Run TDD Before You Write the Check
Most seed-stage investors have no reliable way to evaluate the engineering substance behind a deal. Sprinno changes that — without a $50K consulting engagement.
The Three Bad Options Investors Use Today
Before Sprinno, a solo GP or angel had three choices — none of them good:
- 1.Skip technical diligence entirely — rely on pitch-deck claims and founder charisma. Discover "the CTO is the only person who's ever touched this code" after the check clears.
- 2.Ask a friend to "take a quick look" — informal, unstructured, not repeatable, and dependent on whoever happens to be available.
- 3.Pay $25K–$75K for a manual firm — priced for Series C+ deals, far too slow and expensive for seed-stage pipeline, and delivered as a static PDF that's stale the moment it's read.
Without Technical Diligence
- ❌ Critical vulnerabilities surface post-close
- ❌ Single-engineer bus risk invisible until they quit
- ❌ 60%+ of dependencies outdated or unpatched
- ❌ No test coverage on the core product
- ❌ Paying for a product that doesn't exist yet
With Sprinno Diligence OS
- ✅ Risk scored before term sheet is signed
- ✅ Findings narrated in plain language for non-technical GPs
- ✅ Consistent, repeatable — runs the same way on every deal
- ✅ Live score stays current from term sheet to close
- ✅ Fraction of the cost and time of a manual firm
What the Risk Score Covers
Dependency & Security Risk
Known CVEs across the full dependency tree, severity scoring, outdated packages, and licence exposure. Not a manual review — deterministic scanning 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 or unmaintainable code.
Team Health & Bus Risk
Commit distribution across contributors — surfaces single-engineer knowledge concentration before it becomes a post-investment crisis.
Git & Development Velocity
Commit cadence, PR merge hygiene, branching strategy, and whether the team is actually building or stagnating.
Infrastructure & Secrets Hygiene
Hardcoded credentials, exposed API keys, misconfigured environment handling — risks that sit silently in repositories and become material events.
AI Governance (for AI-native companies)
Training data provenance signals, model evaluation methodology review, EU AI Act conformity posture — the risk surface no generalist TDD firm is equipped to assess.
Real-World Scenarios
Scenario 1: Solo GP at Seed Stage
Evaluating a developer-tools startup. Founder is technical, demo is impressive, ARR trajectory is good. GP has no engineering background and no budget for a $40K manual review on a $350K check.
What They Were Flying Blind On
- •Whether the product demo reflects production code or a polished one-off build
- •Whether the sole founder-CTO is also the only person who's ever committed code
- •Whether the dependency stack has known vulnerabilities that would block enterprise sales
What Sprinno Found
Team Health
94% of all commits originate from one contributor over 18 months. No evidence of second technical hire despite hiring claims in the deck.
Dependency Risk
3 critical CVEs in production dependencies, including one rated CVSS 9.1. All three were fixable with a version bump — but none had been addressed.
Test Coverage
0% test coverage across the billing and authentication modules. Core product logic has 12% coverage.
Outcome
- GP used the findings to negotiate a structured close — first tranche contingent on two of three issues being resolved
- Founder fixed the CVEs and added a second engineer before funds transferred
- Investment proceeded with materially lower technical risk than the original term sheet assumed
Scenario 2: Micro-fund Reviewing a Competitive Deal
Fast-moving seed round in an AI infrastructure company. Multiple term sheets. Fund partner has 48 hours to decide. No time for a manual firm, no technical partner available.
What They Were Flying Blind On
- •Whether the AI components are built on proprietary work or a thin wrapper around a third-party API
- •Licence exposure from open-source model weights in the stack
- •Whether the claimed proprietary training data has any provenance documentation
What Sprinno Found
AI Governance
Model serving layer is a wrapper around a single third-party API with no fallback. Proprietary differentiation is narrower than pitched.
IP & Licensing
Two dependencies use AGPL licences — copyleft terms that may require the company to open-source its own code if used in a SaaS context.
Infrastructure
API keys for the primary model provider found hardcoded in a public-facing config file. Rotated after flagging.
Outcome
- Fund passed on the round — AI moat was materially thinner than the narrative suggested
- AGPL exposure flagged to the founders as a blocking issue for future enterprise sales
- Decision made in under 4 hours with documented, shareable reasoning
Why Sprinno, Not a Manual Firm
1. Speed — days, not weeks
A manual TDD firm takes 3–6 weeks from engagement to delivery. Sprinno connects to the repository and produces a scored, narrated report within hours. That's the difference between diligence that fits inside a deal timeline and diligence that doesn't.
2. Cost — accessible at seed stage
Manual TDD is priced for Series C+ deals. At $25K–$75K per engagement, it's economically absurd to run on every company in a seed portfolio. Sprinno is priced to run on every serious deal in your pipeline.
3. Repeatability — same framework, every time
A friend's informal code review reflects that person's opinions, blind spots, and availability. Sprinno runs the same deterministic scoring framework on every company — so findings are comparable and defensible.
4. Living risk score — not a document that ages
A PDF from a manual firm is stale the moment it's delivered. The risk score Sprinno generates stays live — re-analyzed on a schedule, so a vulnerability introduced after term sheet shows up before the round closes.
5. Narrative synthesis — accessible to non-technical investors
Raw technical findings are useless to a solo GP without an engineering background. Sprinno narrates every finding in plain language — what it means, why it matters, and what a reasonable ask of the founder would be.
What You Get in a Diligence Report
Overall risk score (0–100)
Deterministic composite score across all risk domains. Comparable across deals.
Dependency & CVE scan
Full dependency tree analysis with CVSS-rated vulnerability findings.
Test coverage breakdown
Overall coverage plus critical-path coverage for auth, billing, and data-handling modules.
Team health & bus risk
Contributor concentration analysis across all repositories connected.
AI governance assessment
For AI-native companies — training data provenance, model evaluation, licence exposure.
Plain-language narrative
Every finding explained in terms an investor without an engineering background can act on.
Live IP & licence register
Updated whenever a new dependency is introduced — not regenerated on request.
Shareable report link
Send a time-stamped snapshot to co-investors or LPs as evidence of diligence process.