Data & AI consultancy · London

Data and AI that make it to production.

Wysegen helps enterprises and financial services firms turn fragmented data into governed, AI-ready assets, then leads the programmes that depend on them. From the first diagnostic to go-live.

UK Ltd, LondonFixed-fee diagnosticsUK GDPR by designDelivery in English & French
100,000+employees unified in a group HR data lake across 150 subsidiaries
12concurrent data programmes led across four sectors
15 days → D+1investor reporting, after an end-to-end data overhaul
£1M+programme budgets managed from scoping to go-live
Why AI programmes stall

Most AI initiatives don't fail on the model. They fail on the data underneath it.

Boards are funding AI. Few organisations have measured whether their data can carry it. The gap shows up late: in a pilot that never scales, a regulator's question nobody can answer, or a report that takes two weeks to produce.

60%

of AI projects unsupported by AI-ready data will be abandoned through 2026.Gartner, February 2025

95%

of organisations report no measurable P&L return from their generative AI pilots.MIT NANDA, The GenAI Divide, 2025

30%

of generative AI projects were expected to be dropped after proof of concept by the end of 2025.Gartner, July 2024

Wysegen starts where the risk sits: we measure the data, fix the foundations that matter for your priority use cases, and stay accountable until they run in production.

Services

Six ways we move your data and AI agenda forward

Each engagement has a defined scope, named deliverables and exit criteria. You know what you are buying before we start.

Data & AI Audit

An objective, measured baseline of your data estate and its readiness for AI.

  • Inventory, lineage and flows mapped
  • Quality measured, not estimated
  • Maturity score across 6 dimensions
  • Costed 6 to 12 month roadmap
4–8 weeksFixed fee

AI Strategy & Industrialisation

From a list of ideas to a ranked portfolio of AI use cases with owners, budgets and controls.

  • Use cases scored on value and feasibility
  • Build, buy or partner decisions
  • Priority workflows deployed and monitored
  • MLOps and LLMOps foundations
6–16 weeksPhased SOW

Data Platform & Engineering

Pipelines and models that are versioned, tested and documented, so your team can run them after we leave.

  • Snowflake, Databricks, BigQuery, Azure
  • dbt, Airflow, Dagster, Fivetran
  • Data quality tests and observability
  • Vendor-neutral stack decisions
3–12 monthsProject or retainer

Programme Delivery

We take a data programme that has drifted, reset it and carry it through to a defined milestone.

  • Backlog and scope reset in 3 to 4 weeks
  • Steering with tracked decisions
  • Suppliers held to their commitments
  • Part of the fee tied to the milestone
3–9 monthsMilestone-linked

Data & AI Governance

Ownership, policies and controls your auditors, regulators and board can rely on.

  • Data ownership and stewardship model
  • UK GDPR and EU AI Act readiness
  • ISO/IEC 42001 alignment
  • Model risk aligned to PRA SS1/23
6–12 weeksFixed fee
Most requested

Embedded Data & AI Leadership

A senior Data or AI programme lead inside your team, backed by Wysegen's method and peer review.

  • Interim Head of Data or AI Product Lead
  • Fractional CDO for growing firms
  • Onboarded in 2 to 3 weeks
  • Structured handover to a permanent hire
3–12 monthsMonthly

Capability building. When the project ends, your team keeps the skills. Part-time Data Analytics and AI Workflow programmes for your staff, delivered with our partner Luxley Digital College.

Explore training
AI, industrialised

From pilot to production in five controlled steps

Most firms have run an AI pilot. Fewer have one that runs every day, is monitored, and passes a model risk review. This is how we get there.

Weeks 1–3

Assess

Data readiness, current tooling, skills and risk appetite, scored against your priority use cases.

Weeks 3–5

Prioritise

Use cases ranked on value, feasibility and regulatory exposure. Two or three go forward.

Weeks 5–12

Build

Data pipelines, retrieval, models or agents built against measurable acceptance criteria.

Ongoing

Govern

Model inventory, human oversight, logging and evaluation that stand up to audit.

Quarter 2+

Scale

Reusable components, run books and team training so the next use case costs less.

We work withLLMs & RAGAI agentsDocument intelligenceForecasting & MLMLOps / LLMOpsAzure OpenAIAWS BedrockVertex AIEvaluation & guardrails
Embedded, not parachuted in

We work inside your teams, at their pace, until they can run without us.

Our consultants sit with your engineers, analysts and product owners. Decisions are made together and documented as we go.

See how we work →
Two-minute self-assessment

How AI-ready is your data?

Answer six questions honestly. You get an indicative score and the first thing we would look at.

Do you have an up-to-date inventory of your key data sources and flows?Systems, owners, where data moves

Is data quality measured with figures, not impressions?Completeness, duplicates, freshness

Does each critical dataset have a named business owner?Accountable for definitions and access

Can your platform deliver clean data to a new use case in weeks?Pipelines are automated, tested, documented

Are your AI use cases ranked by value, with budgets and owners?Not just a list of ideas

Do you have AI risk controls a regulator or auditor would accept?Model inventory, oversight, UK GDPR, EU AI Act

Selected engagements

Results that held after we left

Three programmes led by Wysegen's founding team, measured before and after.

Banking · International group

Building a group HR data lake for 100,000+ employees

150 subsidiaries on four continents, no shared employee identifier and 700 users without common BI. We set the reference model, cleansed and unified the data, and trained the users.

