The Problem
A seed-stage investor reviews 200+ companies a year. Of those, maybe 15 get a term sheet. The technical evaluation for those 15? In most cases, it doesn't exist.
The investor reads the deck. Watches the demo. Talks to the founder. Maybe asks a friend, "Hey, can you take a quick look at their GitHub?", and gets back a shrug or a thumbs-up over text.
"We funded a company where the entire product was one engineer's side-project codebase. No tests, no CI, credentials in plain text. We found out six months after close."
— GP, $25M seed fund
This isn't negligence. It's a structural gap. There hasn't been a tool fast enough, cheap enough, or repeatable enough to run technical diligence on every deal in a seed pipeline. Until now.
What Gets Missed
Without a systematic look at the codebase, investors routinely miss risks that materially affect the value of what they're buying:
One engineer wrote 94% of the code. If they leave, the product doesn't survive the quarter.
Critical CVEs sitting unpatched in production dependencies, the kind that block enterprise sales.
Zero test coverage on authentication and billing. Every deploy is a coin flip.
AGPL dependencies in a SaaS product. A licensing landmine that could force open-sourcing the core product.
API keys and credentials committed in plain text to repositories. One breach away from a front-page incident.
"Proprietary AI" that's a thin wrapper around a third-party API with no fallback or differentiation.
Each of these has turned a good-on-paper investment into a write-down. Not because the founders were dishonest, but because nobody looked.
The Old Playbook
The existing options for technical diligence all break at seed stage:
Skip it entirely
Rely on the demo, the deck, and the founder's charisma. Hope the code is as good as the pitch.
Ask a friend
Informal, unstructured, not repeatable. Quality depends on who happened to be available that week.
Hire a manual firm
$25K–$75K per engagement. 3–6 weeks of calendar time. Priced for Series C, not seed. Delivered as a PDF that's stale before it's read.
The result: 90% of seed-stage investments close with zero structured technical evaluation. The investor is betting on narrative, not evidence.
How Scoring Works
Sprinno connects to a company's repositories and runs a deterministic analysis across six domains. No opinions. No variation between reviewers. The same framework, every time.
Dependency & Security Risk
Full dependency tree scan. Known CVEs rated by CVSS severity. Outdated packages. Licence exposure across every declared dependency.
Test Coverage & Code Quality
Coverage across the codebase and specifically across critical paths: auth, payments, data handling. Complexity hotspots that signal fragile code.
Team Health & Bus Risk
Commit distribution across contributors. Surfaces single-engineer concentration before it becomes a post-investment crisis.
Development Velocity
Commit cadence, PR merge hygiene, branching strategy. Whether the team is building or stagnating.
Infrastructure & Secrets
Hardcoded credentials, exposed API keys, misconfigured environments. Risks that sit silently until they become material events.
AI Governance
For AI-native companies: training data provenance, model evaluation methodology, EU AI Act conformity posture.
The output is a single composite score (0–100), comparable across every deal in your pipeline. Think of it as the SAT score for a codebase: standardized, repeatable, and instantly legible.
Anatomy of a Scan
From repository connection to scored report: hours, not weeks. The report is narrated in plain language. Every finding explained in terms a non-technical GP can act on.
Case: The Solo Founder
Situation
A developer-tools startup with an impressive demo and strong ARR trajectory. The GP has no engineering background. A $350K check is on the table. No budget exists for a $40K manual review.
What the scan revealed
- 94% of all commits from a single contributor over 18 months, with no evidence of a second technical hire
- 3 critical CVEs in production (including one rated CVSS 9.1), all fixable, none addressed
- 0% test coverage on billing and authentication modules
Outcome
- GP negotiated a structured close: first tranche contingent on resolving two of three critical findings
- Founder fixed CVEs and hired a second engineer before funds transferred
- Investment proceeded with materially lower risk than the original term sheet assumed
Case: The AI Wrapper
Situation
A competitive seed round in an AI infrastructure company. Multiple term sheets. The fund partner has 48 hours to decide. No time for a manual firm, no technical partner available.
What the scan revealed
- Model serving layer was a wrapper around a single third-party API with no fallback. Moat was thinner than pitched
- Two AGPL-licensed dependencies that could force open-sourcing in a SaaS context
- Primary model provider API keys found hardcoded in a public-facing config file
Outcome
- Fund passed on the round. AI differentiation was materially narrower than the narrative suggested
- AGPL exposure flagged to founders as a blocking issue for enterprise sales
- Decision made in under 4 hours with documented, shareable reasoning
What Changes
When every deal in your pipeline gets a standardized technical score:
Negotiation leverage, not just go/no-go
Findings become term-sheet conditions. Structure closes around evidence, not faith.
Portfolio-level visibility
Compare technical health across every company you've funded. Spot the ones drifting before they surface problems.
Founder accountability
A living score means the founder knows you're watching. Engineering hygiene becomes a board-level metric.
LP-grade documentation
Shareable, time-stamped reports that prove your diligence process is systematic, not ad hoc.
Speed that matches the market
Competitive rounds close in days, not months. Diligence that takes 6 weeks doesn't fit inside a term sheet's expiration.
Stop flying blind on technical risk.
Get a scored, narrated technical report before your next term sheet expires.