Short answer: technical diligence is a documentation problem before it is a technical one. The review itself is predictable — repository health, architecture, infrastructure, security, cost, key-person risk, and the honesty of the roadmap. What varies is how long it takes, and that is almost entirely a function of how quickly you can produce evidence. A prepared data room turns a six-week slog into a two-week review and, more importantly, keeps the narrative in your hands.
Diligence moves at the speed of the documents you can produce.
What the reviewer is actually doing
A technical reviewer is not auditing you for its own sake. They are pricing risk. Every artifact you provide is a way of saying "this risk is understood, bounded, and owned". The absence of an artifact is not neutral — it is read as an unknown, and unknowns get priced conservatively. For the reviewer's side of the same exercise, see our technical due diligence overview.
So prepare artifacts with two columns in mind: the question each one answers and the risk it retires. That framing also tells you what to skip: documents nobody asks for and that retire no risk are just noise.
| Artifact | Question it answers | Risk it retires |
|---|---|---|
| Repository access and a code walkthrough | Is the code maintainable by someone other than its authors? | Key-person dependency, hidden rewrite cost |
| Architecture and data-flow diagram | What are the components, boundaries, and trust zones? | Integration fragility, compliance gaps |
| Infrastructure inventory and IaC | What runs where, and can it be rebuilt? | Recovery risk, undocumented manual setup |
| Cost baseline and trend | What does it cost to run at current and 10x scale? | Margin surprise, unit-economics risk |
| Security questionnaire and evidence | How are access, secrets, and data handled? | Breach exposure, enterprise-sales blockers |
| Dependency and license inventory | What third-party code and obligations are present? | Legal exposure, upgrade debt |
| Test and CI evidence | How do you know a change is safe? | Regression risk, slow delivery |
| Incident history and postmortems | How do you behave when things break? | Operational maturity risk |
| Key-person map and bus factor | Who can do what, and what is documented? | Concentration risk |
| Known-issues list | What is already broken or deferred? | Surprise findings late in the process |
| Roadmap and technical debt plan | What does the next 12 months cost and require? | Plan credibility |
Prepare the repository before anyone looks at it
Reviewers form an impression in the first hour, and the repository is where they start. Practical preparation:
- Grant read access to a clean account — not a personal account, not an account with write access. Access that arrives late is the single most common cause of a slow start.
- Write a two-page orientation: what the product does, how the code is organised, how to run it locally, how to deploy, and what is unusual. If it takes a reviewer two days to run the system, they will assume it takes two weeks.
- Make the history honest. Do not rewrite or squash history to hide churn. Reviewers look at commit patterns, branch lifetimes, and who touched what. An obviously curated history reads as concealment.
- Fix the embarrassing basics first: a README that is accurate, no committed secrets, no commented-out blocks of dead code in critical paths, and a CI pipeline that actually runs.
- Have the numbers ready: lines of code by area, test coverage where it exists, build and deploy duration, dependency count, and average time from commit to production.
One architecture diagram, one page
The diagram reviewers value most is not beautiful. It shows the components, the data stores, the external services, and the trust boundaries — and it fits on one page with a legend. Prepare three versions of the same picture at different zoom levels: the system context, the application components, and the data model for the core entities.
Then add a short data-flow narrative for personal and regulated data: where it enters, where it is stored, who can read it, how long it is retained, and how it is deleted. Most security findings in diligence are really data-flow findings, and a clear narrative lets you answer them once instead of five times.
Security: answer the questionnaire with evidence
You will be asked to complete a security questionnaire, usually a long one. Two pieces of advice make this survivable:
- Answer in facts, not adjectives. "MFA enforced for all production access via SSO; exception list is empty" beats "we take security seriously".
- Attach evidence per control family. Access reviews, backup restore tests, dependency scanning output, incident response runbook, and encryption details. Evidence is what converts a claim into a control.
