What counts as an AI Reliability Sprint?

A focused engagement where we map your AI failure modes, build a scoped evaluation plan, and implement guardrails, automated fallbacks, and recovery controls.

What is not included in the AI Reliability Sprint?

We do not build a generic agent from scratch. The sprint is scoped strictly against accepted production risks for a workflow that already exists but cannot safely launch.

Do you offer ongoing retainers for AI observability?

Yes. After the initial $4,500 reliability sprint, we offer an optional $1,500/mo retainer for ongoing observability, eval refresh, and controlled tweaks.

AI reliability sprint

Make one AI workflow ready for a release your team can defend.

For one agent or RAG workflow that works in a demo but cannot yet pass a defensible release review. We inspect the failure paths, add the controls that matter, and leave a practical handover.

Scope the Reliability Sprint Compare offers

$4,500 reliability sprint · Optional $1,500/mo observability retainer · One defined workflow

Representative release gate

Production readiness profile

Conditional

Release decision

Controls required before approval

High-impact path

A failed tool call must have an intentional retry, fallback, or handoff.

Evaluate

Test set

Control

Approval

Recover

Owner

Representative preview - evidence is built from your workflow

Who this is for

For product and engineering leaders who cannot keep shipping on hope.

Best for Seed to Series B teams-especially in regulated or high-trust industries-where an AI feature exists, but deployment is frozen over hallucination risk, compliance exposure, or reputation damage. This is strongest when one workflow is blocked, not when the entire AI strategy is undefined.

The agent hallucinates, loops, or takes unpredictable actions under real data.
Leadership will not approve a launch because nobody can prove the system is safe.
Tool calls fail, duplicate work, or leave the workflow stuck with no recovery path.
You have logs or traces, but no clear evaluation set or release decision.
Prompt changes create regressions you only notice after users complain.
You are in fintech, healthtech, insurtech, or legaltech and compliance risk is real.

What changes in the sprint

BeforeAfter

“It seems better after the prompt change.”

Representative eval cases and an explicit go / no-go release decision

Failure shows up as a support ticket

Traces, failure classification, alerts, and defined recovery behaviour

AI takes a high-impact action with weak controls

Approval gates, permission boundaries, and clear escalation

Tool or API errors leave the workflow stranded

Retry, fallback, or human handoff-chosen on purpose

Representative release decision

Workflow readiness record

Hold

Current decision

Hold until recovery behaviour is defined

Evaluation coverage: Critical paths exercised; Evidence
Recovery behaviour: Fallback or handoff; Required
Release accountability: Decision owner named; Owner

Representative preview - the final decision reflects evidence from your workflow

What is included

One workflow architecture map and failure-mode inventory
A scoped evaluation plan and representative test set
Observability or tracing improvements so failures are diagnosable
Guardrails, approval points, retries, fallbacks, or recovery controls in agreed scope
Regression checks for the critical paths that matter most
Handover: implementation notes, operating guidance, known limits, and next priorities

Pricing shape

$4,500

Reliability sprint: map failures, add the controls that matter, and produce release evidence for one defined workflow.

$1,500 / month

Optional retainer for ongoing observability, eval refresh, and controlled tweaks after the sprint. Only when it is useful-not as hidden scope.

Days 1–3 - Inspect the workflow, rank risks, lock definition of done

Days 4–10 - Build agreed guardrails, evals, and recovery behaviour

Days 11–14 - Regression review, release decision, handover

Industry applications