Know what you’re buying. Know where the value is

Decision-grade findings before the deal. Working results after it.

Independent technology, data and AI support before and after the deal. We test what is really there before you invest, then help portfolio companies turn technology, proprietary data and AI into measurable value.

Talk about a deal Before the deal

When we get the call

The moments that need an independent view

  • A target in exclusivity, with technology or AI at the centre of the thesis
  • An AI story that carries part of the valuation
  • A data, research or information business where the data is the asset
  • The first 100 days after completion
  • A value creation plan stuck at the pilot stage
  • A portfolio company losing its CTO or data leader
  • A portfolio company preparing for a sale or refinancing

Before the deal

Know what you are actually buying

Technology, data and AI due diligence to the deal timetable: what is really there, what it will cost to put right and whether it supports the investment case. Confidence before you commit, in a ranked report the deal team can act on.

  • Does the technology do what management says?

    Claims tested against the architecture, the code, the data and the people who run it.

  • Is the architecture fit for the growth plan?

    Whether the platform takes the volumes, markets and products in the plan, and what it costs if it does not.

  • Is the AI differentiated, or mostly vendor capability?

    What the business has built, what it rents from a model provider and what a competitor could copy quickly.

  • What proprietary data advantage actually exists?

    Which data is unique, usable, rights-cleared and maintained, and which only looks that way in the data room.

  • What depends on key people or undocumented knowledge?

    Where delivery, operations or customer knowledge sits with a few people, and what it would take to spread it.

  • What technical debt will need funding after completion?

    The remediation the plan does not yet include, sized and ranked.

  • What is the true cloud, platform and supplier cost exposure?

    Run costs at current and planned volumes, contract terms, lock-in and the cost of leaving.

  • Can the product and data architecture support the investment case?

    Whether the roadmap in the plan can be built on what exists, and how long it would take.

  • Is there real AI product potential, or an AI narrative?

    Where AI could add revenue or margin with evidence behind it, and where the story runs ahead of the capability.

  • What needs doing in the first 100 days?

    The priorities a new owner should fund first, and the decisions that can wait.

What you get

  • A red-flag report, ranked by what would move the price, the terms or the plan
  • The evidence behind each finding, with untested claims marked as untested
  • An indicative cost to fix, integrate and run
  • Questions to put to management before signing
  • A first view of the 100-day priorities
  • A readout with the deal team, and follow-up after it

Security and resilience are assessed within the diligence scope, and we say where specialist work such as penetration testing or a cyber audit is needed. Legal, tax and financial diligence remain with your other advisers. Funds, operating partners and corporate acquirers can engage us directly, or with a specialist diligence firm alongside.

Where to start

After the deal

Know where technology, data and AI create value, then make it happen

Portfolio value creation: faster results, more valuable products and a stronger operating business, measured. We work alongside the portfolio company’s own team, from the plan to a working result.

  • Find where data and AI will pay

    The workflows, products and decisions where data and AI would create measurable commercial value, with the economics shown.

  • Redesign expensive expert workflows

    The recurring work where specialist time goes, redesigned around people and AI and measured before and after.

  • Build AI products from proprietary knowledge and data

    New products and features from what the business already owns, built to production standard.

  • Productise information assets

    Data and content the business holds, turned into something customers will pay for.

  • Improve data platforms and analytics

    Platforms that give management the numbers it needs, at a cost that makes sense.

  • Accelerate the first working AI workflow

    One workflow live on the company’s own information within weeks, so the plan has early evidence.

  • Fix platforms and programmes that are not delivering

    Root causes found and put right before more money goes in.

  • Define the technology and data operating model

    Who owns what, how decisions get made and what the team needs to look like.

  • Cover CTO, CDO and data and AI leadership gaps

    Senior leadership on a monthly remit, with a plan for the permanent hire.

  • Give management a practical 90-day plan

    What happens first, who owns it and how progress will be measured.

  • Move from strategy into production

    From the strategy to systems people use, with the evidence that they work.

What management gets

  • A ranked view of where data and AI will pay, with the economics
  • One working workflow or fix, measured before and after
  • A technology and data operating model with named owners
  • Senior cover for leadership gaps, with a plan for the permanent hire
  • A roadmap the board and the operating partner can track

Where to start

Data, insight & information services

Turn proprietary knowledge and data into stronger products, workflows and moats

Many PE-backed businesses sell what they know: proprietary data, research, benchmarks or specialist judgement. AI can widen that moat or wear it away, so the diligence and the value creation plan both need to know which.

Businesses like these

  • Market intelligence
  • Research and advisory
  • Specialist publishing
  • Benchmarking
  • Subscription data
  • Regulatory and policy intelligence
  • Professional information services
  • Expert knowledge businesses

Where the value can come from

  • New AI-enabled subscription products

    Products and tiers built on the data and research the business already owns.

  • Proprietary datasets, used in new ways

    New interfaces and workflows that make the data easier to use and harder to replace.

  • Data and expert narrative together

    The numbers and the analysis that explains them, delivered as one product.

  • Personalised research and intelligence

    Output shaped to each client’s interests and entitlements.

  • Less expert time rebuilding previous work

    Analysts start from what the firm already knows.

