Developers & Builders
AI Notes for Technical Due Diligence
Run technical due diligence with a searchable record of every codebase review, architecture discussion, and risk finding. AI notes keep it all organized.
Technical due diligence is one of the highest-stakes knowledge work there is. In a compressed timeline -- usually two to four weeks -- you need to evaluate a company's entire technology stack, engineering team, development practices, architecture decisions, security posture, and technical debt. Your assessment directly influences investment decisions worth millions.
The volume of information is staggering. Codebase walkthroughs, architecture diagrams, security audit reports, team interviews, infrastructure documentation, CI/CD pipeline reviews, data architecture discussions, scalability analysis. Each generates notes that need to be synthesized into a coherent assessment.
Most due diligence reports are assembled at the end of the process from the analyst's memory and whatever notes they managed to take during intense sessions. Critical details slip through. Nuances from early meetings are forgotten by the time the report is written. The connection between something a developer said in interview two and a finding from the codebase review in week three goes unnoticed.
AI notes change this by creating a comprehensive, searchable record throughout the process that makes the final synthesis dramatically more accurate and complete.
Structuring the Engagement
Create a collection for the due diligence engagement. Within it, organize notes by category:
Architecture -- system design, infrastructure, scalability discussions
Codebase -- code quality, technical debt, testing practices
Team -- engineering interviews, organizational structure, hiring practices
Security -- vulnerability assessments, compliance, data handling
Operations -- deployment, monitoring, incident response, SLA performance
Business/Product -- product-technology alignment, roadmap feasibility
Every meeting, every review session, every document analysis gets captured in a note and tagged to the relevant category and the engagement collection.
Capturing Sessions in Real Time
Due diligence interviews and walkthroughs move fast. The CTO is walking you through the architecture at speed. The lead engineer is explaining a complex data pipeline. The VP of Product is describing the roadmap and you need to assess its technical feasibility.
Record these sessions with Voice Mode. Full capture means you can be fully present -- asking probing follow-up questions instead of furiously typing. After the session, Mem generates a structured note with key discussion points, technical details, and any red or yellow flags you noted.
The voice capture is especially valuable for the subtle signals: hesitation when asked about test coverage, enthusiasm when discussing a new architecture, vague answers about the security audit timeline. These signals inform your assessment but are impossible to capture in typed notes taken during the conversation.
The Rolling Assessment
Instead of waiting until the end to form your assessment, use AI notes to maintain a rolling view throughout the engagement. Periodically ask Mem Chat:
"Based on my notes so far, what are the emerging themes -- both positive and concerning -- about this company's technology?"
"What areas haven't I explored yet based on what I've documented?"
"Are there any contradictions between what different people have told me?"
These mid-process checks are powerful. They surface contradictions early (the CTO says deployment is fully automated; the engineer mentions manual steps). They identify blind spots (you've covered architecture and codebase but haven't discussed monitoring). They help you allocate your remaining time to the highest-value areas.
Risk Cataloging
Technical risk is the core deliverable of due diligence. As you identify risks throughout the engagement, tag them clearly in your notes:
"RISK: Single points of failure in the payment processing pipeline. Currently runs on a single instance with no automatic failover. Team acknowledges this and has it on the roadmap, but no timeline."
"RISK: Key person dependency on the lead architect who designed the core data model. No documentation of the design decisions. If this person leaves, significant institutional knowledge goes with them."
When assembling the final report, ask Mem:
"List all risks I've identified during this due diligence, categorized by severity and area."
The AI compiles every risk mention across all your notes into a structured risk register. This is more thorough than any manually assembled list because it draws from every session, not just what you remembered to include.
Team Interview Synthesis
Engineering team interviews are a critical component of due diligence. You're assessing not just technical skills but culture, morale, retention risk, and leadership quality. Each interview generates insights that are individually informative but collectively revealing.
After completing several interviews, ask Mem:
"What patterns emerge across the engineering team interviews? Are there consistent concerns or strengths?"
"How do different team members describe the engineering culture, and do their descriptions align?"
"What does the team say about technical debt, and does it match what I've seen in the codebase review?"
Cross-referencing interview insights with technical findings produces the kind of nuanced assessment that distinguishes thorough due diligence from superficial reviews. If three engineers independently mention that the testing culture is weak, and your codebase review confirms low test coverage, the finding is much stronger.
The Final Report
The due diligence report is where everything comes together. Instead of writing from memory, ask Mem to help with the synthesis:
"Create a structured summary of this due diligence covering architecture, code quality, team assessment, security, operations, and key risks. Include specific evidence from my notes for each finding."
The AI produces a draft that's grounded in documented evidence, not reconstructed impressions. You refine it, add your expert judgment, and deliver a report that's more thorough and more defensible than one assembled from memory alone.
For ongoing investment monitoring -- tracking whether a portfolio company is addressing the risks identified during due diligence -- the engagement collection remains a reference. Six months later, you can ask: "What did we identify as key risks during due diligence, and have any been mentioned in subsequent conversations?" This connects the assessment to ongoing oversight. For related workflows, see our guide on how developers use AI notes for side projects and technical work.
Getting Started
Create a collection for the engagement with sub-categories for each area
Record every session with Voice Mode -- be present, not note-taking
Tag risks clearly as you identify them
Mid-process, ask Mem for a rolling assessment to guide remaining time
For the final report, ask Mem to synthesize across all notes with evidence
Keep the collection as a reference for ongoing portfolio monitoring
Technical due diligence is a compression problem: massive amounts of information processed in a short time, with high-stakes conclusions. AI notes ensure that nothing captured is ever lost, and that the final assessment reflects every piece of evidence gathered -- not just the pieces you happened to remember at report-writing time.
