Pre-Deal CTO: Technical Diligence That Shapes the Investment Thesis
Table of Contents
- Why Pre-Deal CTO Diligence Matters
- The Cost of Getting Technical Diligence Wrong
- What a Pre-Deal CTO Actually Does
- The Four Pillars of Technical Diligence
- Building Your Technical Diligence Timeline
- Red Flags That Kill Deals (or Should)
- Turning Diligence Into Post-Close Value
- How to Hire a Pre-Deal CTO
- Real-World Examples and Outcomes
- Next Steps: Building Your Diligence Plan
Why Pre-Deal CTO Diligence Matters
You’re looking at a target. The revenue story is compelling. The market timing is right. The founding team has pedigree. But you haven’t brought in someone who can actually read the code, interrogate the architecture, and tell you whether the tech thesis holds up under pressure.
This is where most deals go sideways.
According to Real Deals 2025: Where PE Diligence Is Heading, technology now factors into nearly 60% of post-close value destruction in mid-market deals. Not market risk. Not team turnover. Tech debt, architectural decisions made in haste, and infrastructure that can’t scale.
A pre-deal CTO—a fractional technical leader brought in specifically to audit the investment thesis—changes this. They’re not there to find gotchas. They’re there to:
- Pressure-test the product roadmap against the actual codebase and team capacity
- Size the value-creation upside by identifying quick wins and multi-quarter engineering initiatives
- Price integration risk into the model—what will it cost to migrate this system, plug it into your platform, or consolidate it with a roll-up target?
- De-risk the tech narrative before you commit capital
For private equity firms, strategic buyers, and growth-stage investors, this is the difference between a 2x return and a write-down. For founders and their boards, it’s the difference between a deal that closes on time and one that blows up in data rooms.
The best part: you don’t need to hire a CTO full-time. You bring in someone with 15+ years of engineering leadership, let them spend 4–6 weeks in the codebase, and walk away with a pressure-tested thesis.
The Cost of Getting Technical Diligence Wrong
Let’s talk about what happens when you skip technical diligence or do it badly.
We’ve seen deals where:
- A fintech platform was built on a monolithic Rails app that couldn’t be horizontally scaled. Post-close, the acquirer spent 18 months re-architecting for multi-tenancy. Cost: $2M+ in engineering and delayed revenue.
- A SaaS business had zero infrastructure-as-code, no automated testing, and deployments that took 6 hours and required a specific engineer to be present. Integration into the buyer’s platform took 3x longer than forecast.
- An AI-first company had trained models but no MLOps pipeline, no feature store, and no observability. The models were working in staging but drifting in production. The buyer inherited a liability, not an asset.
- A healthcare platform had built on HIPAA-adjacent architecture but never actually completed a compliance audit. Post-close, they couldn’t sell into enterprise until they spent 6 months and $300K on security remediation.
These aren’t edge cases. Top Technical Due Diligence Firms for SMBs found that 40%+ of SMB acquisitions have unpriced technical risk that emerges post-close.
The reason: most diligence is done by finance, legal, and commercial teams. They’re good at their jobs. But they can’t read code. They can’t assess whether the engineering team is senior or junior. They can’t tell you if the architecture will support 10x growth or collapse at 2x.
A pre-deal CTO fills this gap. They speak the language of both engineers and investors. They can translate technical risk into business impact.
What a Pre-Deal CTO Actually Does
A pre-deal CTO is not a full-time hire. They’re a fractional technical leader—typically someone with 15+ years of engineering leadership experience—brought in for 4–8 weeks to audit the investment thesis.
Here’s what they actually do:
Week 1–2: Intake and Architecture Review
They start by understanding the business. What’s the revenue model? What’s the growth thesis? What’s the acquirer’s integration plan? Then they dive into the codebase.
- Code audit: Walk the main product, understand the architecture, identify technical debt and architectural patterns.
- Infrastructure review: Understand the deployment pipeline, observability, disaster recovery, and cost structure.
- Team assessment: How senior is the engineering team? What’s the bus factor? Can they execute the post-close roadmap?