  • Client-facing intelligence workflows

    Briefings, monitoring and alerts built into the client’s own work.

  • Better data product economics

    A lower cost to produce, maintain and deliver each product.

  • Whether AI strengthens or threatens the moat

    Assessed before the price is set, and revisited in the plan.

Answerable

The patterns are already built

TechGuidr has already built the patterns that turn proprietary knowledge and data into working AI products and workflows. Answerable, our own AI working environment, is in daily production use at a research business with more than 78,500 publications. A portfolio company can start from Answerable as the product, or we can use its architecture inside the company’s own environment.

Our product

Answerable as the product

Start from the platform we already run in production, configured around your organisation.

We configure it around your

  • Data and knowledge
  • Terminology and domain
  • Permissions
  • People and workflows
  • Branding
  • Integrations
  • Evaluation requirements

Usually the faster route to a working environment.

Your environment

The Answerable architecture, in your environment

For organisations that do not want another product or platform. We use the architecture, patterns, components and engineering approach proven in Answerable to build the capability inside your own technology and data estate.

Depending on what you run, for example

  • Azure and AI Foundry
  • Fabric or Databricks
  • Azure AI Search
  • Your identity, security and data controls
  • Your operating model and support

None of these is required: your architecture, security, identity, data and operating model decide the implementation. You own and run the deployment; rights in any TechGuidr components it uses are agreed in the contract, not transferred by default.

If Microsoft Copilot, ChatGPT, a platform you already run, another product or a bespoke build is the better answer, we recommend that instead and say why. Our interest in Answerable is always disclosed.

See Answerable Research & insight

The first 100 days

Make priorities explicit and get one meaningful result moving

After completion, the findings become a plan management can run. It typically settles:

  • What to fix first

    The risks and constraints that block the plan, in order.

  • Which technology decisions can wait

    So money and attention go where they change the outcome.

  • Which AI opportunities have real economics

    Ranked by value, cost and readiness, with the evidence.

  • One working result

    A first workflow or fix live, so progress is visible early.

  • Platform and data dependencies

    What each priority relies on, and what has to happen first.

  • Operating-model gaps

    Ownership, decision rights and ways of working that need to change.

  • Capability and leadership gaps

    Where the team needs senior cover or new hires.

  • A measurable roadmap

    Milestones the board and the operating partner can track.

The plan starts from the diligence findings, or from an Independent Review where someone else did the diligence. The first priorities then run as an AI Work Review, a Sprint, Fix & Optimise or Leadership as a Service, each scoped and priced on its own.

Proof

What we have built and done

Each item says where it comes from: our own products, the founder’s earlier roles or demo configurations.

None of these is a private equity engagement. They show the production products, measured results and leadership a deal team would be relying on.

  • TechGuidr product

    Answerable, in production and daily use

    Our AI working environment for proprietary knowledge and data, first built around the requirements of a leading economic intelligence business: more than 78,500 publications and 25 years of research alongside its forecast data, with sources a reader can check. During ingestion we found premium research that was publicly reachable and reported it to leadership.

    Read the case study
  • TechGuidr product

    Bearing, AI inside an account team’s week

    Portfolio intelligence, renewal risk and personalised actions inside the daily workflow of sales and marketing teams. Every action has an owner and a date, and a person approves or rejects what the agents suggest.

    Read the case study
  • Founder’s earlier role

    A platform that looked enterprise-ready, assessed and rebuilt

    Delivered by Dan in an earlier technology leadership role, at a UK learning and wellbeing business. The product looked close to enterprise readiness; the architecture showed dashboards disconnected from live data. After the rebuild, dashboard queries went from four to five seconds to under 600 milliseconds.

    Read the case study
  • Founder’s earlier role

    Global Data & AI leadership at Microsoft

    From 2022 to 2025 Dan was Global Data & AI Lead in Microsoft’s Retail & Consumer Goods solutions team, after early Azure analytics work and two decades of enterprise data platforms and delivery.

    About Dan

Questions

What buyers ask first

What does technology due diligence cost?

Focused technology, data and AI due diligence starts from £10,000 ex VAT. Fuller assessments are typically £15,000 to £35,000 and above, depending on the deal size, complexity and timetable.

Can you help after the deal as well as before it?

Yes. The findings become a plan for the first 100 days, and the same senior team can then deliver the first priorities through an AI Work Review, an AI Work Sprint, Fix & Optimise or Leadership as a Service, each priced on its own.

Do you carry out penetration testing or cyber audits?

No. We assess security and resilience within the diligence scope and say where specialist testing or a cyber audit is needed.

Can you help a data or research business use AI on its own data?

Yes. We have built the patterns that turn proprietary knowledge and data into working AI products and workflows, in production at a research business. A portfolio company can start from Answerable, or have the architecture built inside its own environment.

Who can engage you?

Funds, operating partners, corporate acquirers and portfolio company leadership teams can all engage us directly, and we can work alongside a specialist diligence firm.

Industries

Related industries

All industries

Tell us about the deal or the portfolio company

Before the deal, we will tell you what fits inside the timetable and what access we would need. After it, we will tell you where to start.

Talk about a deal Technology Due Diligence