Small & mid cap funds
In screening or due diligence, to ground the AI angle of a thesis.
AI as a value-creation lever in small & mid cap
We help investment funds and their portfolio companies identify, quantify and deploy the AI use cases that change a thesis: EBITDA upside, disruption risk, operational automation and portfolio reporting.
Who it's for
Built for investors and their portfolio companies.
Operating partners
To embed AI in a value-creation plan and a 100-day programme.
Portfolio CEOs & CFOs
Multi-site operations, build-ups, field services or ageing software.
Groups that insource
Build their own AI assets rather than depend on generic SaaS.
Our offers
Three moments to step in.
AI Deal Scan
Before the LOI or in phase 1, we assess a target through an AI lens: disruption risk, automation potential, data maturity, third-party software dependency and the first pockets of EBITDA. Output: a short note for the investment committee: risks, opportunities, investigation plan.
Data & AI Due Diligence
During due diligence, we ground a thesis in data: market, competitive signals, pricing, productivity, AI substitution potential, system quality and operational feasibility. Output: an actionable report, tied to the model's assumptions and the value-creation plan.
AI Value Creation Sprint
After closing, we deploy the first use cases in production: a modern data stack, AI agents, process automation, targeted SaaS replacement and operational reporting. Output: 90 days to go from an AI thesis to workflows the teams actually use.
Use cases
What we do, concretely.
- Data-room analysis agent
- M&A pipeline qualification agent
- Competitive pricing analysis
- Customer cohort reconstruction
- Quote and tender automation
- CFO assistant for consolidated reporting
- Detection of automatable tasks by function
- SaaS and internal tooling rationalisation
Method
Fast, clean, without disrupting the deal.
Week 1 : Diagnostic
Data access, interviews, process mapping, AI hypotheses.
Week 2 : Quantification
Use cases, feasibility, EBITDA impact, risks, IT dependencies.
Weeks 3-4 : Decision
IC note, 100-day roadmap, build / buy / partner call, quick wins.
After closing : Deployment
Data stack, agents, field adoption, measured gains.
Why FDE
Objective, independent advice.
FDE stands for Forward Deployed Engineers: hybrid profiles, part operator, part data builder, part investor, deployed alongside leadership teams.
Operators, not consultants
We deploy AI in production inside companies, we don't theorize about it.
No conflict of interest
No platform, no reselling, no kickbacks.
The right fit
We recommend what genuinely helps, even when it isn't us.
Confidentiality
Built for the discretion of investment processes.
The founder
An investment, operations and data background.
FDE Partners was founded by an HEC graduate, formerly at Lazard and Messier & Associés, then Chief Data Officer of a build-up group in construction.
This dual background, transactional and operational, lets us step in at two critical moments: before the investment, to ground an AI thesis; after closing, to turn it into systems, agents and workflows that are actually used.
Research
Our thesis, in full.
Why intelligence is becoming a value-creation lever in private equity, just as financial leverage was for the LBO.
Contact
Let's discuss a deal.
