Technology value creation
Create technology value you can prove at exit.
One baseline across every portfolio company shows where technology moves cash flow, exit price and risk, and tracks each improvement through to the sale.
Portfolio technology baseline
Q2 2026| Metric | Company A | Company B | Company C | Company D | Company E |
|---|---|---|---|---|---|
| Code health0 to 100 | 78 | 66 | 58 | 42 | 72 |
| Key person dependencytop owner share, largest critical system | 14% | 41% | 26% | 38% | 9% |
| Innovation vs maintenanceinnovation share of engineering effort | 64% | 48% | 55% | 22% | 58% |
| Vulnerabilitiescritical / high | 0 / 2 | 1 / 9 | 6 / 31 | 9 / 47 | 0 / 4 |
| Scalability tierT1 most scalable, T5 least | T2 | T2 | T3 | T4 | T2 |
| Delivery (DORA) | |||||
| Deployment frequencydeploys per week | 11.5 | 4.2 | 9.8 | 0.2 | 0.6 |
| Lead time for changeshours | 9.5 | 23.6 | 14 | 910 | 210 |
| AI adoption in development | |||||
| AI-written codeshare reaching production | 38% | 26% | 44% | 3% | 17% |
| AI code merged unreviewedshare of AI-written lines | 4% | 6% | 58% | n/a | 11% |
| Security and supply chain | |||||
| Outdated dependenciesshare more than two major versions behind | 6% | 11% | 18% | 34% | 8% |
| Licence riskcopyleft or unknown licences | 0 | 2 | 1 | 5 | 0 |
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Why technology matters to value
Technology moves cash flow, exit price and risk.
Buyers price all three. Most value creation plans measure only the first.
01Cash flow
How efficiently products are built, shipped and run.
What moves it
- Engineering productivity
- Delivery speed and stability
- AI-assisted and agentic coding
What CodeDD measures
- DORA delivery metrics
- Engineering effort allocation
- AI-written code
02Exit price
What a buyer will pay for the platform and its growth.
What moves it
- Scalability
- Proprietary IP and data
- AI built into the product
What CodeDD measures
- Architecture scalability
- Proprietary versus open-source code
- Evidence of AI in the product
03Risk
What could erode value after close, or surface in a buyer's diligence.
What moves it
- Security and licence exposure
- Key-person dependency
- Accumulated technical debt
What CodeDD measures
- Security and licence posture
- Contributor concentration
- Remediation effort and progress
Technology value creation
Five questions every value creation plan asks of technology.
Each one is measured the same way in every portfolio company, every quarter.
Growth
Can the platform carry the growth plan?
See each company's scalability tier, based on the architecture and deployment setup in its code. Know which platforms need investment first.
Measured by
- Scalability tier
- Architecture
Speed
Which teams ship fast and securely?
Compare deployment frequency and lead time for changes across companies. See how much AI-written code is merged without review.
Measured by
- Deployment frequency
- Lead time for changes
- AI code merged unreviewed
Efficiency
Where does engineering time go?
See the split between new features and maintenance in every company, and which systems take the most effort. Move engineering capacity to the work in the plan.
Measured by
- Innovation vs maintenance
- Technical debt
Defensibility
What would a buyer pay a premium for?
See how much of each product is proprietary code and where AI is built into the product. Know which platforms support a higher multiple.
Measured by
- SBOM and dependencies
- AI Native
Risk
What would a buyer's diligence find?
See open vulnerabilities, licence exposure and key-person risk in every company. Fix them well before the exit process starts.
Measured by
- Vulnerabilities
- Key person dependency
How CodeDD supports value creation
From manual reporting to live evidence.
Much of tech reporting today still runs on questionnaires, calls and spreadsheets. CodeDD automates evidence collection and interpretation straight from the code and the CI/CD pipeline.
Before CodeDD
With CodeDD
Questionnaires and interviews. Every review starts with long forms and management calls. Portfolio teams lose days to it.
Answers from the code. CodeDD reads each company's Git history directly. Interviews focus only on findings that need context.
Inconsistent data. Each portco reports its own way. Nothing lines up, so no one can compare.
One scorecard for all. Same definitions and same time window for every company. You compare like with like.
Data that arrives late. Numbers come in weeks after the quarter closes. Problems have already grown.
