since 2015
AI in production at German clients
Small-cap & mid-cap · portfolio in its growth phase
You bought growth.
The reporting cannot prove it.
Together with the CFO of your portfolio companies we first build the KPI system that makes topline, product performance and customer value steerable — and only then the AI building blocks that let the company grow without pulling headcount along proportionally. In this order, because the later stages are not measurable without the first.
For investment teams in small-cap and mid-cap — and the CFOs of their portfolio companies
100+ projects
private equity & family offices
11 years
CFO track record · digital finance
Dedicated CFO advisory practice
for portfolio companies
Google Cloud Premier Partner
certified AI service provider
EU AI Act native
sovereign, hosted in Germany
Who this is for
Two perspectives on the same number.
The investment team and the portfolio CFO are not looking at the same thing — and that is precisely why a shared KPI system is the lever.
01 · Investor side
Comparability across the portfolio.
Without a clear view of the numbers, every board reporting cycle and every steering decision turns into stress and night work: chasing figures, reconstructing variances, deferring decisions. With the shared KPI system you see every company under the same definition — topline, product margin, customer value. What the value creation plan promises is verifiable in the monthly reporting, not merely asserted. And once a process starts, the number holds in the data room.
Investment associate · investment manager · investment principal · investment director
02 · Portfolio company
One KPI system instead of three parallel worlds.
Today the board pack is assembled by hand every month, at the last minute: copying figures out of several systems, aligning definitions, explaining variances — and in the meeting the discussion is about the number instead of the business. We build the structure that carries the monthly close with you: harmonised definitions across the entities, accruals under IFRS, German GAAP or US GAAP — and analysability even out of legacy systems, without replacing the ERP.
CFO · head of controlling · group finance
2×
In the 11th private equity leadership survey by AlixPartners, executives at portfolio companies are twice as satisfied with AI as their investors — and the priorities diverge: investors emphasise topline growth and add-on acquisitions, portfolio leadership emphasises margin management and deleveraging. That is why we always start with the CFO. That is where the number originates that both sides have to rely on.
AlixPartners, 11th Annual Private Equity Leadership Survey · n = 427 · surveyed Oct–Dec 2025 · published 25 March 2026
After the third add-on
Three entities, three definitions of revenue.
The platform grows through acquisitions — and every add-on brings its own KPI logic into the group. A different ERP, a different CRM, a different chart of accounts, a different product and customer hierarchy. The reporting does not become wrong. It becomes incomparable.
01
No shared product dimension
“Revenue per product” means something different in every entity — a portfolio margin cannot be calculated from that.
02
No shared customer dimension
The same customer appears several times. Customer value and churn cannot be added up across the group.
03
History stuck in legacy systems
Contracts, terms and special cases sit in free text and in retired systems — and are therefore missing from every cohort analysis.
04
The close eats the month
Consolidation, accruals and rework tie up the finance team instead of answering steering questions.
69 %
That this is the bottleneck is shown by the demand side itself: in the European Value Creation Survey 2026 by Alvarez & Marsal, data analysis and insight generation is the most frequent AI application in portfolio companies at 69 percent — ahead of operational efficiency at 60 percent and optimisation of the finance function at 55 percent.
Alvarez & Marsal, European Value Creation Survey 2026 · n = 200 private equity investors and portfolio executives across 10 countries · surveyed February 2026 by Statista Q · published 19 May 2026
What that costs at exit
What you cannot prove, no bidder will pay for.
01 · Returns
The acquisition alone no longer carries the return.
Bain & Company analysed buy-and-build platforms: those relying on multiple arbitrage alone achieved an average multiple on invested capital of 1.4 — those with an operational rationale, driving accelerated organic growth or meaningful margin improvement, achieved 2.2. Among the operational levers Bain explicitly names the systems and the monitoring of operational key performance indicators. Bain & Company, “Building a Stronger Buy-and-Build”, Global Private Equity Report 2024
02 · Growth
The bar for EBITDA growth has doubled.
In the 2010s roughly 5 percent annual EBITDA growth was enough to hit the return target over five years. At today’s entry multiples, borrowing costs of 8 to 9 percent and leverage of 30 to 40 percent of the purchase price, it is closer to 10 to 12 percent. Bain calls it “12 is the new 5”. Multiple expansion still contributed more than half of all buyout returns in 2015 — that tailwind is gone: 79 percent of the firms surveyed expect purchase price multiples to stay roughly where they are. Bain & Company, Global Private Equity Report 2026 · GP Outlook 2026 with StepStone, n = 103, surveyed Dec 2025–Jan 2026, published 2 March 2026
03 · Evidence
Value creation has to be visible in EBITDA — not in the deck.