Week 2–4: Deep Dives and Risk Mapping
Now they’re pressure-testing specific hypotheses:
- Scalability: Can this system handle 10x growth? What breaks first? What’s the cost of fixing it?
- Security and compliance: Is the system audit-ready? What’s the gap between current state and SOC 2 / ISO 27001?
- Data and ML: If there’s AI in the thesis, is there a real ML platform or just notebooks? Can models be retrained? Is there observability?
- Integration feasibility: How hard will it be to plug this into the buyer’s platform? What’s the real timeline and cost?
Week 4–6: Thesis Validation and Roadmap Sizing
They come back to the investment thesis:
- Value-creation opportunities: What quick wins can be shipped in the first 90 days? What’s a realistic 12-month roadmap?
- Risk quantification: What’s the probability of major technical problems? What’s the cost if they occur?
- Integration roadmap: If this is an acquisition, what’s the real post-close engineering plan?
Week 6–8: Board-Ready Report and Recommendation
Final deliverable: a technical diligence report that:
- Validates or challenges the business thesis
- Sizes value-creation opportunities with confidence levels
- Prices technical risk into the deal model
- Recommends post-close actions and engineering priorities
The report is written for investors, founders, and boards—not engineers. It answers: “Should we do this deal? At what price? And what do we need to fix first?”
The Four Pillars of Technical Diligence
Every technical diligence process should cover four areas. Miss one, and you’re flying blind.
Pillar 1: Product and Engineering Capability
Can the team actually build what they say they’ll build?
What to assess:
- Product-market fit: Is the product solving a real problem? How sticky is it? What’s the churn rate?
- Engineering velocity: How fast can they ship? What’s the deployment cadence? Are they shipping features or firefighting?
- Technical depth: Do they understand their own system? Can they explain architectural decisions? Or are they just maintaining legacy code?
- Team composition: What’s the ratio of senior to junior engineers? Who are the critical people? What’s the risk if they leave?
Red flags:
- Engineering team is entirely junior (< 3 years experience)
- No one can explain why they chose their tech stack
- Deployment is manual and takes > 2 hours
- No automated testing or CI/CD pipeline
- Code review process is non-existent or purely ceremonial
Green flags:
- Deployment is automated and takes < 15 minutes
- Test coverage is > 70% and meaningful (not just line coverage)
- Engineering team has 50%+ senior engineers (7+ years)
- Code review is rigorous and asynchronous
- They’ve made intentional architectural choices and can defend them
Pillar 2: Architecture and Scalability
Will the system support the growth thesis?
What to assess:
- Monolith vs. distributed: Is it a monolith or microservices? If monolith, can it be modularised? If microservices, is there unnecessary complexity?
- Data architecture: How is data stored, processed, and served? Is there a data warehouse? Can they run analytics without breaking production?
- Real-time vs. batch: What’s the latency requirement? Can they meet it with current architecture?
- Cost structure: What’s the cost per customer? Does it scale linearly or exponentially?
- Capacity planning: Have they modelled growth? Do they know when they’ll hit limits?
Red flags:
- Monolith with no clear module boundaries
- Database is the bottleneck and they have no plan to address it
- Infrastructure costs are growing faster than revenue
- No capacity planning or performance monitoring
- Single points of failure (one database, one server, one person who understands the system)
Green flags:
- Clear service boundaries (even if it’s a modular monolith)
- Horizontal scalability is built in from the start
- Infrastructure cost per customer is declining or flat
- They’ve load-tested and know their limits
- Observability is built in (metrics, logs, traces)
Pillar 3: Security and Compliance
Can they pass the audit and protect customer data?
What to assess:
- Security posture: Do they have a security policy? Are secrets rotated? Is access controlled?
- Compliance readiness: Are they SOC 2 ready? ISO 27001? GDPR? HIPAA (if applicable)?
- Data handling: How is customer data encrypted? At rest and in transit? Who has access?
- Incident response: Have they had a breach? How did they respond? What did they learn?
- Vendor risk: What third-party services do they rely on? Are those vendors audited?
