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AI strategy · Data · Engineering

From AI ambition to systems that run in production.

We help leadership teams decide what is worth building, prove it fast, and ship it with the governance, data foundations and engineering rigour that make it stick.

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01 / 04The ambition

Every leadership team has a hundred AI ideas.

Copilots, agents, forecasting, a chatbot for the service desk. The list grows every quarter, and every idea sounds plausible in a meeting.

02 / 04The gap

Most of them stall as pilots.

A demo works on clean data, then meets the real systems, the real controls and the real running cost. Without an owner and a way to measure value, it never leaves the lab.

03 / 04The method

We rank them, then prove the few that matter.

A value map sets every idea against what it is worth and what it would take. The best are built as thin slices on real data, with evaluation and a cost model from the first week.

04 / 04Production

What survives runs in production, and keeps running.

Hardened, governed and handed to your team with the documentation and the habits to run it. That is the part of AI that shows up in the numbers.

Who we are

Adoption is not the hard part. Getting value out is.

Most organisations already have AI pilots. Far fewer have AI in the operating rhythm of the business, with owners, controls and a measurable return. We work at that gap.

DevQux puts strategists, data scientists and software engineers on one team, so the roadmap we write is one we can also build, and the system we build is one your teams can run.

When teams call us

  1. 01

    You have a list of AI ideas and no reliable way to rank them by value, feasibility and risk.

  2. 02

    A pilot worked in a demo and stalled on the way to production: data access, security review, running cost, or ownership.

  3. 03

    The board is asking about AI governance, regulation and risk, and nobody owns the answer yet.

How we help

Six ways we work with clients

Each engagement starts with a business decision, not a technology. We scope to the outcome and staff to the work.

AI strategy & readiness

A ranked portfolio of AI opportunities, an honest read of your data, talent and infrastructure, and a 12 to 18 month roadmap leadership can fund.

Talk it through (AI strategy & readiness)

Data foundations

Data platforms, pipelines and quality controls that make models trustworthy, from lakehouse design to feature stores and lineage.

Talk it through (Data foundations)

Generative & agentic AI

Copilots, agents and retrieval systems built on your own knowledge, with evaluation harnesses, guardrails and cost controls designed in from day one.

Talk it through (Generative & agentic AI)

AI governance & risk

Operating models, policies and controls aligned to the EU AI Act, NIST AI RMF and ISO/IEC 42001, so your programme passes audit and earns trust.

Talk it through (AI governance & risk)

Capability building

Embedded teams, hiring support and hands-on training that leave your people able to own, extend and improve what we built together.

Talk it through (Capability building)

What makes us different

Strategy and engineering in the same room.

We do not hand a deck to a delivery partner. The people who scope the work build it, and the people who build it stay accountable for the outcome.

  1. 01

    Outcome-scoped engagements

    Every statement of work names the business metric we are moving and how we will measure it.

  2. 02

    Vendor-neutral by design

    We work across clouds and model providers and recommend the stack that fits your constraints, not our partnerships.

  3. 03

    Production-first engineering

    Evaluation, observability, security review and cost modelling are part of the first sprint, not the last.

  4. 04

    We leave your team stronger

    Pairing, documentation and handover are deliverables. Success is when you no longer need us for the thing we built.

How an engagement runs

Four steps from question to running system

  1. 1

    Discover

    Interviews, a data inventory and a value map.

    2 to 3 weeks
  2. 2

    Prove

    A thin end-to-end slice on real data, with evaluation and a running-cost model.

    4 to 8 weeks
  3. 3

    Build

    Harden, integrate and roll out, with security, governance and change management.

  4. 4

    Scale

    Operate, measure and extend. Hand over to your team on a defined timeline.

Start here

Start with a 30-minute working session.

Bring one problem. We will leave you with a point of view on whether AI is the right answer, what it would take, and how we would prove it. No deck, no pitch.

We reply within two business days.