CASE STUDY
M&A / EXIT PREPARATIONSaaS founder enters diligence with a clean data room — 20% valuation uplift at close
Result
20% uplift in final agreed valuation
Timeline
8 weeks pre-diligence
Sector
B2B SaaS — UAE & MENA
The Context
A UAE-based B2B SaaS founder was preparing to exit a workforce management platform she had built over seven years. The business had strong revenue retention and a growing regional client base, and had attracted credible interest from a Gulf-focused PE fund. Indicative terms had been exchanged. Formal diligence was expected to begin within three months.
The founder had been through a due diligence process once before as a buyer. She knew what a thorough technical review looked like — and she knew her business was not ready for one. The codebase had grown organically over seven years with two different CTO hires. Documentation was sparse. One senior engineer held the institutional knowledge for the core billing integration. And the company had recently started describing itself as "AI-powered" in marketing materials without a clear internal definition of what that meant.
The Challenge
The PE fund's LOI included a clause allowing for a price adjustment of up to 15% downward based on technical diligence findings. The founder's advisors considered this standard but were concerned about what the buyer's technical team would find. Their read was that three categories of risk were most likely to attract scrutiny:
Two critical integrations — the payroll API and the WPS compliance module — were understood and maintained by a single engineer who had already signalled he might not stay post-acquisition.
The platform used a rules-based workflow engine and one GPT-4 prompt for report summarisation. Marketing described it as AI-powered. A technical reviewer would flag the gap immediately.
Three older modules — covering multi-currency invoicing, legacy client data imports, and a deprecated mobile SDK — had no internal documentation. Any question from diligence about those areas would produce a slow, uncertain answer.
Our Approach
We structured the engagement across eight weeks with three phases, each with a defined deliverable the founder could share directly with the buyer if she chose to:
Phase 1 — Weeks 1–3
Pre-diligence audit
We conducted a structured review of the codebase, architecture, team documentation, third-party integrations, and data infrastructure — working from the same framework a buy-side technical advisor would use. We produced a findings register ranked by likely buyer impact, not internal severity, to focus remediation on what would actually move the needle in a diligence conversation.
Phase 2 — Weeks 4–6
Remediation and documentation sprint
We prioritised three workstreams: (1) pair-documentation sessions with the at-risk engineer to capture the payroll API and WPS module logic into structured technical runbooks; (2) an honest AI capabilities summary — documenting exactly what the platform did with AI, what was planned, and what the realistic roadmap looked like, written to replace the ambiguous marketing language with a credible technical narrative; and (3) documentation of the three legacy modules to a level where any competent engineer could understand and maintain them without institutional knowledge.
Phase 3 — Weeks 7–8
Data room preparation and narrative alignment
We worked with the founder and her M&A advisor to organise the technical data room: architecture diagrams, integration dependency maps, the AI capabilities summary, the key-person risk mitigation plan, and a forward-looking technical roadmap structured around value-creation opportunities the buyer could action post-close. We also prepared the founder for the technical management presentation — anticipating the questions most likely to come from the buyer's technical team and preparing clear, honest answers.
The Outcome
The buyer's technical diligence ran for four weeks. The buyer's advisor raised six questions during the process — all of which were answered directly from the data room materials prepared during the engagement. There were no findings that required a price adjustment. The founder entered the management presentation with documented answers and a clear technical story rather than fielding questions defensively.
In the final negotiation, the founder's advisors were able to defend and expand the valuation on the basis of the documented AI roadmap — specifically, the post-acquisition automation opportunities that had been identified and quantified during the engagement. The final agreed price was approximately 20% above the initial indicative terms.
Zero price adjustments from technical diligence — all buyer findings addressed from pre-prepared data room materials
Key-person dependency risk resolved — critical integration logic fully documented before the engineer's notice period
AI narrative rewritten from vague marketing claims to a credible, substantiated capability summary
Three legacy modules documented to maintainable standard — no "black box" risks remaining at close
Final valuation 20% above initial indicative terms, supported in part by documented post-acquisition AI value-creation roadmap
"I came in knowing what the weak points were. What I didn't know was how to present them in a way that didn't become buyer leverage. Polar Frequency helped me control that conversation — and the outcome showed it."
— Founder & CEO, B2B SaaS Platform, UAE
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