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Alphacroft

Services / Intelligence · CAP.007

An AI plan with numbers, not vibes.

A short engagement that maps your workflows against what current AI can reliably do, and returns a prioritized, costed roadmap with success metrics your CFO will accept.

1-2 weeksSenior AI engineerFixed price, credited against a build

01 · When you need this

If any of these sound familiar, you’re in the right place.

  • The board keeps asking about AI and you need an answer that isn't a shrug or a moonshot.
  • You tried a chatbot pilot; it embarrassed itself and the budget.
  • Competitors claim AI features and you can't tell marketing from substance.
  • Your team drowns in a repetitive task that feels automatable; you want to know if it actually is.

02 · What we deliver

Exactly what you walk away with.

No vague 'solutions'. These are the concrete deliverables: each one is a line in the proposal and a checkbox at handover.

DEL.01

Workflow audit

Your processes ranked by automation potential, with the 'looks automatable, isn't' traps flagged.

DEL.02

Feasibility verdicts

For each candidate: what today's models genuinely deliver, error rates included.

DEL.03

Costed roadmap

Build cost, running cost, and expected saving per use case. Sequenced.

DEL.04

Success metrics

What you'll measure so an AI project can fail honestly instead of limping forever.

DEL.05

Risk & governance notes

Data privacy, failure modes, and what a human must still check.

03 · How it runs

Phase by phase, with nothing hidden.

Every phase states what happens and what you see in your project portal while it does.

01

Immersion

2-3 days

We watch the actual work, where the hours actually go, not where everyone assumes.

In your portal: Observation notes.

02

Feasibility testing

3-5 days

Quick technical probes with your real data samples, evidence, not benchmarks from a blog.

In your portal: Probe results, including the failures.

03

Roadmap

2-3 days

The costed, sequenced plan with metrics, and the board-ready summary.

In your portal: Final report and walkthrough.

04 · Standards

Non-negotiables, in writing.

'No' is a valid finding

If AI won't beat a checklist or a €100/month tool for your case, the report says so, in bold.

Tested on your data

Feasibility claims come from probes on your actual documents and tickets, not vendor demos.

Running costs included

Model costs at your real volumes, the number most AI pitches forget.

Failure modes named

Every recommendation states what wrong looks like and who catches it.

05 · Stack

Tools chosen for your handover, not our comfort.

ClaudeOpenAIopen-weight modelsevaluation harnesses

Model-agnostic by design: the roadmap prices at least two model options per use case, because vendor lock-in is a cost too.

06 · Questions

Asked often, answered straight.

Isn't this just riding the AI hype?
The hype is why you need the filter. Roughly half the AI probes we run conclude 'not worth it yet'; that conclusion is cheap now and expensive after a failed build. The other half become projects with measurable returns. The job is telling them apart.
Is our data safe during the probes?
Probes run under NDA, on data-processing terms your counsel can review, using samples you approve, anonymized where possible. Nothing is used to train anything, and everything is deleted at engagement close.
Do we have to build with you afterwards?
No, like our discovery work, the roadmap is written to be executed by anyone, and the fee is credited if you do build with us. Vendor-neutrality is what makes the advice trustworthy.

Ready to talk about ai strategy?

Describe where you are. We’ll respond within one business day with honest next steps, even if the honest next step isn’t us.

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