Technology after the deal: the engineering work that follows an investment

Technology after the deal is the engineering work that follows an investment: what the portfolio company's software has to become in the first hundred days, what happens when two products are merged or one is cut from a parent, how much technical debt the portfolio is carrying, who leads the engineering function, and how progress reaches the fund in a form a board can read.

This guide is written for the people who own that work from the investor's side: private equity operating partners, venture platform leads, and board members of portfolio companies. It also serves a portfolio CEO who has just been handed a diligence report and a value creation plan and wants to know what the technology section means in practice.

Reveneau builds software for portfolio companies with AI-written code checked against an evaluation suite derived from the specification, so a fund can put a fixed-price build into a value creation plan and hold it to the plan; the pages here describe the work as any operating partner would run it, whoever does the building. Where a third party has measured something, the figure and its source are given. Where nothing has been measured, the page says so instead of inventing a number.

Who this guide is for, and what it is not

The reader we have in mind has closed a deal, or is about to. The technical due diligence guide covers the work before signing: what to look at, how to run the review, what the report should contain. This guide starts where that one stops. The diligence report is an input here, and the first thing the 100-day plan has to do is turn it into work.

Three kinds of reader arrive at this page with different questions:

• An operating partner at a private equity fund wants a technology workstream that fits the 100-day plan, integrates the add-ons the fund is about to buy, and produces a number for the board deck each quarter.
• A platform lead at a venture fund wants to know what engineering support the platform team can offer a founder, where that support stops, and when to bring in an outside team.
• A board member of a portfolio company wants to read the engineering update and know whether the team is on track without learning to read code.

Each sub-page answers one of those questions in full. This pillar gives the shape of the whole thing.

The short version

If you read nothing else, this is the answer to "what does the technology work after a deal consist of":

• Turn the diligence report into a plan in the first 30 days. Every finding becomes a task with an owner, a date, a cost and a measure, sequenced by risk to the investment case.
• Fix the things that can lose the company first: access control, backups, single points of failure in people and systems, and any licence exposure the report flagged.
• Decide the integration or separation architecture before buying the next add-on. Buy-and-build without a platform decision produces a portfolio of systems that cannot share a customer record.
• Size the technical debt across the portfolio in one method so the fund can compare companies and spend where the return is largest.
• Choose the engineering leadership model deliberately: interim, fractional, permanent hire, or a build partner that takes the outcome.
• Put AI adoption on the plan as engineering work with a measure, since the surveys say funds expect value from it and few portfolio companies have it in production.
• Report progress with four delivery measures every quarter, so the board sees a trend instead of a status colour.

The rest of this page explains why each of those matters, in the order the work happens.

How the work runs, step by step

The technology work after a deal falls into four phases. They overlap in practice, and the order below is the order in which a fund usually notices each one.

Phase 1: the first 100 days

The 100-day plan is the document a private equity fund and a new management team agree in the weeks after closing. It lists what changes first. The idea is older than most of the funds using it: a 2014 survey of 120 private equity professionals by The Deal and Pepper Hamilton LLP found 47.5 percent using a 100-day program to plan operational improvements, against 16.8 percent using a three-to-five-year plan.

Technology usually enters that plan late and thin. BCG's January 2026 survey of 100 senior private equity investors found that 73 percent run digital due diligence on most deals, but only 22 percent said a company's digital readiness influences the go or no-go decision. Nearly 30 percent integrate digital levers into the diligence phase, and a further 57 percent say those levers are core to value creation planning. Read together, those figures say the technology work is expected to create value after signing, in the plan, and that most of it has not been decided when the deal closes.

The technology workstream in a 100-day plan does three things: it converts the diligence findings into a sequenced backlog, it stabilises whatever can lose the company (access, backups, key-person risk, licence exposure), and it sets the measures the board will see each quarter. The technology workstream in a 100-day plan walks through it week by week, and turning the diligence report into the first-year plan covers the conversion from findings to work.

Venture funds run a lighter version of the same thing through their platform teams. Engineering support as a platform service reads what Vista, Insight Partners, Bain Capital Ventures and a16z say they offer, by their own account, and where each stops.

Phase 2: integration and separation

The second phase begins when the fund buys another company, or sells part of this one. Both produce a software problem.

Merging two codebases is the problem after an acquisition: two products, two data models, two teams, one customer list that has to become one record. Merging two codebases after an acquisition sets out the four integration shapes and how to choose between them.

