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For product leaders
Discovery to pilot

AI Product Consulting

As a product leader or founder, you find out whether an AI feature belongs in your product, which workflow it should serve first and what it costs to run, with pilot evidence before you commit.

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BRIEF: AIPC-07 PHASE: DISCOVERY

Who brings us an AI idea

We work with heads of product and founders who are under pressure to add AI and want evidence before a quarter of engineering time goes into it. Some bring a prototype that impressed in a demo and stalled before production.

Technology choices for the whole business, such as architecture or vendors, belong to our Technology Strategy Consulting service.

Time to call us when

  • The board wants AI on the roadmap and we can't name the feature.
  • The prototype impressed everyone in the demo and fails on real cases.
  • Nobody knows what this feature will cost per month at full use.
  • Our documents are scattered and outdated, and the model would need them.

Why AI features stall before production

A roadmap written around a demo

A few minutes of prompting can produce an impressive demo, and the roadmap follows it. Production then meets permissions, edge cases and policy calls the model cannot see.

Adoption left to chance

Teams get a new AI tool and are told to work it out. Usage stays low because nobody redesigned onboarding, templates and review checkpoints around it.

Pilots with no end date

A pilot that needs six months to show evidence is scoped too broadly or has vague success criteria. Rollouts stuck in pilot for nine months rarely recover.

Scope by workflow, scale on evidence

We start from a business constraint written as a testable target, such as cutting median first response on tier-one tickets from 12 minutes to 4. That target picks the workflow. We map how the work runs today, what people consult and what they hand off, then ask where a model fits.

Every AI output in a customer-facing or regulated flow gets a named reviewer and a written rule for when a human must check it. We recommend training reviewers before users: two days on review patterns beats a week of prompt workshops. Where the feature meets a security review, we cover that too.

2-4 weeks
Fixed-price discovery sprint
8-12 weeks
Pilot window before a scale decision

Use-case selection

We rank candidate use cases by the business constraint each one moves and the data it needs, then choose one workflow with a measurable outcome.

Data readiness

We list the documents and connectors the model may read under your access controls, and flag outdated or contradictory material before a pilot relies on it.

Evaluation and feedback

Before release the same prompts run against two providers to catch regressions, and users get a way to flag a wrong answer in about 30 seconds.

Running cost

We estimate cost per accepted output before the pilot and keep the model behind your own interface, with written off-ramp criteria for price or quality changes.

What you hold after the pilot

  • A one-page recommendation with one specific next step
  • A step-by-step map of the chosen workflow
  • The sources the feature may read, with access rules
  • Written rules on who checks AI output and when
  • A cost model per accepted output, with provider off-ramp criteria

From idea to decision

One workflow, start to decision

  1. Discovery sprint

    We test the idea against your data and your people, then pick one workflow with a measurable outcome and write down its current state in detail.

    Duration
    2-4 weeks
    Result
    A next step for one named workflow
    • Use case
    • Data

  2. Narrow build

    Your engineers or ours build a narrow version inside a tool the team already uses, such as Slack or the CRM, with review rules agreed first.

    Duration
    4 weeks
    Result
    A working slice ready for pilot users
    • Integration
    • Rules

  3. User trial

    A small group uses the feature on real work while we track time saved, edit rate and output quality as judged by your own reviewers.

    Duration
    4 weeks
    Result
    Evidence on adoption, quality and running cost
    • Edit rate
    • Cost

  4. Decision

    You choose to scale to the next workflow, redesign or stop, with the reasons in writing. Customer-facing features wait for evidence from an internal workflow.

    Duration
    1-2 weeks
    Result
    The next workflow, or a documented stop
    • Rollout
    • Stop

Ways to buy AI advice

Discovery sprint

From $8,000

Two to four weeks at a fixed price, for when you are sure AI belongs in the business but cannot yet name where.

Roadmap retainer

From $8,000 per month

Three to six months, when several use cases are queued and leadership agrees. We sequence what ships first, what waits and what gets cut.

AI strategy review

A two-hour session on your AI roadmap and a quarter of usage data. You leave with three constraints to tighten and one feature to kill.

Retainers carry a fixed monthly fee and a bounded scope. Later engineering is quoted per scoped milestone or on time and materials.

Related reading

All articles →

Questions product teams ask about AI

What does a discovery sprint deliver?

A one-page recommendation with one specific next step, such as prototyping a named workflow, hiring one engineer for a defined job or dropping the idea. It works best when data access is ready and one executive sponsor will make the call.

Which use case should go first?

The one tied to the constraint that costs you most, in a single workflow where change can be measured, usually an internal one. A broad assistant as the first launch is what kills most AI roadmaps. See our AI product strategy guide.

What if the pilot says no?

Then you stop, and we write down why it did not work. A written stop after one pilot is worth far more than a feature drifting into half-adoption, and the workflow map and data clean-up stay useful for whatever you try next.

Do you work on AI moderation queues?

Yes. Our moderation work starts where a classifier's output meets a reviewer's decision. We read your policy and a sample queue in a week and return a one-page review; larger engagements run 4-8 weeks and end with a working tool change. See human-centred AI moderation.

Should we self-host a model?

Usually not. For most SMBs a hosted API is the right start, with the saved engineering time spent on integration, retrieval and review tooling. Self-hosting fits a clear data-residency or cost reason plus a team to maintain it for years. More in generative AI in business.

Client reviews

What clients say about working with Vadim

5.0 75 reviews on Google

All reviews
Yosef M. D. 2022 · Google ★★★★★

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I strongly recommend getting in touch with Vadim.

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itzik klein 2025 · Google ★★★★★

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ארז כהן 2025 · Google ★★★★★

Professional service, excellent attitude and a fair price. Highly recommended.

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Tomer Shloman 2022 · Google ★★★★★

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He solved a problem other people could not.

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Ronen Radomski 2021 · Google ★★★★★

A real professional!!
Reliable, honest and fair!
Highly recommended!!

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Test your AI idea

Tell us which workflow you want AI to change and what data it needs. We reply with whether a sprint fits or what to fix first.