Data that stays current. Git syncs daily, so a slipping company shows up weeks before the board meeting.
Progress you have to take on trust. Status updates say every plan is on track. No one can verify it.
Progress you can prove. Every company's change since the last audit is measured, so you can show it to buyers at exit.
Delivery (DORA)
Which portfolio companies can deliver their plan?
The four key DORA metrics for every company, on the same definitions and the same 90-day window. Company C deploys almost as often as Company A, but one release in four needs a fix, so its speed is costing it stability.
Portfolio delivery (DORA)
Last 90 days · five portfolio companies| Metric | Company A | Company B | Company C | Company D | Company E |
|---|---|---|---|---|---|
| Throughput | |||||
| Deployment frequencydeploys per week | 11.5 | 4.2 | 9.8 | 0.2 | 0.6 |
| Lead time for changeshours | 9.5 | 23.6 | 14 | 910 | 210 |
| Failed deployment recoveryhours | 0.8 | 3.5 | 31 | 190 | 20 |
| Instability | |||||
| Change fail rateshare of deploys needing a fix | 3.1% | 8.2% | 27% | 12% | 6.5% |
AI in development
See where AI speeds up delivery and where it adds risk.
CodeDD tracks AI use and code review in every portco. Company C writes 44% of its code with coding agents and ships over half of it unreviewed. Its 27% change fail rate is the likely result.
- AI-written code counts only commits that name a coding agent, so each share is a floor.
- Company D shows n/a because its Git host does not expose review data. Missing data is never counted as a shortfall.
Portfolio AI in development
Last 90 days| Metric | Company A | Company B | Company C | Company D | Company E |
|---|---|---|---|---|---|
| Adoption | |||||
| AI-written codeshare of lines | 38% | 26% | 44% | 3% | 17% |
| Repositories with agent instructionsof repositories in scope | 9 of 12 | 5 of 10 | 2 of 14 | 0 of 6 | 3 of 8 |
| Review | |||||
| AI code merged unreviewedshare of AI-written lines | 4% | 6% | 58% | n/a | 11% |
| Open AI pull requestsunreviewed / total | 2 / 14 | 1 / 9 | 23 / 31 | 0 / 0 | 4 / 12 |
Exit readiness
Software and AI reach deeper into every business.
Technology value creation is now a core lever in the value creation plan. It drives valuation and has a direct impact on the deal outcome.
Ready for a fast exit
The private equity exit backlog may call for a fast process. Are your companies prepared?
Higher valuation
A scalable platform, a record of improvement and evidence of AI in your products support a stronger multiple.
Lower risk
Fewer diligence findings mean fewer price chips and delays.
Our connections
GitHub
GitLab
Azure DevOps
Bitbucket
FAQ
Questions operating partners ask us.
Which measures does the portfolio view include?
Code quality across seven dimensions, delivery through the five DORA metrics, AI in development, and key-person risk. Each uses the same definitions as the individual product views and is applied the same way in every company.
How do portfolio companies connect?
Each company grants read-only access to its Git host: GitHub, GitLab, Azure DevOps or Bitbucket. There is no agent to install and no survey to fill in.
How current is the data?
Delivery data syncs from Git every day. The view shows when each company last synced and flags anything older than 48 hours.
Can companies with different release models be compared?
Yes, with context. A company whose customers install the software may release quarterly and rate Low on deployment frequency. That can suit the business, so read each company's metrics against how it delivers to customers.
Do you rank individual developers?
No. The view reports on repositories and companies. It does not score or rank individual engineers. Contributor data can also be fully anonymised to meet local data protection policies.
What data do you store?
Structured results such as deploy events, review linkage, AI-authored line counts and ownership shares. Not source code.
How does this feed the value creation plan?
The entry audit ranks findings by impact and estimates the engineering effort for each one. Operating partners use it to set technology priorities for the 100-day plan, and quarterly checks show whether they are moving.
Can we share results with buyers at exit?
Yes. Reports can be shared by name with buyers and their advisors, with role-based access. Quarterly progress on the same benchmark shows how the technology improved during the hold.
Can companies on different technology stacks be compared?
Yes. Scores use the same definitions across languages and frameworks, legacy code included, so every company is measured the same way.
How would a buyer read your portfolio's technology today?
Book a call to see your portfolio on one set of measures, from the 100-day plan to exit.