Buy-and-build is the same problem repeated. Bain & Company reported in February 2019 that in 2003, 21 percent of add-on deals were at least the fourth acquisition by a single platform company, and that the share had moved closer to 30 percent in recent years, with 10 percent of add-ons being at least the tenth. A platform company on its fourth add-on either has an integration architecture or has four products with the same logo. Buy and build: consolidating acquired software platforms is the page for that decision.

Carve-outs run the problem backwards. The software has to be cut away from a parent's shared systems, identity, data and vendor contracts, usually under a transition services agreement with an end date. Deloitte's 2026 Global Divestiture Survey, with 981 respondents, found that in 2024 only one-third of sellers met their expectations for timing and proceeds, and that by the end of 2025 nearly half did. Carve-outs: separating software from a parent company covers the separation plan.

Phase 3: capability

The third phase is the standing question of whether the company can build what the investment case needs.

Technical debt across a portfolio is the first part. A single-company method is described in the diligence guide's page on assessing technical debt. At portfolio level the question changes: the fund has ten companies, one budget, and needs to know where a dollar of engineering spend returns most. Stripe's September 2018 survey of developers and executives across six countries measured a mean of 17.3 hours per week going to maintenance work out of a 41.1 hour week, with 13.5 hours attributed to technical debt. Technical debt across a portfolio gives a method that produces one comparable score per company.

Engineering leadership is the second part. A portfolio company that has lost its CTO, or never had one, has four options: an interim executive, a fractional one, a permanent hire, or a build partner that takes responsibility for the outcome. Interim, fractional, or as-a-service CTO compares them without a preferred answer, and our shorter comparison with a fractional CTO covers the one people ask about most.

AI adoption is the third part, and the one where the surveys are loudest. Mubadala and MGX, with Bain & Company, reported in January 2025 that 93 percent of investment funds anticipate AI-driven value within three years. FTI Consulting's May 2026 survey of 200 fund and operating leaders found 36 percent using AI across use cases and 7 percent at enterprise scale. AI adoption in portfolio companies sets the surveys side by side and says what a fund can do with them.

Phase 4: reporting

The last phase is the one that runs for the whole hold. The board needs to know whether engineering is on track, and most engineering updates are prose plus a colour. Reporting engineering progress to the fund builds the quarterly report on DORA's four measures, which the DevOps Research and Assessment programme has used to describe software delivery performance for a decade: change lead time, deployment frequency, change fail rate, and failed deployment recovery time.

What to check, in each phase

The list below is the operating partner's version of the checklist. Each item names a document or a number that should exist, so the check is "does it exist and is it current" rather than a judgement call.

Phase	What should exist by the end of it	Who owns it
First 100 days	A technology backlog built from the diligence report, each item with owner, date, cost and measure	Portfolio CTO or interim, signed off by the operating partner
First 100 days	Access control, backup and restore, and key-person risk closed or scheduled with a date	Portfolio CTO
First 100 days	The four DORA measures captured for the first time, as a baseline	Engineering lead
Integration	An integration architecture decision (absorb, bridge, rebuild or federate) recorded before the next add-on closes	Operating partner and platform CTO
Separation	A dependency map of every shared system, with the transition services agreement end date against each	Carve-out lead
Capability	One portfolio-wide technical debt score per company, produced by the same method	Operating partner
Capability	A named engineering leadership model with a review date	Board
Capability	AI adoption listed as engineering work with a measure of its own	Portfolio CTO
Reporting	A quarterly engineering report on the same four measures every quarter	Engineering lead

If any row is missing after the first 100 days, that row is the first agenda item at the next board meeting.

Common mistakes

The mistakes below repeat across funds and stages. Each one is avoidable if it is named early.

Treating the diligence report as the plan. A diligence report describes risk. A plan describes work. Findings that are never converted into tasks with owners are findings the company will rediscover in year two, at a worse moment. The BCG survey gives the shape of this: 82 percent of firms track return on investment from digital initiatives and 72 percent track cost savings, but only 11 percent explicitly link digital progress to the exit narrative and 40 percent use a formal digital-maturity score.

Buying the second add-on before deciding the integration architecture. The first add-on can be bridged. The fourth cannot be bridged onto three earlier bridges. The decision about whether acquired products are absorbed, connected, rebuilt or left separate has to be taken once, before the pattern is set by accident.