Red flags:
- No security policy or it’s aspirational
- Secrets are hardcoded or stored in version control
- No encryption at rest or in transit
- Access control is based on trust, not roles
- They’ve never done a security audit
- They can’t explain their compliance status
Green flags:
- They have a documented security policy and it’s actually followed
- Secrets are rotated regularly and stored in a vault
- Encryption is standard (TLS in transit, AES at rest)
- Access is role-based and logged
- They’ve passed or are on track to pass SOC 2 / ISO 27001
- They have incident response procedures and they’ve tested them
For security-focused diligence, M&A Due Diligence Challenges: Pre-Deal IT Due Diligence outlines how to assess security posture and compliance readiness across infrastructure, software, and operations.
Pillar 4: Operations and Integration Feasibility
Can you actually integrate this system post-close?
What to assess:
- Infrastructure and deployment: Where does it run? Cloud or on-prem? What’s the deployment process? Can you automate it?
- Monitoring and observability: Do they know what’s happening in production? Can you see errors, latency, and resource usage?
- Documentation: Is there runbooks? Architecture diagrams? Or is knowledge in people’s heads?
- Dependencies and integrations: What other systems does it depend on? Are those dependencies documented?
- Migration and data: If you’re migrating customers, how hard is that? What’s the data model? Can you export and import?
Red flags:
- Infrastructure is undocumented
- No monitoring or observability
- Documentation is non-existent or outdated
- Critical knowledge is in one person’s head
- Integrations are brittle (tight coupling, no versioning)
- Data export/import is manual and error-prone
Green flags:
- Infrastructure is defined as code (Terraform, CloudFormation)
- Comprehensive monitoring and alerting
- Documentation is current and accessible
- Knowledge is distributed across the team
- Integrations are well-defined and versioned
- Data can be exported and imported reliably
Building Your Technical Diligence Timeline
Timing matters. Bring in a pre-deal CTO too early, and they’re sitting idle. Too late, and you’re in a data room with no time to dig deep. Here’s the right timeline:
Pre-LOI (Letter of Intent)
Timeline: Weeks 1–2
If you’re serious about a target, bring in a CTO for a quick assessment. This is a 2–3 day deep dive:
- Code and architecture review
- Team assessment
- Preliminary risk flags
Deliverable: A 1-page technical summary. “Green light, proceed with diligence” or “Red flags, negotiate hard or walk.”
PADISO offers a AI Quickstart Audit that works similarly—a fixed-scope, fixed-fee 2-week diagnostic that tells you where you actually are and what to ship first.
Post-LOI, Pre-Close (Data Room Phase)
Timeline: Weeks 3–8
Now you have exclusivity and a real timeline. This is where the deep work happens.
- Full codebase and architecture review
- Scalability and performance analysis
- Security and compliance audit
- Integration feasibility study
- Value-creation roadmap
Deliverable: A 30–50 page technical diligence report with:
- Executive summary (1 page)
- Findings by pillar (architecture, security, operations, team)
- Risk quantification and pricing
- Post-close roadmap and quick wins
- Board-ready recommendation
Post-Close (First 90 Days)
Timeline: Weeks 1–12
The CTO transitions from diligence to execution. They become the technical lead for integration, working with the target’s engineering team to:
- Execute the post-close roadmap
- Ship quick wins
- De-risk the critical path items
- Build the technical foundation for the next phase of growth
Red Flags That Kill Deals (or Should)
Some technical issues are fixable. Some are deal-killers. Here’s how to distinguish them.
Absolute Deal-Killers
If you see these, walk or price them in heavily:
1. Unresolvable Scalability Limits
The system is built on a technology or architecture that fundamentally cannot scale. Examples:
- A relational database that’s already at capacity with no sharding strategy
- A monolith with such tight coupling that any change breaks everything
- A system that’s already hitting latency limits and there’s no path to improve
Fix cost: Months of re-engineering. Millions in engineering spend.
