Workflow in scope
One client-facing agent or RAG workflow that the agency expects to operate or reproduce.
AI reliability for IT, Marketing & Sales Agencies
Make your agency AI safe enough to launch.
Your team can build AI demos quickly, but client projects stall when integrations, testing, and production controls are missing. We test failures, add controls, and give you launch evidence.
Repeatable delivery without hidden production risk · $4,500 sprint · One defined workflow
Direct answer
IT, Marketing & Sales Agencies · AI Reliability Guardrails
What does this engagement mean for agency teams?
Agencies need repeatable evaluation and release controls before they can responsibly ship similar AI features across clients. Reliability work should expose which behaviours are shared, which are client-specific, and how regressions are caught before an update reaches production.
Likely system boundaries
- Shared evaluation harness
- Client-specific datasets and policies
- Tracing, release, and support workflow
Evidence required
- Separate platform regressions from client-data problems
- Test permission and retrieval boundaries for each tenant
- Define rollback, escalation, and ownership after handover
Important boundary
The sprint hardens one reusable delivery pattern. Each materially different client workflow still needs its own acceptance criteria and evidence.
Who this is for
For product and engineering leaders who cannot keep shipping on hope.
Best for Seed to Series B IT, Marketing & Sales Agencies teams—where an AI feature exists, but deployment is frozen over hallucination risk, compliance exposure, or reputation damage.
- 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
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
Frequently Asked Questions