We help investors and acquirers see the full technical picture before they commit capital. Our diligence combines engineering depth, product intuition, and a commercial lens so you know what you are really buying, what it will take to scale, and which risks matter most.
Why expert technical diligence matters
- Industry studies routinely show 50–70% of tech deals underperform because integration, security, or talent risks surface after close. Early detection lets you price, structure, and plan with confidence.
- Most high-growth tech companies are cash-flow negative and expensive to own; a few technical details like data leverage, latency, and reliability often decide whether enterprise value shows up.
- AI-heavy businesses can hide large training and inference costs inside “software” margins; surfacing true cost-to-serve and scalability keeps the thesis intact.
- Niche or vertical AI providers sell complex, practitioner-specific capabilities that financials alone can’t validate; a practitioner lens separates real defensibility from positioning.
- Talent and leadership depth drive execution velocity; without the right AI and infra expertise, even good code and models stall.
- Security, safety, and reliability gaps can pause go-to-market for quarters or force costly rewrites; catching them pre-close protects both downside and the growth story.
How we add value
We work as an embedded partner to your deal team—responsive, candid, and concise. You get a clear point of view, not a checkbox exercise. We triangulate product claims with architecture, code, operations, and market context so you receive an integrated assessment rather than siloed findings.
What we evaluate
- Technical legitimacy and defensibility: Depth checks on models, data pipelines, and IP to distinguish proprietary work from wrappers, duct tape, or vendor lock-in.
- Market position and competitive edge: How the technical stack supports unique moat—data network effects, latency advantages, vertical depth, modeling flywheels, or integration surface area competitors lack.
- ML/AI rigor: Model lifecycle discipline, evals and red-teaming, prompt/feature governance, drift monitoring, and the credibility of the roadmap versus talent and data access.
- Infrastructure strength and cost-to-serve: Inference and training topology, scaling patterns, observability, and whether unit economics hold under real usage.
- Security and reliability: Isolation of data and secrets, model/input security, abuse handling, incident posture, and resilience at the LLM/data layer and surrounding services.
- Technical leadership and team balance: Track record of the CTO/heads of AI, hiring pipeline, pairing of research and production engineering, and the ability to execute roadmap and integration demands.
- Execution quality and velocity: Shipping cadence, test and deploy discipline for models and services, and how quickly the team can respond to market shifts or competitive launches.
Deliverables you receive
- Investor-ready memo summarizing findings, risks, mitigations, and recommended deal structures (e.g., holdbacks, covenants, earnouts tied to technical milestones).
- In-depth supporting report with deep dive elaborating findings with
- 30/60/90-day action plan (in activist scenarios) with the highest-ROI steps to stabilize, integrate, or accelerate.
- Q&A access through close so you can react quickly to competing bids, board questions, or seller pushback.
Our thorough, transparent process
We tailor the cadence to each deal and will happily compress timelines for urgent transactions. Here’s a representative example process:
1) Thesis alignment (Day 0)
We anchor on the AI thesis: where moat should come from (data, latency, vertical depth, integrations). Then align objectives so the diligence targets what’s needed for the deal.
2) Product and claims verification (Day 1–2)
Hands-on product runs when relevant, demo replication, and doc review to map claims to what’s live versus aspirational across models, features, and SLAs.
3) Model and data deep dive (Day 2–4)
Inspect model stack, training/eval pipelines, data sources and quality, guardrails, prompt/feature governance, and how drift is detected and handled.
4) Infrastructure and cost review (Day 4–7)
Trace inference/training topology, latency and reliability posture, observability, vendor reliance, and unit economics under realistic load.
5) Security, safety, and reliability pass (Day 5–8)
Data isolation, secrets and access patterns, abuse and prompt-injection handling, incident posture, and resilience across AI and surrounding services.
6) Team and leadership interviews (Day 6–9)
Conversations with AI/engineering/product leads to test technical judgment, roadmap credibility, hiring pipeline, and ability to integrate or accelerate.
7) Market and competitive cross-check (Day 7–10)
Connect technical realities to competitive edge: differentiation against foundation model vendors and peers, integration surface area, and roadmap feasibility.
8) Synthesis and scenario planning (Day 10–11)
Risks, mitigations, and scenarios tied to cost, speed, and moat: base case, upside with targeted investment, and downside with contingencies.
9) Executive readout (Day 12)
Concise memo and direct answers to valuation, structure, and day-one operating questions.
Who this is for
- Investors assessing AI, data, or infrastructure targets where the code, models, services, and operations are central to enterprise value. We cover both private and public investment targets.
- Acquirers planning carve-outs or integrations who need clarity on risks and plans.
- Boards and investment committees seeking an independent view to validate or challenge the sponsor’s thesis.
Engagement model
- Pre-LOI chat: Initial scan to discuss offer structure and exclusivity.
- Diligence (0–3 weeks): Execution of a process like above including deep sampling and leadership interviews.
- Deal-close support and follow-up: Follow-up Q&A or additional services to ensure everything is in place to close the deal.
If you want a diligence partner who is technically rigorous, commercially minded, and straightforward to work with, let’s talk. We’ll help you avoid expensive surprises and move into the deal with conviction.