2. Regulatory or Compliance Liability
The system is operating outside of regulatory requirements and fixing it is non-trivial. Examples:
- A healthcare platform that’s not HIPAA-compliant and the architecture makes it hard to achieve
- A financial services platform that can’t pass SOC 2 audit without fundamental changes
- A GDPR-regulated business with no data residency controls
Fix cost: Months of remediation. Potential legal liability.
3. Vendor Lock-In or Dependency Risk
The business is entirely dependent on a vendor or technology that:
- Could be acquired or shut down
- Is proprietary and you can’t migrate away
- Has pricing power and can extract margin
Examples:
- A platform built entirely on a proprietary vendor’s API with no fallback
- A business that depends on a single person’s expertise (they built it, no one else understands it)
- A system that uses a deprecated or unsupported technology
Fix cost: Rewrite or accept the dependency.
4. Undocumented or Unmaintainable Code
The codebase is so poorly documented and so tightly coupled that no one can modify it without breaking things. Examples:
- No tests, so changes are terrifying
- Code is written in a style that’s hard to understand
- No one can explain why things are the way they are
- Bus factor of 1 (if one person leaves, the system is unmaintainable)
Fix cost: Months of refactoring or rewrite.
Manageable Red Flags
These are fixable, but they increase risk and cost. Price them in:
1. Technical Debt
The system has accumulated shortcuts and workarounds. It still works, but it’s fragile. Examples:
- Monolithic architecture that needs to be modularised
- Legacy ORM that’s slow and should be replaced
- Database schema that’s not normalised
Fix cost: 3–6 months of engineering effort. $500K–$2M depending on scope.
2. Missing Observability
They don’t know what’s happening in production. No logs, no metrics, no traces. Examples:
- No error tracking (they find out about bugs from customers)
- No performance monitoring (they don’t know latency until it’s a problem)
- No alerting (they discover outages when revenue drops)
Fix cost: 4–8 weeks to instrument the system. $100K–$300K depending on scope.
3. Weak Engineering Team
The team is junior or inexperienced. They can execute on a roadmap, but they can’t architect or lead. Examples:
- Average experience is 2–3 years
- No senior engineers to mentor and lead
- High turnover (people leave after 1–2 years)
Fix cost: Hire senior engineers post-close. $200K–$500K per senior hire.
4. Missing Security Controls
The system doesn’t have basic security controls. It’s not breached, but it’s exposed. Examples:
- No encryption in transit or at rest
- Access control is based on trust, not roles
- No audit logging
- Secrets are hardcoded
Fix cost: 6–12 weeks to implement controls. $200K–$500K depending on scope.
For a comprehensive framework, How to Run a Technical Due Diligence? walks through structuring diligence across product, engineering, and organisational dimensions.
Turning Diligence Into Post-Close Value
Diligence is only valuable if you act on it. Here’s how to translate findings into post-close value.
Step 1: Build a 90-Day Roadmap
Before close, the CTO should have identified 3–5 quick wins that can be shipped in the first 90 days. These are:
- High impact: They move the needle on revenue, cost, or risk
- Low effort: They can be shipped in 2–4 weeks
- De-risking: They reduce technical risk or unlock the next phase
Examples:
- Implement observability (logs, metrics, traces) so you can actually see what’s happening
- Automate deployment so the team can ship faster
- Implement basic security controls (encryption, access control, audit logging)
- Modularise a critical component so it can scale independently
- Migrate to a new database or infrastructure to fix a scalability bottleneck
Step 2: Transition the CTO to Execution
Post-close, the CTO doesn’t disappear. They become the technical lead for integration. They:
- Work with the target’s engineering team to execute the 90-day roadmap
- Unblock integration work
- Make architectural decisions
- Hire and build the post-close team
If the target’s CTO is staying, the pre-deal CTO becomes their technical advisor and partner. If the target’s CTO is leaving, the pre-deal CTO becomes the interim CTO.
PADISO offers Fractional CTO & CTO Advisory in Sydney and across major cities—New York, San Francisco, Boston, and Melbourne—specifically for this post-close transition.
