Case study · Private equity · Portfolio reporting & governance
Automated Portfolio Reporting.
Manual data requests replaced with a quarterly, like-for-like tech baseline.
- Portfolio companies
5
benchmarked on one standardised, code-level baseline
- Manual requests
0
data requests or interviews needed to produce the quarterly baseline
- Assets flagged
1
flagged for immediate review on lagging AI-coding-agent adoption
- Reporting cadence
Quarterly
same metrics, same scoring, every quarter, board-ready
The challenge
Manual reporting, inconsistent results.
The fund's quarterly portfolio reporting relied on manual interviews and data requests to each portfolio company. Returned data was inconsistent, partially missing, or hard to compare across assets, and it created recurring extraction and compilation work for portfolio company teams.
Fund management could not fully rely on the results to track technical debt, delivery performance, or AI adoption, or to build a defensible view of value creation and exit readiness across the portfolio.
- 01
How do five portfolio companies compare on code health, risk, and delivery, on a like-for-like basis?
- 02
Where is technical or key-person risk concentrated, and which asset needs attention first?
- 03
How do we track AI adoption and value creation progress without another round of manual data requests?
The solution
A standardised, repeatable portfolio baseline.
CodeDD provided a standardised, automated data feed across the relevant metrics for the fund, allowing deep dives into any relevant area, to target priorities with each company and share learnings across the portfolio with quarterly updates.
- 01
Scanned every asset the same way
The same automated analysis run across all five portfolio companies, no interviews, no manual data requests, no inconsistency between teams.
- 02
Benchmarked code health and risk
Code health, key-person dependency, vulnerabilities and scalability tier compared like-for-like across the portfolio.
- 03
Tracked delivery and AI adoption
DORA delivery metrics and the share of AI-written code reaching production tracked alongside the risk baseline.
- 04
Refreshed quarterly, board-ready
The same baseline re-run every quarter, giving fund management a repeatable, comparable view for governance and value creation.
codedd.ai / portfolio-benchmark
Portfolio technology baseline
Q2 2026 · five portfolio companies| Metric | Company A | Company B | Company C | Company D | Company E |
|---|---|---|---|---|---|
| Code health0 to 100 | 78 | 66 | 58 | 42 | 72 |
| Key person dependencyshare of code by top developer | 18% | 6% | 47% | 34% | 12% |
| Innovation vs maintenanceinnovation share of engineering effort | |||||
| Vulnerabilitiescritical / high | 0 / 1 | 2 / 27 | 9 / 48 | 17 / 94 | 3 / 12 |
| Scalability tierT1 most scalable, T5 least | T1 | T2 | T3 | T3 | T2 |
| Delivery (DORA) | |||||
| Deployment frequencydeploys per week | 22 | 14 | 9 | 3 | 18 |
| Lead time for changehours | 42 | 61 | 88 | 210 | 36 |
| AI adoption in development | |||||
| AI-written codeshare reaching production | 55% | 41% | n/a | 0% | 34% |
The results
A governance baseline the fund can act on, every quarter.
Like-for-like view across five companies, refreshed every quarter without a single manual data request.
One asset flagged for immediate review, Company D, on lagging adoption of AI coding agents in production code.
Succession and domain-expertise risk surfaced at Company C, where 47% of the codebase sits with a single contributor.
Manual data requests replaced by a repeatable, automated scan across the entire portfolio.
Delivery and AI adoption tracked alongside code health, giving the fund a single baseline for value creation and exit readiness.
“We used to chase five companies for five different spreadsheets. Now we get one baseline, the same day, every quarter.”
Portfolio Operations Director, private equity fund