In the EY Global Private Equity Exit Readiness Study 2026, evidencing value creation initiatives in exit EBITDA remains the top challenge for private equity — and the one with the biggest impact on exit outcomes. Timing matters too: sellers who begin 12 to 24 months before the process report the strongest effects; with less than six months of preparation the outcomes are materially weaker. EY, Global Private Equity Exit Readiness Study 2026 · 100 private equity executives and 100 executives of recently exited portfolio companies · surveyed February–April 2026 · published 2 June 2026
Where in the portfolio is the lever?
We do not know that in advance. So we start with a conversation — about the state of your reporting and about what can be steered from it.
Arrange a strategy conversation
The order
First the number that holds. Then the AI that works.
Three stages, not three offerings. Each builds on the previous one — and without the first, the later ones cannot be assessed.
Stage 1 · with the CFO
Reporting structure and KPI system
Steering topline performance, with a drilldown into product performance and customer value. Harmonised definitions across all entities.
Stage 2 · revenue side
SalesAI and SupportAI
Impact on the revenue side with a clear AI strategy: more automation in sales and support, to enable growth while keeping headcount as constant as possible.
Stage 3 · productivity
AI in development and the value stream
AI in development and in the value stream — for more productivity in delivery.
Stage 1 · KPI system
First the number that holds — with your CFO.
We sit down with the CFO of the portfolio company and build the reporting structure and the KPI system that actually steers the business. Not another dashboard — but the definitions, the data lineage and the calculation paths that finance, management and the investment team can rely on together.
Level 1 · topline
Steering topline performance
Revenue, recurring revenue and order intake under one definition across all entities. Forecast and accruals under IFRS, German GAAP or US GAAP included. You see the development while it happens — not six weeks later.
Level 2 · product
Drilldown into product performance
From the topline into the product: contribution margin per product and per product line, across a shared product hierarchy. This makes visible which part of the growth brings margin — and which is only volume.
Level 3 · customer
Drilldown into customer value
From the product view into the customer: customer value, customer lifetime value, cohorts by quarter of entry — with logo churn and revenue churn reported separately. Growth becomes a variable you steer instead of a surprise.
What this produces
Harmonised definitions
One metric, one definition — across all entities and all acquisitions. Including a shared product and customer hierarchy.
What this produces
Legacy systems made analysable
We use AI to unlock contract and customer details from free-text invoices and retired systems too. No ERP replacement required.
What this produces
Close and liquidity kept current
Bottlenecks and headroom become visible early — multi-country VAT and accruals included. You decide ahead instead of in hindsight.
What this produces
Robust in the data room
What you present in the board pack also holds in vendor due diligence — with traceable lineage for every number.
What this creates
Fewer late hours
A single finance reporting standard takes the night work on the board pack off the CFO and the investment principal — and gives both the time to work strategically and as a team.
20 %+
Deloitte Germany quantifies the effect: “A strong CFO influence can increase exit value by 20 percent or more.” As a trend, Deloitte explicitly names consolidated systems, clean KPIs and cloud-based tools. Demand points the same way: optimisation of the finance function is the third most frequent AI application in the portfolio according to Alvarez & Marsal.
Deloitte Germany, “Daten und KI steigern den Unternehmenswert im Private Equity”, 15 July 2026 — Deloitte does not disclose a survey behind the 20 percent; read it as Deloitte’s assessment
+20 %
That matches our experience: in a competitive bidding process, robust KPIs and customer analytics supported a sale value roughly 20 percent higher — because the value contribution could be evidenced instead of merely asserted.
Own statement by niologic GmbH from a completed engagement · anonymised, no inference about any individual portfolio company
Stage 2 · revenue side
Then comes growth that needs no proportional headcount build-up.
Once the KPIs hold, AI becomes a value lever — because its effect is now measurable. We develop the AI strategy along the places where growth costs headcount today: sales and support.
SalesAI · sales
The AI colleague in sales
Integrated straight into the ERP: quotes, availability, terms and customer history. SalesAI qualifies enquiries, calculates and answers in the context of real business data. Your team focuses on closing, not on preparing quotes.
SupportAI · service
The AI colleague in support
Integrated with Jira and other service desks. SupportAI resolves tickets in the context of your documentation, your systems and the customer history — and hands over clean escalations when a human is needed. Support scales with revenue, not with the team.
33 %
The benchmark shows that ownership structure shifts go-to-market efficiency: PE-backed software companies spend 33 percent of revenue on sales and marketing, VC-backed companies 47 percent. Exactly this shift has to be evidenced — through metrics such as revenue per employee and the personnel cost ratio, not through usage statistics of an AI tool.