Step 3: Measure and Iterate
Post-close, track:
- Engineering velocity: Are they shipping faster? Deployment frequency, lead time, change failure rate.
- Technical risk: Is technical debt decreasing? Are security and compliance improving?
- Business impact: Is the integration unlocking revenue? Are customers happier? Is churn lower?
Every quarter, reassess the roadmap. What’s working? What’s not? What’s the next priority?
How to Hire a Pre-Deal CTO
Not all CTOs are created equal. A pre-deal CTO needs a specific skill set.
What to Look For
Experience:
- 15+ years of engineering leadership
- Experience building and scaling systems (not just managing teams)
- Experience with multiple tech stacks and architectural patterns
- Experience in your industry (fintech, healthcare, SaaS, etc.) is a plus
Skills:
- Can read and understand code in multiple languages
- Can assess architecture and scalability
- Can evaluate engineering teams and hiring plans
- Can communicate technical risk to non-technical audiences
- Can price technical work and understand trade-offs
Temperament:
- Outcome-focused, not process-focused
- Can work independently and manage up
- Comfortable with ambiguity and incomplete information
- Can challenge assumptions and push back on BS
- Collaborative, not territorial
Where to Find Them
Option 1: Hire a Fractional CTO
Companies like PADISO specialise in fractional CTO services. You get someone with 15+ years of experience, without the full-time cost. They’ve done diligence before, they know what to look for, and they can move fast.
PADISO’s Services include CTO as a Service, custom software development, and AI automation—all relevant to pre-deal technical work.
Option 2: Hire an Independent Consultant
Find a former CTO or VP Engineering who’s available for fractional work. Vet them carefully:
- Ask for references from past diligence engagements
- Ask what they found in those diligence projects (without breaching confidentiality)
- Ask how they’d approach your specific situation
- Do a trial project (1–2 weeks) before committing to the full engagement
Option 3: Hire a Tech Due Diligence Firm
Firms like Bain’s Tech Due Diligence and others offer dedicated technical diligence services. They’re more expensive, but they have processes and they’ve done hundreds of deals.
What to Pay
Fractional CTO: $15K–$30K per week, or $60K–$120K for a 4–6 week engagement.
Independent Consultant: $10K–$25K per week, depending on experience and market.
Tech Due Diligence Firm: $50K–$150K+ for a full engagement, depending on scope and complexity.
For most deals, a fractional CTO or experienced independent consultant is the sweet spot. You get deep expertise without the overhead of a full-time hire.
Real-World Examples and Outcomes
Let’s look at how pre-deal CTO diligence has changed real deals.
Example 1: SaaS Platform Acquisition
The Situation:
A growth-stage SaaS company (Series B) was acquiring a competitor. The deal was $20M. The buyer’s thesis: combine the two platforms, eliminate redundancy, and cross-sell to both customer bases.
The Problem:
The buyer’s finance team was excited. The target’s revenue was growing 40% YoY. But no one had looked at the code.
The Diligence:
A pre-deal CTO spent 6 weeks in the target’s codebase. What they found:
- The target’s platform was a monolith built on an older version of Rails
- The buyer’s platform was a microservices architecture on Node.js
- Integration would require building adapters or rewriting the target’s platform
- The target’s engineering team was 3 junior developers (no seniors)
- Technical debt was severe (< 20% test coverage, no observability)
The Impact:
Instead of a 6-month integration, the real timeline was 18 months. Instead of eliminating redundancy, they’d need to run both platforms in parallel for a year. The buyer renegotiated the deal down by $5M and built a post-close integration roadmap that accounted for the real work.
Outcome:
The deal closed at $15M instead of $20M. Post-close, the buyer hired senior engineers and executed a 18-month integration. The combined platform was stronger, but the buyer had accurate expectations and pricing.
Without diligence, the buyer would have overpaid and been surprised post-close.
Example 2: Fintech Platform Acquisition
The Situation:
A fintech acquirer was buying a payments platform. The deal was $50M. The thesis: acquire the customer base and the payment processing capability.
The Problem:
The target was operating in a regulated industry (financial services). Compliance was critical. But the buyer hadn’t done a compliance audit.