Data lakePythonData governanceBI
−50%duplication errors
100%staff with a unique ID
−30%analysis time
+40%BI adoption, 700 users
Financial services · Leasing

From 15-day manual reporting to daily investor access

Investor reports built by hand in Excel, partner payments keyed manually with recurring errors, no GDPR framework. We centralised the data, automated reconciliation and set governance.

AutomationBank reconciliationUK GDPRReporting
D+1investor reporting, was 15 days
0partner payment errors
−80%data inconsistencies
100%GDPR compliance
Consultancy · Multi-sector

Leading 12 concurrent data programmes with a team of 8

Single customer view, big data architecture and governance for clients in transport, retail and healthcare, delivered in parallel without diluting quality.

Single customer viewBig dataPythonPMO
95%client satisfaction
15 hsaved per week by automation
−25%processing time
+30%customer data quality
The real problem wasn't technical. Nobody had defined what a unique identifier was before starting. Twelve per cent duplicates across 100,000 people, and nobody saw it because nobody was looking.
William Hornig, Founder, on the banking group HR data lake

Client names are withheld under confidentiality agreements. Full case studies are available on request.

How we work

Measured first. Delivered second. Handed over always.

Every engagement follows the same four stages, so you see progress and evidence at each step.

A consultant interviewing a stakeholder during a diagnostic
Every diagnostic starts with structured interviews across business, IT and operations.
01

Diagnose

We measure your data and processes against the decisions they need to support.

You get a scored baseline and risk register

02

Prioritise

We rank initiatives on business value, effort and risk, and agree what not to do.

You get a costed roadmap your board can sign

03

Deliver

Hands-on build and programme leadership in short, demonstrable increments.

You get working outcomes, not slideware

04

Hand over

Documentation, training and KPIs so your team owns the result from day one.

You get independence, with optional steering

Engagement models

Start small. Scale when the evidence is there.

Clear commercial terms under a UK statement of work, invoiced in pounds sterling.

Diagnostic

A fixed-scope audit that gives you an objective baseline and a costed roadmap.

Duration
4–8 weeks
Pricing
Fixed fee, agreed upfront
Best for
Before a major AI, platform or migration investment
Most common

Delivery programme

We lead a programme to a defined milestone, with part of our fee tied to reaching it.

Duration
3–9 months
Pricing
Monthly fee plus milestone success fee
Best for
Programmes that are late, stalled or about to start

Embedded lead

A senior Data or AI lead joins your team on contract, with Wysegen behind them.

Duration
3–12 months, renewable
Pricing
Monthly, no long lock-in
Best for
Leadership gaps while you hire permanently
Sectors

We know the data problems of your industry

Financial services

Regulatory reporting, model risk, investor and client data.

Transport & mobility

Operational data, passenger flows, asset and network performance.

Public sector

Registers, case data, procurement-ready delivery and assurance.

HR & workforce

Group HR data lakes, unique employee identity, people analytics.

Retail & consumer

Single customer view, loyalty, pricing and demand data.

Healthcare

Sensitive data handling, interoperability, outcome reporting.

Telecoms

High-volume event data, churn and network analytics.

Industry & manufacturing

Regulated R&D, product lifecycle, certification and quality data.

Charities & foundations

Donor and impact data, governance on lean budgets.

About Wysegen

Senior people on your project. Every time.

Wysegen is a London firm founded by practitioners. The people who scope your engagement are the people who deliver it.

Portrait of William Hornig

William Hornig

Founder & CEO · Data & AI programme lead

More than ten years leading data programmes in banking, transport and digital. Led a group-wide HR data lake for a banking group of 100,000+ employees and managed programme budgets above £1M.

MSc Information Systems MgmtData SciencePRINCE2ITIL 4
Portrait of Rowan Read

Rowan Read

Co-founder · UK Operations & Governance

Eleven years in UK public service at HM Land Registry, working on registration and governance. Rowan leads client relationships and makes sure every engagement runs to UK standards. He is the first person most clients speak to.

UK public sectorGovernanceClient partnership

The problem is rarely where you think it is.

We start by asking which decision the data must support. The answer often moves the project.

Clean data beats clever data.

Governance and quality come before any model. That is what makes AI reliable.

Deadlines are kept. Scope is honest.

If something will not land on time, you hear it from us first, with a plan.

FAQ

Questions we hear before the first call

Anything else? Ask us directly at contact@wysegen.com.

How quickly can you start?

Most engagements start within two to three weeks of a signed statement of work. A diagnostic can often start sooner.

Do you work outside London?

Yes. We work across the UK, remotely or on site, and with French-speaking teams in France and the EU.

Are you tied to a technology vendor?

No. We are vendor-neutral and recommend the stack that fits your volumes, team and budget, including what you already own.

How do you contract?

As a UK limited company under a statement of work with defined deliverables and exit criteria, invoiced in GBP. We assess IR35 status with you for every embedded engagement.

How do you handle our data?

We work inside your environment wherever possible, sign NDAs as standard and apply UK GDPR principles to every flow we touch.

What happens on the discovery call?

Thirty minutes with a founder. We listen to your situation, tell you plainly whether we can help, and suggest a first step. There is no charge and no obligation.

Let's talk

Tell us where your data is holding you back.

Book a free 30-minute discovery call with a founder. You leave with a clear view of the next step, whether or not we work together.

  1. We reply within 2 working dayswith two or three slots for a call
  2. 30-minute discovery callyour context, constraints and goals
  3. A short written proposalscope, timeline and fixed price, within a week
Office London, United Kingdom

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