Production AI infrastructure for teams that ship.
Infrand Platform unifies tracing, evals, guardrails, governance, and cost control so you can move fast without losing control.
Capture prompts, tool calls, retrieval, and model outputs—then debug failures in minutes.
Turn quality and safety checks into scorecards that gate changes and monitor drift.
Enforce policies, budgets, and escalation paths so production behavior is predictable.
Modules that cover the full lifecycle.
6 modules, one workflow. Adopt incrementally, expand as your system grows.
End-to-end lineage for AI interactions.
- •Prompt + response lineage with metadata
- •Tool-call timing breakdowns and error attribution
- •Retrieval observability (query, top‑K, document IDs, citations)
Quality gates you can ship with.
- •Dataset and rubric versioning
- •Scorecards for quality, safety, and task success
- •CI integration and release gating
Runtime policies, budgets, and safety controls.
- •Policy engine for allow/deny and transformations
- •Budget caps and rate limits by team/route/env
- •Routing rules and fallbacks for degradation modes
Provenance for prompts, models, and configs.
- •Versioned prompt registry with diffs and approvals
- •Model catalog with provider settings and constraints
- •Environment promotions (dev → stage → prod)
Attribution, budgets, and cost/latency control.
- •Cost attribution by team, route, and environment
- •Latency SLOs and tail-latency breakdowns
- •Budgets and alerts for spend/tokens/requests
Auditability for critical actions.
- •Audit logs for privileged actions and policy changes
- •Evidence export for reviews (selected artifacts)
- •Access visibility (who changed what, when, and why)
Trace → Evaluate → Control
Infrand Platform is built around an operational loop: capture reality, measure changes, then enforce controls.
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.
From prototype to production, without losing control.
Infrand gives platform teams the guardrails they need—while letting product teams move fast.
- •RBAC, audit logs, and environment isolation
- •Cost budgets, latency SLOs, and alerts
- •Prompt + model provenance with promotion workflows
Built for modern AI systems
RAG and agentic workflows introduce new failure modes. Infrand makes them observable and governable.
Trace retrieval inputs/outputs, evaluate answer quality, and enforce citation policies across environments.
Track multi-step tool calls, timeouts, retries, and failure modes with end-to-end lineage.
Apply runtime guardrails and escalation for high-risk requests, with evidence and audit trails.
Compare providers and models using eval scorecards and cost/latency attribution before switching.
Provider-agnostic by design
Infrand integrates by instrumenting your application, not by replacing your stack.
- •OpenAI, Anthropic, Google, Azure, AWS Bedrock (via your application)
- •Vector stores and retrieval layers (captured through trace metadata)
- •CI/CD pipelines for gated releases
- •SSO and identity providers (Team/Enterprise)
Move fast without losing control
Infrand is designed to make AI systems measurable and controllable, so production doesn’t become guesswork.
Explain failures with end-to-end lineage across prompts, retrieval, tools, and model outputs.
Ship prompt/model changes behind eval gates, then monitor drift after release.
Enforce runtime budgets and policies so unsafe outputs and runaway costs are contained.
Link runtime behavior back to approved versions of prompts, policies, and configuration.
Avoid fragmented tooling
Teams often start with logs and scripts. The problem is consistency, governance, and operational control across routes.
You get fragments: logs and dashboards, but no lineage, governance, or consistent controls across routes.
You get ad-hoc checks, but not versioned scorecards tied to releases and drift monitoring in production.
Policies live in code paths and differ per service—hard to audit, hard to change safely.
FAQ
Yes. Infrand is provider-agnostic and designed for multi-model routing and experimentation.
Trace is one module. Infrand Platform also includes evals, runtime guardrails, governance, and cost control.
Enterprise deployments can support stricter isolation requirements and custom data-handling constraints.
You control capture, redaction, and retention policies. Sensitive fields can be filtered at ingestion.
Infrand Evals runs repeatable suites in CI and production to catch quality and safety drift before and after release.
Yes. Tracing and guardrails are designed for multi-step runs with tool-call timing and failure analysis.
Instrument one production route, capture traces, run a baseline eval suite, then expand coverage.
Infrand Cost attributes spend and latency per route and team, and ties changes back to releases and configs.