The Diligence:
A pre-deal CTO with fintech experience audited the target’s security and compliance posture. What they found:
- The platform was not SOC 2 compliant
- Encryption was not implemented consistently
- Access control was based on trust, not roles
- There was no audit logging
- The target had never had a security audit
The Impact:
Fixing these issues would take 6 months and $1M+ in engineering work. The buyer couldn’t operate the platform without compliance, so they had to fix it immediately post-close.
Outcome:
The buyer renegotiated the deal down by $2M to account for remediation costs. Post-close, they hired a security engineer and spent 6 months getting to SOC 2. The deal still made sense, but the buyer had accurate expectations.
Without diligence, the buyer would have discovered these issues post-close, when they were more expensive and risky to fix.
Example 3: AI-First Startup Funding
The Situation:
A Series A investor was funding an AI-first startup. The deal was $5M. The thesis: the team had built a proprietary ML model that could predict customer churn.
The Problem:
The investor had seen the model’s accuracy (95%+) but hadn’t looked at the ML infrastructure.
The Diligence:
A pre-deal CTO with ML expertise audited the ML platform. What they found:
- The model was trained and working in staging
- But there was no MLOps pipeline, no feature store, no model monitoring
- In production, the model was drifting (accuracy was dropping over time)
- There was no way to retrain the model automatically
- The team had built the model but didn’t have the infrastructure to operationalise it
The Impact:
The investor realised the model wasn’t actually deployable in its current form. The team would need 2–3 months and $200K+ in engineering work to build the MLOps infrastructure.
Outcome:
The investor funded the round but reduced the valuation by $1M and structured the funding to reward hitting MLOps milestones. The team used the first 3 months to build the infrastructure. By month 4, they had a production ML platform.
Without diligence, the investor would have overpaid for a model that wasn’t actually deployable.
Sizing Value Creation With Technical Diligence
One of the most valuable outputs of pre-deal CTO diligence is a realistic value-creation roadmap. Here’s how to build one.
Value-Creation Levers
There are typically 5–7 technical value-creation levers in any acquisition:
1. Revenue Synergies
Can you sell the target’s product to the buyer’s customers, or vice versa? Technical diligence answers:
- How hard is it to integrate the target’s product into the buyer’s platform?
- What’s the real timeline and cost?
- Are there architectural incompatibilities that make integration infeasible?
2. Cost Synergies
Can you eliminate redundant infrastructure, teams, or processes? Technical diligence answers:
- What’s the target’s infrastructure cost? Can you migrate it to the buyer’s infrastructure?
- How many redundant engineers can you eliminate?
- What’s the real timeline and cost to consolidate?
3. Efficiency Gains
Can you make the target’s operations more efficient? Technical diligence answers:
- What’s the target’s engineering velocity? Can you improve it?
- What quick wins can be shipped to improve customer experience or reduce churn?
- What’s the ROI on technical improvements?
4. Risk Reduction
Can you reduce technical risk and improve the target’s position? Technical diligence answers:
- What technical risks exist? What’s the cost to fix them?
- Can you improve security and compliance to unlock enterprise deals?
- Can you improve scalability to support growth?
5. Platform Leverage
Can you build the target’s product on the buyer’s platform? Technical diligence answers:
- Is the buyer’s platform mature enough to support the target’s use cases?
- What’s the real timeline and cost to migrate?
- What’s the benefit (cost savings, new capabilities, faster shipping)?
