Most technology diligence ends at the report. Ours doesn't.

You're here when

You have more targets than diligence budget, and no way to tell which ones deserve the spend.

Pre-deal Assessment

Outside-in business assessment

An outside-in read of a target's technology, team and market signals, before you commit to full diligence. Built for corporate development teams triaging a pipeline, and for investors sizing a target early.

Deliverables

• —Outside-in technology and digital footprint analysis
• —Hidden risk signals from public and proprietary sources
• —Commercial red-flag triage report
• —Go / no-go recommendation with confidence levels

You're here when

You're buying into a sector nobody in the room can technically judge, and the IC needs an opinion it can price.

Technology Due Diligence

Commercially-focused, PE-fluent

Twelve scope areas, sized to the deal. We start with the foundations, so what we recommend is something the company can actually implement, and every finding carries a cost, an owner and a date. Our partners have run global technology functions, and the integration is costed here, before you sign.

Deliverables

• —Scope sized to the deal, not a blanket review: people, process, platform, security, data, AI, resilience, scalability, obsolescence risk, cost, roadmap
• —A report non-technical readers can use, written for the investment committee
• —A costed roadmap: every recommendation with a cost, a timing and an owner
• —Recommendations that drop into a 100-day plan, plus carve-out and integration risk
• —Architecture, code, IP ownership and how replicable the technology really is
• —AI and generated code: what was built with generative tooling, who owns the output, and what it costs to run and maintain

You're here when

The thesis leans on AI, and nobody in the room can tell you whether the position holds for twelve months.

AI in the Deal

Judgement, not a checklist

AI in a deal behaves like a Rubik's cube. Four faces move at once and turning one turns the others: what is running today, what it could become, what competitors and customers are building instead, and how quickly a defensible position becomes a commodity one. A target that looks strong at signing can lose its place a month after close. This practice exists because that call takes judgement, and someone has to put their name to it.

Deliverables

• —Capacity today: what is actually in production, what it costs to run, and who owns it
• —Potential: what this estate could become, and what the value-creation plan is quietly assuming
• —Disruption from outside: competitors shipping the same capability, and customers building their own
• —Decay: how long the position holds, and what has to be true for it to still be worth something at exit
• —Governance sized to the business: EU AI Act, ISO 42001, model cards and data governance
• —A costed view, so the AI story arrives as an opinion the investment committee can price

You're here when

You're going to market, and the technology story is about to be tested by someone who does this for a living.

Sell-side & Vendor Diligence

Own the technical narrative before a buyer takes it from you

Vendor technology diligence, run the way a buyer would run it, with time left to fix what it finds. We surface every risk before an acquirer does, reframe each one as a costed remediation roadmap, and prepare the management team for the room. Then we stay through investor Q&A to close.

Deliverables

• —Vendor tech DD report in buyer format, written to survive a buyer's own diligence
• —Costed remediation roadmap: every gap with a price, a timing and an owner
• —Management preparation for the investor room, including the questions that land hardest
• —AI narrative that stands up to scrutiny rather than inviting it
• —Investor Q&A support through to close
• —A clear read on what to fix now, what to price in, and what to simply disclose

You're here when

The deal closed, the plan exists on a slide, and nobody internally has the time or the scars to run it.

Post-Merger Integration

The practice most advisers don't have

This is the work we are built around. For repeat acquirers and larger groups, integration is where the deal is either delivered or quietly lost: Day 1 readiness, exit from transitional services, carve-out separation, platform consolidation, and the savings and gains the deal was actually priced on. We run it alongside management, with the people who wrote the diligence, and we bring interim leadership when there is no bench to do it.

Deliverables

• —Day 1 readiness: what must work on the first morning, and who owns each piece
• —Transitional service exit and carve-out separation, sequenced and costed
• —Platform and back-office consolidation (see Ascend)
• —The savings and gains tracked against the plan the deal was priced on
• —Interim CTO, CIO and CPO leadership while the permanent team is built
• —Spend taken out through the hold, and exit readiness at the end of it

You're here when

On paper it's integrated. Inside it's still Them and Us, and the people who hold the knowledge are the ones edging towards the door.

People & Kolbe Practice

The half of integration that fails first

This is Post-Merger Integration seen through the people who have to deliver it. Technology integration fails on people, not platforms. Using Kolbe assessments and decades of integration experience, we map team strengths, predict friction, and design the structure that makes the deal thesis deliverable. It runs alongside the integration programme, not separately from it.

Deliverables

• —Kolbe assessments for leadership and tech teams
• —Merger team mapping & friction prediction
• —Role fit between an entrepreneurial founder-CTO and a governed operator
• —Restructure and operating model design
• —Retention planning for the people who hold the knowledge

You're here when

The value-creation plan assumes AI. What exists is a demo nobody would let near a customer or a diligence room.

Bespoke AI for Portcos

AI that survives the next diligence

Most portco AI is a demo that never reaches production. We build the opposite: bespoke AI applications scoped to a specific value-creation hypothesis, engineered to PE governance standards, and documented to survive the next Tech DD. Every decision the model makes is logged, explainable and defensible to regulators, auditors and acquirers.

Deliverables

• —Data readiness: lineage, licensing, governance and AI usage policy
• —PE-grade architecture: model routing, evals, observability, human-in-the-loop
• —Bespoke build aligned to one clear value-creation hypothesis
• —Taking a generated prototype to production: ownership, testing, security and support
• —Run-the-app: monitoring, cost controls, model cards and exit-ready documentation