Technical diligence for AI-native software teams

Know the team before you buy the code.

AlexaGuru gives CEOs, boards, PE firms, and venture investors a decision-grade read on engineering capability: code quality, delivery behavior, AI adoption, key-person risk, and the technical liabilities that do not show up in a pitch deck.

Already building with AI? We help you see the work, check the code, and set clear gates for agent changes.

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Built for Private Equity Venture Capital CEOs & Boards CTOs in transition

Engineering Diligence Snapshot

Sample acquisition target

Elevated risk

Architecture 82

Clear modules; two services carry most product risk.

Team Health 76

Strong core contributors; review load is concentrated.

Codebase Agent Readiness 56

Useful type coverage, weak test pairing in key flows.

Exposure 38

Three exploitable paths need pre-close remediation.

Knowledge concentration is measurable, not guessed: two engineers own 71% of critical-path changes.
Guardian findings mapped to OWASP, NIST, SOC 2, and remediation priority.
CycleTrack history shows delivery velocity improving, but review latency rising.

30+years leading software teams

2purpose-built analysis platforms

100%evidence-backed findings

AIdiligence for the new build model

What we answer

The questions leaders need answered.

We read delivery history alongside the code to give CEOs, boards, and investors evidence they can act on.

For CEOs & boards

Is AI coding helping or hurting my business?
Is my engineering team better today than it was last year?
Are we shipping faster without taking on more risk?

For PE & investors

Can this team deliver the growth plan after close?
Which technical risks could change the price or integration plan?
Where does critical work depend on one engineer?

How we help

Technical Due Diligence

Architecture risk, maintainability, security exposure, test depth, delivery velocity, and remediation cost - quantified before close.

For PE & VC

Engineering Talent Evaluation

Measure contribution, review capacity, and key-person risk alongside the team's AI use. Assess whether its practices and codebase are ready for agent-assisted delivery and growth.

For boards & acquirers

CTO Advisory

Design ticket-to-PR workflows, quality gates, and review points for teams adopting AI-assisted development.

For CTOs & founders

Evidence engine

Behavior plus code, read together.

Most diligence reads the repository or interviews the team. AlexaGuru connects both: how the team actually works, and what that behavior has produced.

How we measure AI's effect

AI adoption is a starting point. AlexaGuru examines how the team uses AI, whether delivery and quality improve together, and whether its codebase and review process can support agent work. We compare trends over time and show what the evidence supports.

CycleTrack

Behavioral analytics from real delivery history: contribution concentration, review load, delivery archetypes, velocity changes, and AI-adoption maturity.

Guardian

AI-aware semantic code scanning for security, architecture, maintainability, compliance mapping, SARIF export, and agent-readiness scoring.

For teams building with AI

Make agent work visible, reviewable, and accountable.

Whether agents write code or assist human engineers, leaders need to know what changed, who reviewed it, and where risk remains. These tools connect delivery history, code checks, and the work queue.

CycleTrack · Delivery evidence

See how the team actually ships.

Spot review bottlenecks, concentrated ownership, and changes in delivery pace as AI becomes part of the workflow.

Guardian · Code checks

Catch risky changes before merge.

Scan human and agent changes for security, maintainability, architecture, and the code clarity agents need to work safely.

Nightshift + Ranger · Agent workflow

Give agents a clear path to review.

Move scoped tickets through implementation, tests, quality gates, and pull requests so people can review the result before it ships.

Start with your current workflow. We can identify where AI helps, where review is overloaded, and which controls the team needs next.

Discuss your AI team

The deliverable

Built for the IC memo.

Executive summary with go / no-go / proceed-with-conditions framing.
Risk register ranked by materiality, exploitability, and remediation cost.
Team-health evidence with key-person dependency, AI use, review capacity, and scaling constraints.
Codebase agent-readiness score: how safely agents can understand and modify the codebase.
Follow-on advisory plan for the first 30, 60, and 90 days after close.

Diligence Risk Register Sample excerpt

Finding

Severity

Decision impact

Auth bypass in admin workflow

High

Condition precedent

Review load concentrated in one lead

Medium

Retention risk

Agent-safe structure in core services

Strength

Acceleration upside

Test depth weak around billing flows

Medium

Price remediation

Make the software decision with evidence.

Whether you are evaluating an acquisition, backing a growth round, or taking over an engineering organization, AlexaGuru gives you the technical truth before the decision is irreversible.

Email info@alexaguru.com