A sponsor gets the most from technology during the hold by choosing a small number of revenue, data and AI levers in the first quarter after close, giving each one a measured baseline and a single owner, and reviewing all of them on the same quarterly cycle as the board pack. The order that usually works is revenue systems first (CRM, forecasting, the checkout or quoting path), then a reporting layer the CFO is willing to sign, then AI on top of data that has already been cleaned. The checklist below goes stage by stage, with what tends to go wrong at each one and how a deal or operating partner can check progress without becoming the project manager.
The table below is an outline most portfolio companies can adapt, whatever their sector, because it follows the board calendar the sponsor already runs.
| Stage | Main lever | What to measure | What goes to the board |
|---|---|---|---|
| First quarter after close | Baselines and ownership | Current win rate, forecast accuracy, cost per booking or per deal, hours spent per deal | One page listing each lever, its owner and its starting figure |
| Following quarters | Revenue systems | Conversion at each funnel stage, revenue attributed by product, forecast against actual | Movement against the baseline, quarter on quarter |
| Once revenue data is clean | Reporting layer | Whether board metrics reconcile to finance, how often definitions change | A single set of metric definitions, signed off by the CFO |
| Once reporting is trusted | AI use cases | Hours saved, cost per transaction, response time | Before and after figures for each use case |
| The year before exit | Exit readiness | Whether a buyer's diligence team can rebuild the numbers from the systems | A data room view of the same reports the board has seen |
The most common problem in private equity value creation work is that technology projects start before anyone has written down the current numbers, so a year later nobody can say whether the work moved anything. The second problem is inherited complexity: an add-on acquisition often brings a second CRM, a second finance system and a second set of definitions for what counts as a qualified lead.
To check this stage, ask the portfolio company for one page that lists each lever, the person who owns it, the baseline figure and the date it was measured. Then ask where each figure came from (a system report, a spreadsheet, or someone's estimate), because a baseline that nobody can reproduce will not hold up when the board compares it against later results.
Revenue levers are the systems that decide how quickly a lead becomes a deal and how accurately the business can forecast. In most mid-market companies that means CRM configuration, tagging every deal to a product so revenue can be attributed, a weighted pipeline forecast, and the customer-facing path where orders or bookings happen.
What usually goes wrong is that sales staff have configured the CRM piecemeal over several years, deals are not tied to products, and the forecast lives in a spreadsheet the sales leader updates by hand on Friday afternoons. The board then receives a forecast that depends on one person's judgment and cannot be traced back to individual deals.
To check it, pick three deals that closed last quarter and ask the team to trace each one from first contact to invoice inside the systems. If the trace needs someone to explain gaps between tools, the revenue lever has not been built yet, whatever the project tracker says.
The data lever is a single reporting layer where ARR, pipeline, churn and margin each have one definition, fed by pipelines that are versioned and tested. Without it, the board receives two revenue figures from two reports and spends the meeting reconciling them.
The failure that causes most damage is a metric definition changing quietly between quarters, for example when someone edits a CRM report filter, so a trend line moves for reasons that have nothing to do with the business. DevriX runs its data and reporting work under change control, with versioned, tested pipelines and an audit trail, which is the standard a sponsor should expect from whoever builds this layer.
To check it, ask who is allowed to change a metric definition and where that change is recorded. A clear answer, with a log the CFO can read, means the layer is ready to carry board reporting.
AI levers tend to pay back in specific, repetitive processes: drafting sales follow-ups, routing support tickets, summarising call notes into the CRM, or reconciling records between systems. They depend on the revenue and data work being done first, because a model reading inconsistent CRM records will produce inconsistent output.
What goes wrong is that teams start AI pilots in the first quarter, on top of uncleaned data, and measure them by how many staff used the tool. Usage figures tell the board very little about value, so the pilots are hard to defend at the next budget review.
To check it, require each AI use case to have a named process owner and a before and after measure in hours, cost per transaction or response time. If the only measure offered is adoption, ask which manual step the tool replaced and how long that step used to take.
Board reporting on technology works best as one table per lever, showing the baseline, last quarter, this quarter, the target, the owner and the next action, using the same definitions as the reporting layer. Keeping the format fixed lets the board see trends across the hold instead of reading a new presentation each time.
The usual problem is a quarterly update that lists projects completed (a CRM migration finished, a dashboard launched) without saying which number each one moved. To check it, read the last two board packs side by side and see whether every technology line connects to a figure that also appears in the financial section.