Benchmarkit, “2025 SaaS Performance Metrics”, data year 2024 · the sample size is not disclosed
Stage 3 · productivity
And then AI in development and the value stream.
AI in development and in the value stream — for more productivity in delivery. Where exactly it applies depends on the business model and is part of the strategy conversation.
Why in this order
AI on top of an unclear KPI system only amplifies the argument about the number.
The scepticism is justified
39 percent expect no impact at all
In the GP Outlook 2026 by Bain & Company, 39 percent of the private equity firms surveyed expect no material financial impact from AI in their portfolio companies in 2026. They see the highest benefit in due diligence and deal sourcing — that is, at fund level, not in the portfolio.
The reason
Not attributable means not defensible
Where contribution margin per product and customer value are not cleanly defined, the effect of an automation cannot be attributed to anyone. And what cannot be attributed gets disputed rather than scaled in the next board meeting.
The consequence
No rollout without a robust business case
At the core of every AI Design Sprint we assess the economics of the use case in a business case — before any investment, with a team from finance, the business function and IT. If it does not pay off, we stop early and cheaply instead of late and expensively.
Group.one · buy-and-build in Europe
22 brands, one definition of revenue.
A European hosting group, grown through acquisitions, with distributed teams across Europe — and with as many KPI logics as entities.
Starting point
A data platform was already in place, but there was no scalable onboarding for new companies and no unified, customer-centric steering across the group.
- Differing KPI definitions and reporting logics per entity
- Contract, product and revenue data scattered across ERP, CRM and in-house systems
- Country-specific GDPR and data residency requirements
- High manual reporting effort, delayed decision-making in portfolio steering
What we did
We created a PMI playbook and integrated 22 brands — the existing companies plus new acquisitions — into an extended data platform.
- KPI definitions harmonised across Europe in workshops and expert interviews with finance, the business functions and data engineering
- Five years of contract, customer, product and revenue history imported from legacy systems and unified
- GDPR-compliant anonymisation with country-specific data residency
- Scalable onboarding for further companies and data sets
Result
Daily, customer-centric KPIs across the entire group — consistent and robust.
- Daily churn, daily new customers, portfolio switches visible
- Customer lifetime value across the group — current and historical
- The CFO receives robust analyses for financial and revenue steering
- The standardised PMI playbook accelerates the integration of further acquisitions
Investor side
Equivia Private Equity
Equivia Private Equity
In private equity in particular we support numerous portfolio companies — and we established a dedicated CFO advisory practice for it, equipping modern CFOs with AI: from the CFO data platform through management and investor reporting to exit readiness.
For Equivia Private Equity we built a CFO data platform for growth and liquidity planning within four weeks after a buyout: data connectors in every subsidiary, twice-daily reconciliation against the gold standard in a medallion data warehouse, data marts for churn analysis and accruals under IFRS and US GAAP, enriched through master data management. The company was thereby optimally prepared for further acquisitions and an external valuation.
Client voices
We help investors, CFOs and CEOs scale their platforms sustainably
These reviews were written in German. The quotes below are faithful English translations; the source link leads to the original.
Christian NietzardHead of Group Controlling
Contabo GmbH · hosting & cloud
Marketing dashboard
“Niologic helped us build a marketing dashboard, particularly with integrating Google and further marketing sources. Niologic also supported us on the complete KPI catalogue — both in the internal alignment and in the technical implementation.”Read the review on Agenturmarkt.de (in German) →
Christian ClevenSVP PMI
Group.one · hosting group Europe
The data experts
“Alexander and team supported us in implementing a global, cross-brand and unified commercial reporting. Very good collaboration as fully fledged team members in a cross-functional set-up.”Read the review on Agenturmarkt.de (in German) →
Jan LöfflerCTO
Synava Group · healthcare IT
Niologic, the partner for AI solutions
“[Niologic] helped us a great deal in an AI Design Sprint to automate our support processes with AI. […] Niologic helped us establish a vocabulary and an ontology of our processes and systems so that the AI systems give better answers.”Read the review on Agenturmarkt.de (in German) →
Ridvan KesginCFO
Velia · hosting & data centres
Niologic — AI analyses for the CFO dashboard
“Niologic has been running our CFO dashboard and finance data warehouse since 2023. On data quality in particular, the AI-based analysis of legacy contracts moved us forward considerably. We look forward to continuing the collaboration!”Read the review on Agenturmarkt.de (in German) →
Rated by our clients
The next board meeting
What it feels like when the number is no longer debated.
01
The board pack arrived without rework.
No question about which definition applies — the discussion was about decisions, not about numbers.
02
Every entity calculated the same way.
The next acquisition was in the reporting within weeks, not quarters — the playbook was in place.
