Nine out of ten AI workflows die in 30 days — not because the model failed, but because nobody noticed it had quietly stopped working. We come in for a fixed-price independent audit of any agent, automation or AI copilot you run, score it against a three-defence reliability checklist, and tell you which ones to fix, which ones to retire, and which ones are quietly working better than you realised.
Every dead AI workflow we’ve been called in to autopsy followed the same five-step pattern. The middle three steps are where the audit lives — that’s where the failure was visible if anyone had been looking.
Workflow goes live. Initial output is brilliant. Everyone’s impressed.
Something changes silently. A list shrinks, a source moves, a model updates.
Output is technically a response but substantively useless. No-one notices.
A customer or stakeholder notices something off. Awkward conversation.
Workflow killed. “AI doesn’t really work for our business.”
The painful part: it’s almost never the model’s fault. The model did exactly what it was told. What broke was the workflow around it — missing canary fields, no silent-failure alerts, no weekly check that caught the drift. An audit catches all three before they kill the workflow.
This is the standard we benchmark against. It’s the same checklist Launchpad builds every agent against — we’re publishing it because the framing is more useful in the market than as a private spec.
Every run produces a verifiable field — timestamp of the most recent source, count of items processed, hash of the input. If the canary stops moving, something has changed upstream. Without this, silent failure is invisible until a customer hits it.
If the workflow finds nothing to do, it raises an alert — not an empty output. Empty outputs that look fine are the most dangerous failure mode in AI. The alert routes to a human inside the same hour the run completes.
Four minutes a week. A human reads one full output end-to-end. The canary catches structural drift; the spot-check catches tone, voice and judgement drift — the qualitative failure mode the metrics will never surface.
The deliverable is a written report, not a meeting. You can read it, share it, hand it to your in-house team or another supplier, and act on it. We’ll happily implement the fixes ourselves, but the audit deliberately stands alone so you’re not buying us; you’re buying clarity.
For each workflow: what it watches, reads, produces, won’t do, and how you know it worked. Most workflows fail at the “won’t do” line.
Where canary fields exist, where they don’t, and what each canary should actually monitor.
The specific ways your workflow could fail silently, scored by likelihood and blast radius. Each scenario gets a named owner inside the business.
If the workflow has an eval suite, we score it. If it doesn’t, we tell you the 20–50 examples it needs and where to get them.
Is the workflow running on a laptop? A cron on someone’s machine? A VPS with auto-restart? Each comes with a survival rating.
Token cost per run, latency p50 + p95, monthly bill projection. Catches the "we’re spending what?" surprise before it’s a surprise.
UK GDPR + DPA 2018 alignment, data-classification accuracy, retention policy. Per-engagement DPA available.
Every finding sized as quick-win, build, or rebuild. Quick-wins ranked first — usually 60–80% of the value at 10–20% of the cost.
One workflow, a small estate, or a full sweep across the business. All prices exclude VAT, all are fixed-price — if it takes longer than scoped, that’s our problem, not yours.
Single Workflow
For one named workflow. The whole 3-defence checklist plus the 8-item written audit. Most useful for confirming a single high-value workflow is safe to scale or hand off.
Multi-Workflow
For a small portfolio of AI workflows — usually a mix of agents, automations and copilots. Comparative scoring lets you see which ones earn their keep and which ones to retire.
Estate Audit
For businesses with five or more AI workflows live. Full estate audit with a risk matrix, dependency map, and a recommendation on which workflows to consolidate or retire entirely.
The audit is deliberately independent. If we built the workflow you want audited, we’ll bring in a second pair of eyes — or we’ll point you to another supplier we trust. The point of the audit is impartial assurance; we won’t mark our own homework.
We approach AI workflow audits the same way our team has approached system audits in the NHS, Police and MOD for the last two decades — with the documentation and the accountability trail to match. 72+ years of combined regulated-environment experience, including SC-cleared personnel.
Yes — that’s the most common reason people book this. Most audits we do are of workflows built by an in-house team, a freelancer, or another agency. The audit is deliberately vendor-neutral. You hand us read access to the workflow, the inputs, and the outputs; we read the code or the configuration; we score it against the same checklist whether the original builder was us, you, or someone else.
That’s a good outcome. You get a written assurance you can put in front of a board, a customer, or an auditor. "We audited this in May 2026 and it scored 28/32" is a much stronger statement than "we think it’s probably fine." If the audit finds the workflow is well-built, the report says so. We don’t manufacture findings to justify the fee.
A code review checks whether the code works. A reliability audit checks whether the workflow will keep working in three months without anyone watching it. The two overlap, but the audit is much more focused on the operational layer: canary fields, alerting, eval coverage, infrastructure resilience, cost trajectory, compliance posture. A code review will tell you the function is correct. The audit will tell you whether the function will still be doing what you think it’s doing on day 90.
Yes — for SaaS-based workflows (Zapier, Make, Power Automate, OpenAI Assistants, custom GPTs) we audit the configuration, the inputs, and the outputs without needing source. For workflows built around a hosted LLM with a custom harness, we’ll need read access to the harness code. We’ll always tell you exactly what access we need before we quote.
The report describes what we find. If your supplier built a good workflow, the report says so. If they built a fragile one, the report says that too. We frame every finding as a structural issue, not a personal one — "this workflow has no canary field" rather than "the supplier missed canary fields." Most suppliers we’ve worked alongside actually welcome an independent reliability audit; it gives them a clear roadmap of what to fix without an internal argument about scope.
Yes, but it’s genuinely your choice. Most audits hand off cleanly to whoever owns the workflow — in-house team, original supplier, or a new one. If you’d like us to implement the fixes, that’s a separate engagement: usually Launchpad Agent if we’re rebuilding the workflow, or a fixed-price remediation package if the workflow stays in place and we’re adding the missing defences.
For most SME-grade compliance asks: yes. The report is written to a standard you can hand directly to a cyber-insurance underwriter, an ISO auditor, or a procurement team asking how you assure your AI workflows. For regulated sectors (financial services, NHS, central government, MOD-adjacent) the audit runs under a per-project DPA and the lead is SC-cleared. We’ll match the level of formality to the audience.
Book a 30-minute scoping call. We’ll walk through your live workflows, agree which tier fits, and quote you a fixed price — same call.
Book a scoping call