Adding engineers to a late project. Brooks's law, from Fred Brooks's 1975 book The Mythical Man-Month, states that adding manpower to a late software project makes it later, because new people need ramp-up time and every added person adds communication paths. It is the most-cited rule in software management and the most-ignored one in value creation plans. The scaling engineering teams guide covers when adding people helps and when it does not.

Reporting engineering with a colour. Green, amber and red compress a quarter of work into a single judgement made by the person being judged. Four delivery measures, captured the same way each quarter, give the board a trend it can question.

Putting AI on the plan as a slide. The surveys are consistent that funds expect value from AI and that portfolio companies have little of it running. Mubadala and MGX with Bain reported in January 2025 that 18 percent of portfolio companies were already seeing concrete value from operational AI use cases. The gap closes with engineering work that has a measure, in the same backlog as everything else.

Letting the transition services agreement expire without a cut-over date for every system. A carve-out that still depends on the parent's identity provider on the day the agreement ends is a carve-out that stops working that day.

Confusing a vendor's incentive with the company's. BCG's survey found that 70 percent of the firms it classed as successful use specialised digital boutiques and 59 percent engage strategy firms for transformation planning, while only 45 percent of those successful firms systematically ensure knowledge transfer from external partners to internal teams. Whoever does the work, the company has to end up able to run it.

Timelines and costs that third parties have measured

Most figures circulating about this work are unsourced. The ones below have a named source and a date, and each page in this guide keeps to the same rule.

Hold periods. Bain & Company's February 2026 press release for its Global Private Equity Report says holding periods at exit for buyout funds now sit at seven years, up from an average of five to six years across 2010 to 2021. That is the window in which the technology work has to return.

The growth the deal needs. The same release says a typical private equity investment used to need 5 percent annual EBITDA growth, and that typical deals now need 10 to 12 percent average annual EBITDA growth to generate the same 2.5x return. Bain calls this "12 is the new 5". Software is where much of that growth has to be built.

Deal timelines in carve-outs. Deloitte's 2026 Global Divestiture Survey, analysing 908 divestitures of at least $100 million announced or closed between January 2020 and December 2025, found sign-to-close timelines had lengthened by 6 percent compared with 2020 and could extend to 10 months or more, with a median of three months. The technology separation plan has to fit inside that window.

What engineering leadership costs. The U.S. Bureau of Labor Statistics reports a median annual wage of $175,140 for computer and information systems managers in May 2025, and projects 16 percent employment growth for the occupation from 2025 to 2035. A CTO at a funded company is paid above that median, and the figure is the floor for any comparison of interim, fractional and permanent options.

What technical debt costs in hours. Stripe's 2018 Developer Coefficient survey put the mean developer week at 41.1 hours, with 17.3 hours on maintenance, 13.5 of them attributed to technical debt and 3.8 to bad code. Stripe's own calculation from average developer salaries put the bad-code figure at $85 billion a year worldwide in lost opportunity. Those are Stripe's numbers from Stripe's survey; use them as a scale; a forecast for any one company comes from its own measure.

What AI adoption returns. FTI Consulting's May 2026 survey found 95 percent of funds reporting that AI initiatives met or exceeded their original business case criteria, with revenue acceleration the top priority at 41 percent and talent the primary constraint at 35 percent. Those are self-reported by the funds surveyed.

What DORA measures show about AI in delivery. The 2024 DORA report, announced by Google Cloud in October 2024, estimated that increased AI adoption was accompanied by a 1.5 percent decrease in delivery throughput and a 7.2 percent reduction in delivery stability, with 39 percent of respondents reporting little to no trust in AI-generated code. The 2025 report, from nearly 5,000 technology professionals, found a positive relationship between AI adoption and throughput, a continued negative relationship with stability, and 30 percent reporting little or no trust. A fund that wants AI-written code in its portfolio companies should want the evaluation discipline that closes that stability gap; eval-driven development describes it.

Every cost figure that is not on this list, on any page in this guide, is either a vendor's stated price with the vendor named, or absent.

Where this connects to the rest of our work

The technical due diligence guide is the method before signing, and its report structure and template page is the document this guide expects to receive. Scaling engineering teams covers team growth, which comes up in every value creation plan. AI-generated code to production holds the argument about AI-written code and the security figures behind it.

If you would rather have this run for you than read about it, building with investors and their portfolio companies describes how we work with a fund: a fixed scope, an evaluation suite written from the specification before the code, and a small team because the code is written by AI, with that saving passed into the price.