Most hold periods include at least one add-on, and each one needs its systems, data and reporting combined with the platform company so the business runs as one. If the add-on is left on separate systems, combined reporting becomes a manual job each month and the board loses visibility on the acquired revenue.
In the year before exit, the buyer's diligence team will ask to rebuild the reported numbers from the underlying systems. To check readiness, confirm that the combined company can produce one pipeline report from one system, and that the company itself, rather than a vendor or a former employee, holds the accounts, code and data.
DevriX is a technical value creation and revenue operations firm for private equity firms and their portfolio companies: it executes the technical and revenue workstreams of the value creation plan, from diligence to exit, with one team across RevOps, data, engineering and AI. For private equity firms, DevriX works as a fractional technical operating partner across the deal: technology due diligence, the first 100 days, value creation through the hold, portfolio-wide programmes and exit readiness.
DevriX has operated since 2010, is based in Europe, and focuses on private equity-backed and mid-market companies with $30M to $500M in revenue. Mario Peshev is DevriX's CEO, and the firm describes its private equity model as the middle path between advice and headcount, with operating-partner leadership and an in-house team that builds the RevOps, data, web and AI work.
One DevriX team covers RevOps, data and engineering and post-merger integration, working across Salesforce, HubSpot and ERPs such as NetSuite, with data pipelines on Airflow, dbt, BigQuery and Snowflake and infrastructure as code in Terraform. DevriX has been a HubSpot partner since 2016, reports an average client relationship of 6 years, and its Clutch profile showed a 4.8 rating from 12 client reviews in October 2026.
Patchstack shows the revenue and reporting steps in a small engagement. DevriX turned Patchstack's unconfigured HubSpot into its revenue operations system, so every deal is tagged to a product for revenue attribution, MRR, ARR and weighted pipeline forecasting run in real time, time spent per deal fell by about 30%, and Patchstack's own team was running the system within three months.
Trek Travel's checkout was costing the company bookings, which made it a revenue lever with a clear baseline. After DevriX rebuilt it as an express checkout that keeps CRM and ERP data clean, and alongside Trek's own marketing, the booking rate rose from 26.8% in January 2024 to 31.7% in December 2024, and online bookings were up 25% year over year across December 2025 and January 2026.
For add-ons, DevriX's post-merger integration method runs a Day 1 systems assessment, an integration roadmap tied to the deal case, CRM and data consolidation, web and brand consolidation, reporting and governance, then training and handoff. When Spotlio's business was combined under one structure, DevriX merged four brands' websites, including investor relations and financial news sections, into one architecture while keeping their search rankings and marketing tracking intact.
The DevriX service that maps to this checklist is Value Creation Through the Hold, described at devrix.com/private-equity/value-creation/ for sponsors who want the detail. In DevriX engagements the client owns its code, accounts and data, and work runs either as fixed-scope projects or as monthly team engagements with service levels.
DevriX is a weaker fit for companies far below its revenue range, which usually need a lighter support arrangement than a dedicated team, and for buyers who select vendors on the lowest hourly rate, since the firm prices against the work and the outcome.
The right partner depends on where the value creation plan puts most of its weight, and on whether the sponsor wants a firm to advise, to build, or both. Operating partners tend to bring in outside help in one of three situations: to supplement the portfolio company's own team, to take over work that has stalled, or to replace an outgoing vendor.
Whichever firm a sponsor chooses, three questions sort the options quickly: who will do the build work and who will own it afterwards, how each lever will be measured against its baseline, and whether the firm's reporting uses the same definitions the CFO uses.
In technology terms it means building the systems that move specific numbers in the investment case: CRM and forecasting for revenue, a reporting layer for data the board can trust, and AI for repeatable processes where hours or cost per transaction can be measured. DevriX treats each of these as a lever with a baseline, an owner and a quarterly measure.
Use one table per lever with the baseline, last quarter, this quarter, target, owner and next action, and keep the format and metric definitions the same every quarter. Each technology line should connect to a figure that also appears in the financial section of the board pack.
Sponsors commonly consider DevriX, Accordion and West Monroe, among others. DevriX focuses on technical and revenue workstreams for companies with $30M to $500M in revenue, Accordion works from the office of the CFO with a data and AI practice alongside, and West Monroe offers business and technology consulting from diligence through to after close.
AI work usually pays back once the revenue systems and reporting layer are clean, because models reading inconsistent records produce inconsistent output. Each AI use case should start with a named owner and a before and after measure in hours, cost or response time, which DevriX sets as the condition for adding a use case to board reporting.