03
Customer value was evidenced over years.
Not as an assertion but as a cohort with history — robust in the data room too.
04
Revenue grew faster than the team.
Automation in sales and support carried the growth that would otherwise have required new hires.
The first step
Strategy conversation on AI in value creation
In the strategy conversation we bring the CIO and CTO perspective together with the CFO's and develop a self-contained AI strategy from it: the revenue benefit is reconciled with the value contribution of each individual use case. We do not know in advance where your lever sits — so we start with a conversation, not with a proposal.
What we talk about
The state of the reporting
Which KPIs exist across the holdings, how they are defined, where the data comes from — and what the monthly close costs today.
What we talk about
The KPI system
Where topline, product performance and customer value diverge today — and what it would take for the numbers to line up across the entities.
What we talk about
The AI strategy
Whether and where SalesAI and SupportAI would carry weight in your holdings — and how the value contribution of each use case measures up against the revenue benefit. No rollout without a sound business case.
Dr Alexander Nichau · founder & managing director
“Sometimes the hold period ends before an AI investment pays off. That is why we talk first about what becomes visible within 18 months.”
This is the concern we hear most often in investment teams — and it is a fair one. A KPI system works quickly, and it works even if not a single AI project follows: it makes the monthly reporting comparable, portfolio steering faster and the data room robust. The AI building blocks sit on top of it, as soon as their effect can be attributed.
And because portfolio data is sensitive: we work sovereign and hosted in Germany, EU AI Act native, safeguarding your trade secrets — including while a process is running.
niologic GmbH · Hürth near Cologne · nationwide
Frequently asked
Frequent questions from investment teams.
Why the KPI system first and not AI straight away?
Because otherwise the effect cannot be attributed. Where contribution margin per product and customer value are not defined consistently, the effect of an automation cannot be attributed to anyone — and what cannot be attributed gets disputed in the next board meeting rather than scaled. In the GP Outlook 2026 by Bain & Company, 39 percent of the private equity firms surveyed expect no material financial impact from AI in their portfolio companies in 2026. That is exactly the gap the first stage closes.
Do you work with the investment team or with the CFO?
With both, but in this order: the first conversation is with the investment team, the implementation starts with the CFO of the portfolio company. That is where the number originates that both sides have to rely on. Without the CFO there is no viable KPI system, and without the investment team there is no prioritisation across the portfolio.
Do we have to replace the ERP for this?
No. We build on the existing systems and use AI to unlock contract and customer details from free text and from retired legacy systems as well. A replatforming during the hold period is rarely the most economical route — and it pushes the benefit into exactly the period in which it is needed.
Do you also carry out due diligence before signing?
Yes, but that is a separate service with its own logic. This page covers the growth phase after acquisition. We describe the assessment of a target’s technology, data maturity and AI before signing on our page about IT and AI due diligence.
What happens if a use case does not pay off?
Then it is not implemented. At the core of every AI Design Sprint we assess the economics of the use case in a business case before any investment is made — with a team from finance, the business function and IT. No rollout without a robust business case. If it does not pay off, we stop early and cheaply instead of late and expensively.
Growth that holds in the data room.
First the number. Then the AI.
The first step is small: a conversation. Then you decide whether and where it continues.
Sources for the market figures cited.
Bain & Company, “Building a Stronger Buy-and-Build”, Global Private Equity Report 2024. —
Bain & Company, Global Private Equity Report 2026, and “Private Equity’s Reality Check: The GP Outlook for 2026” (with StepStone Group, n = 103 investment and investor relations professionals, surveyed December 2025 to January 2026, published 2 March 2026). —
EY, Global Private Equity Exit Readiness Study 2026 (100 private equity executives and 100 executives of recently exited portfolio companies, surveyed February to April 2026, published 2 June 2026). —
Alvarez & Marsal, European Value Creation Survey 2026 (n = 200 private equity investors and portfolio company executives across 10 countries, surveyed February 2026 by Statista Q, published 19 May 2026). —
AlixPartners, 11th Annual Private Equity Leadership Survey (n = 427, surveyed October to December 2025, published 25 March 2026). —
Deloitte Germany, “Daten und KI steigern den Unternehmenswert im Private Equity”, 15 July 2026; Deloitte does not disclose a survey behind the figure of 20 percent or more, so it should be read as Deloitte’s assessment. —
Benchmarkit, “2025 SaaS Performance Metrics”, data year 2024; the sample size is not disclosed.
Own statements. The project descriptions for Group.one and Equivia Private Equity are own statements by niologic GmbH. The client quotes are faithful English translations of published reviews on agenturmarkt.de, which were written in German; each source link leads to the original review.