Building the Roadmap
For each value-creation lever, the CTO should estimate:
- Probability: What’s the likelihood this lever will work? (High, Medium, Low)
- Timeline: How long will it take to realise the value? (Weeks, Months, Quarters)
- Cost: What’s the engineering effort and cost? (In FTE-months or dollars)
- Upside: What’s the financial benefit? (Revenue uplift, cost savings, risk reduction)
Example:
| Lever | Probability | Timeline | Cost | Upside | NPV |
|---|---|---|---|---|---|
| Revenue synergies (cross-sell) | Medium | 6 months | 3 FTE-months | $2M ARR | $1.5M |
| Cost synergies (consolidate infra) | High | 3 months | 1 FTE-month | $500K/year | $1.5M |
| Efficiency gains (improve velocity) | High | 6 months | 2 FTE-months | 20% faster shipping | $1M |
| Risk reduction (security audit) | High | 3 months | 1 FTE-month | Unlock enterprise deals | $500K |
| Platform leverage (migrate to buyer’s platform) | Low | 12 months | 6 FTE-months | $1M/year savings | $1M |
Now you have a realistic value-creation thesis. You can price the deal, allocate resources, and measure success post-close.
Security and Compliance Diligence
For companies operating in regulated industries (fintech, healthcare, insurance), security and compliance diligence is critical. Here’s what to audit.
SOC 2 and ISO 27001 Readiness
If the target is operating in a regulated industry or selling to enterprise, they need to be SOC 2 and/or ISO 27001 compliant (or audit-ready).
PADISO’s Security Audit service uses Vanta to assess and improve SOC 2 and ISO 27001 readiness. The diligence process should answer:
- Current state: Are they SOC 2 / ISO 27001 compliant today?
- Gaps: What controls are missing? What’s the remediation effort?
- Timeline: How long to achieve compliance? Can it be done before enterprise deals?
- Cost: What’s the engineering effort and external audit cost?
Data Handling and Privacy
If the target handles customer data, audit:
- Data classification: Do they know what data they have? How sensitive is it?
- Encryption: Is data encrypted at rest and in transit?
- Access control: Who has access to customer data? Is access logged?
- Data retention: How long do they keep data? Can they delete it on request (GDPR right to be forgotten)?
- Third-party vendors: What vendors have access to customer data? Are they audited?
Incident Response
If the target has had a security incident, audit:
- What happened: What was the breach? How did it happen?
- Response: How quickly did they detect it? How did they respond?
- Remediation: What did they fix? What did they learn?
- Prevention: What controls did they add to prevent recurrence?
If they’ve never had an incident, that’s good. But ask: do they have incident response procedures? Have they tested them? Can they respond to a breach in < 24 hours?
Platform Engineering and AI Diligence
If the target is building on modern infrastructure (microservices, Kubernetes, serverless) or has AI/ML capabilities, audit these specifically.
Platform Engineering Diligence
For Platform Development in Sydney, New York, San Francisco, and Boston, technical diligence should assess:
- Infrastructure as code: Is the infrastructure defined as code (Terraform, CloudFormation)? Can you reproduce it reliably?
- Containerisation: Are services containerised (Docker)? Is orchestration automated (Kubernetes)?
- Observability: Is there comprehensive logging, metrics, and tracing? Can you understand what’s happening in production?
- Deployment pipeline: Is deployment automated? How long does a deployment take? What’s the failure rate?
- Cost optimisation: Is infrastructure cost optimised? Are there obvious cost-saving opportunities?
AI and ML Diligence
If the target has AI/ML capabilities, audit:
- Model development: How are models trained? Is there version control for models and training data?
- Model evaluation: How is model quality measured? What’s the accuracy, precision, recall? Is there a test set?
- Model deployment: How are models deployed to production? Can you deploy a new model without downtime?
- Model monitoring: Are models monitored in production? Do you know when they’re degrading?
- Model retraining: Can models be retrained automatically? How often? Is there a feedback loop?
- Feature engineering: Is there a feature store? Can features be shared across models?
Red flags:
- Models are trained in notebooks, not a reproducible pipeline
- No version control for models or training data
- Model quality is measured informally (“it seems to work”)
- Models are deployed manually or require downtime
- No monitoring of model performance in production
- Models are trained once and never updated
- Features are hardcoded or duplicated across models
Green flags:
- Model development is in a reproducible pipeline (MLflow, Kubeflow, etc.)
- Models and training data are versioned
- Model quality is measured rigorously (test set, cross-validation, etc.)
