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AI Discovery & Roadmapping

AI discovery & roadmapping services

A short, structured discovery that turns a long list of AI ideas into a ranked roadmap with costs, risks and a first project ready to build.

A custom engineering engagement

We scope this service around your workflow, existing systems and acceptance criteria. The work starts with the problem and its constraints, then a written scope and price. A packaged solution is a different buying route: choose it when its defined deliverables already fit the job.

01The problem

Where things usually stall

Pinpoint the AI use cases worth funding and the order to build them in.

  • 01

    Too many ideas, no ranking

    Every department has an AI wish list. Without a shared way to score them, the loudest idea wins.

  • 02

    Unknown data readiness

    Teams commit to a use case before checking whether the data behind it exists, can be reached or is any good.

  • 03

    No business case

    Pilots get approved on enthusiasm and cancelled when nobody can say what they returned.

02Capabilities

What we deliver

  • 01

    Workflow mapping

    We sit with the people doing the work and map where time, errors and delays actually pile up.

  • 02

    Use-case scoring

    Each opportunity is scored on value, feasibility, data readiness and risk, using a model your leadership can reuse.

  • 03

    Data readiness audit

    We check the sources each use case depends on: where the data lives, who owns it and what needs fixing.

  • 04

    Architecture options

    Build, buy or extend. We outline the options for each priority use case with rough costs.

  • 05

    ROI model

    Baseline metrics and expected impact for every recommended project, so success is defined before work starts.

  • 06

    90-day roadmap

    A sequenced plan with the first project scoped, estimated and ready for a build team.

03Outcomes

What changes once it’s live

  • 01

    A ranked decision

    Use cases compared against business value, available data, operating risk and integration effort.

  • 02

    A buildable first scope

    An initial workflow with owners, acceptance criteria and dependencies your team can review.

  • 03

    A reason to stop when needed

    Data gaps and weak business cases made visible before they become a software budget.

04Approach

How we deliver it

  1. 01

    Find the decision behind the request

    Interview the people doing the work and establish which business decision or task needs to improve.

  2. 02

    Check the evidence

    Inspect data samples, access permissions and system boundaries instead of assuming they are ready.

  3. 03

    Compare feasible routes

    Separate deterministic automation, retrieval and model-based approaches, then explain the trade-offs.

  4. 04

    Hand over a delivery decision

    Present the ranked opportunities, estimates, ownership and first validation plan, including reasons not to proceed.

05Stack

Tools we use most

  • Claude
  • Gemini
  • Python
  • pandas
  • Snowflake
  • PostgreSQL
  • OpenAI
  • LlamaIndex
  • Pinecone
  • Weaviate
  • Ollama
  • MLflow

07FAQ

Frequently asked questions

View pricing guide
How long does discovery take?

Three to four weeks for most companies. Larger organisations with several business units may need six.

What do we get at the end?

A ranked use-case portfolio, a data readiness report, an ROI model and a scoped first project with estimates.

Is discovery tied to a build contract?

No. The roadmap is yours. Many clients build the first project with us; some take it in-house.

How is the engagement priced?

We agree a fixed scope and price after reviewing the work, system dependencies and acceptance criteria. The pricing guide explains approved ranges for the engagement types it covers. A written proposal confirms what is included before a build starts.

How should the first delivery decision be made?

Interview the people doing the work and establish which business decision or task needs to improve. Inspect data samples, access permissions and system boundaries instead of assuming they are ready.

What should the acceptance criteria cover?

Use cases compared against business value, available data, operating risk and integration effort. An initial workflow with owners, acceptance criteria and dependencies your team can review. Data gaps and weak business cases made visible before they become a software budget.

AI readiness

Not sure where you stand?

Ten questions, about three minutes. Find out what needs attention before you build.

AI Discovery & Roadmapping

AI Discovery & Roadmapping

Pinpoint the AI use cases worth funding and the order to build them in.