AI WORKFLOW FIX.

    Fix your broken AI workflows

    This is for you if...

    You spent the money. The AI never made it to production.

    It worked in the demo. The first week in production it started failing and nobody knew why.

    The person who built it left. Now it is a black box that occasionally works.

    You are paying for tools and subscriptions that are not producing measurable output.

    You have rebuilt the same workflow twice already. You do not want to do it a third time.

    DELIVERABLES
    Failure diagnosis report
    One rebuilt AI workflow
    Monitoring layer
    Maintenance documentation
    AI opportunity map

    A precise breakdown of why the implementation failed, within five business days.

    Fixed from the root cause, not the symptom.

    So the team sees failures before clients do.

    So any team member can maintain the workflow.

    A prioritised roadmap for what to build next.

    Failure diagnosis report
    Data schema changed silentlyData qualityHIGH
    No output validation layerMonitoringHIGH
    Prompt hardcoded, not versionedEngineeringMED
    No error handling on failureEngineeringMED
    One rebuilt AI workflow
    Before
    Demo passed, prod failed
    Schema drift undetected
    No monitoring
    Built by contractor, left
    root cause
    No prod
    hardening
    After
    Validated on real data
    Schema change detection
    Alerts on every failure
    Docs any dev can follow
    Monitoring layer
    14:02:11workflow.startedinput validated
    14:02:45workflow.completedoutput quality: 94%
    14:03:07workflow.degradedconfidence: 0.62
    14:03:19alert.fired → Slackon-call notified
    Maintenance documentation
    SectionOwnerUpdateDone
    Architecture overviewEngineeringOn change
    Data schema referenceData teamMonthly
    Prompt changelogAI teamOn change
    Incident runbookDevOpsQuarterly
    AI opportunity map
    InitiativeEffortImpactWhen
    Automate proposal generationLOWHIGH
    NOW
    CRM data enrichmentMEDHIGH
    NOW
    Customer churn modelHIGHHIGH
    NEXT
    Support deflection botLOWMED
    NEXT
    CASE STUDY

    It works.

    Pre-seed startup // content generation

    A pre-seed startup building a content generation platform had a convergence problem. Their AI was producing content, but every piece looked like the last. Context was built with fixed one-way prompts, so the system had no awareness of what it had already made. Users needed variety. We rearchitected the context layer into simple loops that tracked prior output per topic, then rewrote the prompts with the founder's own knowledge and voice baked in. The content started actually varying.

    Key Results

    • 150+ pieces of content generated per week
    • Content costs cut by 90%+
    • Millions of viewers reached monthly
    FAQ

    Frequently Asked Questions

    GET STARTED

    CONTACT US

    Value Iteration