- Models are deployed automatically with canary releases
- Model performance is monitored continuously (accuracy, latency, cost)
- Models are retrained automatically when performance degrades
- Features are managed in a feature store
Next Steps: Building Your Diligence Plan
If you’re acquiring a company or investing in a startup, here’s how to get started with pre-deal CTO diligence.
Step 1: Define the Technical Thesis
Before you bring in a CTO, be clear on what you’re trying to validate:
- What’s the core technical bet? Is it the product? The team? The infrastructure? The data?
- What could go wrong technically? What are the biggest risks?
- What’s the integration plan? If this is an acquisition, how will you integrate the technology?
- What’s the value-creation thesis? What technical improvements will unlock value?
Step 2: Hire a Pre-Deal CTO
Based on your thesis, hire someone with relevant experience. If you’re acquiring a fintech platform, hire a CTO with fintech experience. If you’re acquiring an AI company, hire a CTO with ML experience.
Start with a 2–3 day assessment (pre-LOI). If it looks promising, move to a full 4–8 week engagement (post-LOI).
PADISO’s AI Advisory Services Sydney and fractional CTO services across Sydney, Melbourne, New York, San Francisco, and Boston can help you navigate this process.
Step 3: Set Clear Deliverables
Before the CTO starts, agree on deliverables:
- Pre-LOI: 1-page technical summary and recommendation
- Post-LOI: 30–50 page technical diligence report with findings, risk quantification, and post-close roadmap
- Weekly updates: Brief updates on progress and emerging findings
Step 4: Integrate Findings Into Deal Model
Once you have the diligence report, use it to:
- Validate or challenge the business thesis: Does the tech support the growth plan?
- Price technical risk: What’s the cost to fix issues? How does that impact valuation?
- Build the post-close roadmap: What’s the first 90 days? What’s the first year?
- Negotiate deal terms: Use diligence findings to negotiate price, earnouts, or retention agreements
Step 5: Plan for Post-Close Execution
Before close, agree on:
- Post-close technical leadership: Who’s leading technical integration? Is it the target’s CTO, the buyer’s CTO, or a pre-deal CTO?
- 90-day roadmap: What are the quick wins? What’s the critical path?
- Team and hiring: What senior engineers do you need to hire?
- Success metrics: How will you measure technical success? (Velocity, security, scalability, cost)
Conclusion: Pre-Deal CTO as a Strategic Advantage
Pre-deal CTO diligence is not a nice-to-have. It’s a strategic advantage.
In a competitive market, the difference between a 2x return and a write-down often comes down to whether you understood the technical risk before you committed capital. The difference between a smooth integration and a 18-month nightmare is whether you had a realistic roadmap before close.
A pre-deal CTO gives you that edge. They pressure-test the thesis. They size the value-creation upside. They price integration risk. They tell you, in plain terms, whether this is a good deal and what you need to do post-close to win.
For founders, the benefit is similar. If you’re raising a Series A or Series B, having a CTO audit your tech before you go into the data room means you can answer diligence questions with confidence. You can tell investors: “Yes, we’ve thought about scalability. Here’s our roadmap. Here’s why we’re not worried about technical risk.”
For operators and engineering leaders, it’s an opportunity to get external validation of your technical decisions. A pre-deal CTO can tell you: “Your architecture is sound. Here’s what you should focus on next.”
The investment is small (4–8 weeks, $60K–$120K). The upside is large (millions in deal value, months of integration time saved, technical risk de-risked).
If you’re serious about a deal, bring in a pre-deal CTO. It’s the best $100K you’ll spend on diligence.
Getting Started
Ready to pressure-test your technical thesis? PADISO specialises in pre-deal CTO diligence and fractional technical leadership. We’ve done this for founders, operators, and investors across Australia, the US, and beyond.
Start with a conversation. We’ll help you define the technical thesis, scope the diligence, and build a plan.
Book a call with PADISO or explore our Services to learn more about how we approach technical diligence and post-close integration.
Or, if you’re in Australia and want to explore AI readiness and technical strategy, try our AI Quickstart Audit—a fixed-scope, fixed-fee 2-week diagnostic that tells you where you actually are and what to ship first.