Discovery Sprint → Implementation Sprint → Managed Ops

How we take AI from “interesting” to “operational”

Most “AI projects” fail for one of three reasons: vague goals, messy reality, or a charming lack of guardrails.
Our service is built to avoid all three.

You choose the level you need:

1. Discovery Sprint — decide what to build and why
2. Implementation Sprint — build it properly and deploy it
3. Managed Ops — keep it working, safe, and improving

Discovery Sprint

A short, paid engagement where we turn “we want to use AI” into a clear, testable plan that won’t collapse the moment it meets real operations.

What it’s for

• Identify where AI can actually reduce workload or improve throughput (not “add a chatbot for vibes”).
• Map the process end-to-end (inputs → decisions → outputs).
• Decide what should be automated, what must stay human, and where the risks live.
• Produce a delivery plan with scope, costs, and measurable outcomes.

What you get

• Use-case shortlist ranked by ROI, feasibility, and risk
• Process map (current vs improved flow)
• Data & systems inventory (what you have, what’s missing, what connects to what)
• Guardrails (permissions, approvals, audit logs, fail-safes)
• Implementation plan with milestones and a realistic estimate
• Prototype where it makes sense (a small proof of concept)

What we need from you

• Access to the right people for a few focused sessions
• Visibility of your tools (CRM, email, spreadsheets, WhatsApp flow, forms, etc.)
• A clear definition of “done” (time saved, fewer errors, faster response, fewer follow-ups)

Outcome

A plan you can execute confidently—either with us, your team, or another provider. No lock-in. No smoke.

Implementation Sprint

This is where the plan becomes a working system: workflows, integrations, guardrails, and deployment.
In other words: less theory, more results.

What it’s for

• Turn the chosen use case(s) into a live workflow
• Connect your real tools (CRM, email, forms, spreadsheets, databases, internal apps)
• Build in controls so it’s safe: approvals, permissions, logging, fallbacks
• Make it usable for humans who do not have time to “babysit AI”

What we build

• Workflow orchestration (steps, decisions, hand-offs, exceptions)
• Integrations (APIs/webhooks/connectors where appropriate)
• Data validation (clean inputs, structured outputs, error handling)
• Guardrails (access control, approval gates, audit logs, escalation rules)
• Interfaces if needed (simple dashboards, forms, or a chat front-end)
• Documentation (how it works, what breaks it, and what to do when it does)

What we need from you

• A point of contact who can make decisions quickly
• Access to the systems involved (or someone who can provide it)
• Real example cases (sanitised is fine) so we test reality, not fantasy
• Agreement on success metrics (speed, accuracy, time saved, error reduction)

Outcome

A production-ready AI workflow that is integrated with your tools and controlled by your rules—so it saves time without introducing “surprise creativity”.

Managed Ops

An ongoing service where we run, maintain, improve, and govern your AI system after it goes live—because reality changes, integrations drift, and yesterday’s “perfect prompt” becomes today’s liability.

What it’s for

• Keep workflows reliable and secure over time
• Prevent silent failures and “it worked last month” surprises
• Continuously improve performance as your business and processes evolve

What’s included

• Monitoring & issue handling (errors, integrations, performance drift)
• Workflow tuning (fewer steps, better accuracy, smoother hand-offs)
• Prompt/tool updates when policies, products, or processes change
• Safety & compliance checks (permissions, logs, data handling practices)
• Reporting on usage, outcomes, and improvements
• Change requests within an agreed monthly allowance (bigger changes scoped separately)

What it’s not

• Unlimited scope disguised as a monthly retainer
• Random feature building without priorities
• A substitute for your internal decisions (we automate your rules; we don’t invent them)

Outcome

Your AI doesn’t become a neglected gadget. It becomes a maintained operational capability—stable, auditable, and improving month by month.

Which one do you need?

If you want clarity before spending money: Discovery Sprint.
If you already know what to build: Implementation Sprint.
If you want it to stay reliable after launch: Managed Ops.

And if you want the honest version: most businesses start with Discovery, then do Implementation, then keep Managed Ops at a sensible level so the system doesn’t quietly rot in the corner.