Technical Due Diligence
Invest or partner with confidence. Get a clear, unbiased view of technology, risks, and upside—so you can act decisively.
The Challenge
Limited time, vendor gloss, and unverified AI claims create uncertainty and deal risk.
Our Approach
Independent deep-dive across code, infra, data, security, and delivery maturity—synthesized into clear findings with remediation priorities.
Key Outcomes
Decide with Confidence
Concise exec narrative backed by evidence.
Know the Risks
Red/Yellow/Green heatmap and implications.
Negotiate from Strength
Findings that inform valuation and terms.
Post-Close Momentum
Concrete remediation plan and milestones.
Credible AI Assessment
Claims validated against architecture and data realities.
Core Deliverables
- • Diligence report (exec summary + deep dive)
- • Risk heatmap and remediation plan
- • Q&A list for founders/leadership
- • Optional post-close review
Cross-links: Post-close enablement via AI & ML Architecture and MLOps & ML Systems.
Our Method — AtelaMind Diligence Path
Scope & Priorities
align on thesis and risk focus
Evidence Review
repos, docs, architecture, data
Interviews & Probing
leaders and engineers
Synthesize Findings
risks, upside, recommendations
Brief & Plan
clear next steps and ownership
Engagement Models
Case Study
VC Diligence — Assessed an AI platform's scalability and cost structure; provided a prioritized remediation roadmap.
FAQs
Access & confidentiality?
We work under strict NDAs with read-only access and defined information barriers.
How deep do you go?
Depth is tailored to stage and thesis; we prioritize the risks that matter.
How are you different from general IT diligence?
We specialize in AI/ML architecture, data, and MLOps realities.
Big firms or cloud PS vs AtelaMind?
Independent, senior-only reviewers; no product or implementation bias.