi-step runs
- •Sampling controls per environment and route
- •Redaction hooks for sensitive fields
- •Debugging regressions after prompt/model changes
- •Explaining “why did the model say that?”
- •Tracing agent workflows with tool calls
Infrand Evals
Turn AI quality into an engineering discipline with versioned datasets, scorecards, CI gating, and drift monitoring in production.
- •Dataset and rubric versioning
- •Scorecards for quality, safety, and task success
- •CI integration and release gating
- •Side-by-side comparisons for model/prompt changes
- •Human review workflows for ambiguous cases
- •Production drift monitoring and alerts
- •Preventing regressions before deployment
- •Measuring model/provider tradeoffs
- •Establishing defensible safety checks
Infrand Guardrails
Enforce consistent policies and constraints at runtime—so risky requests, unsafe outputs, and budget blowups are handled predictably.
- •Policy engine for allow/deny and transformations
- •Budget caps and rate limits by team/route/env
- •Routing rules and fallbacks for degradation modes
- •Output filtering and sensitive-data handling flows
- •Escalation paths for high-risk interactions
- •Audited “break-glass” workflows
- •Keeping production behavior inside policy
- •Reducing incident impact and blast radius
- •Controlling spend and tail latency
Infrand Registry
Ship changes with traceable versions, approvals, and rollbacks—link runtime behavior back to exactly what was deployed.
- •Versioned prompt registry with diffs and approvals
- •Model catalog with provider settings and constraints
- •Environment promotions (dev → stage → prod)
- •Change history with owners and timestamps
- •Rollback support with clear provenance
- •Links from traces to config versions
- •Platform governance and safer releases
- •Incident response and postmortems
- •Reducing “unknown change” failures
Infrand Cost
Make spend and latency visible down to routes and teams, then enforce budgets with alerting tied to releases.
- •Cost attribution by team, route, and environment
- •Latency SLOs and tail-latency breakdowns
- •Budgets and alerts for spend/tokens/requests
- •Provider comparison for cost/perf tradeoffs
- •Correlation to releases and config changes
- •Usage insights for capacity planning
- •Preventing surprise bills
- •Managing tail latency in production
- •Scaling AI usage across teams
Infrand Audit
Built-in audit logs and evidence workflows to support security reviews and incident investigations.
- •Audit logs for privileged actions and policy changes
- •Evidence export for reviews (selected artifacts)
- •Access visibility (who changed what, when, and why)
- •Environment isolation patterns
- •SSO integration options (SAML/OIDC)
- •Vendor security reviews
- •Compliance-oriented teams
- •Reducing time-to-answer during incidents
Start small. Expand safely.
Most teams begin with Trace on one critical route, then add eval gates and runtime controls as coverage expands.
- 1.Instrument a production route and capture end-to-end traces.
- 2.Build a baseline eval suite and gate prompt/model changes in CI.
- 3.Add guardrails and budgets for predictable production behavior.
- 4.Introduce provenance and promotions to strengthen governance.