Prepare the areas that generate the most follow-up: identity and access (who can reach production, and how is that reviewed), secrets management, vulnerability management, backup and restore (with a real restore test), logging and monitoring, and the process for handling a suspected breach. If you have a known gap, disclose it with a remediation plan and a date. A disclosed gap with a plan is a manageable finding; a discovered gap is a credibility problem.
Cost, contracts, and concentration risk
Three areas are systematically underestimated by founders preparing for diligence:
- Cost. Provide monthly infrastructure and third-party cost by category, with the drivers and the unit economics — cost per tenant, per order, per model call, or per active user. Reviewers extrapolate. If cost grows superlinearly with usage, show that you know it and what the plan is.
- Contracts. List every third-party dependency that has a contract, a commitment, or a renewal date, plus the terms that constrain you: exclusivity, data processing, minimum spend, and termination rights.
- Concentration. Identify single points of failure across vendors, models, regions, and people. For each, state the mitigation and whether it has been tested. "We could switch providers in a week" is a claim; "we ran one workload on the secondary provider in March" is evidence.
Worked example: preparing a data room in three weeks
Consider an illustrative case: a twelve-person B2B SaaS company with a five-year-old codebase, one main product, a small engineering team, and three weeks before the first diligence call. This is a composite, not a client story.
Week one goes to inventory and access: create a read-only reviewer account, verify repository permissions, export the infrastructure list, gather cloud and vendor invoices for the last twelve months, and assemble the dependency and license report. Most of the effort is finding things, not writing them.
Week two produces the narrative documents: the three-level architecture diagram, the data-flow narrative for personal data, the known-issues list, and the incident history with two postmortems. The team writes the known-issues list candidly — eight items, each with an impact statement and an owner.
Week three is the security questionnaire, the cost baseline, and a dry run. The dry run matters most: someone who is not on the engineering team reads the data room and asks the questions an outsider would ask. That surfaces the gaps — a missing restore test, a diagram that omits the analytics pipeline, a cost number that does not reconcile with the invoice — while there is still time to fix them.
The review starts on schedule. The findings are real, but they are the same findings the team had already written down, which means the conversation is about remediation sequencing rather than about what else might be hidden.
Limitations and assumptions
This playbook assumes a conventional software business with a codebase, infrastructure, and third-party services; heavily regulated products, hardware, or businesses whose value is a dataset need additional preparation in their own domains. It assumes the engineering team can spend real time on preparation — roughly a week of one senior engineer plus a week of a lead for a company of this size, and less if the documentation habit already exists. It also assumes that disclosing known issues early is culturally acceptable in your organisation; teams that hide findings usually lose more value in credibility than they save in valuation. Finally, this is not legal advice: the representations you make in a data room have contractual weight, so legal counsel should review anything you publish as a statement of fact.
Working with CodeAustral
We prepare companies for technical diligence and act as the technical counterpart during the review: architecture and data-flow documentation, infrastructure inventory, security evidence, and a remediation plan for the findings. If you have a process starting soon, send a brief with the timeline and see the systems we have built while we scope the data room with you.
Frequently asked questions
How early should we start preparing for technical diligence?
Start the inventory two to three months before you expect a process, and the narrative documents about three weeks out. The inventory is the long pole because it involves finding credentials, invoices, and old decisions. Companies that begin a week before the first call usually produce documents that raise more questions than they answer.
Do we have to fix everything before diligence?
No, and attempting to do so is a mistake. Reviewers expect a mature codebase to have known issues. What they evaluate is whether you understand them, whether they are bounded, and whether the remediation plan is credible. A rushed fix that destabilises production during diligence is far worse than a documented gap.
What if our codebase is genuinely in bad shape?
Say so, with a plan. A clear statement — "the billing module is the highest-risk area; here is the migration plan, the estimate, and the person who owns it" — converts an open-ended worry into a priced, scheduled project. Concealment converts it into a discount or a dead deal.
Who should present the technical review?
The person who owns the architecture and can answer follow-up questions without translating through a manager. Founders who present the technical story without an engineer in the room lose credibility quickly on detail, and the reviewer will ask to speak to the engineer anyway.