01
Material risk, separated from noise
Distinguish normal startup trade-offs from issues that could change valuation, deal terms, integration plans, or time to value.
Technical due diligence
See the technology clearly. Decide with confidence.
Independent technical due diligence that connects architecture, product, data, AI, security, and team capability to the investment thesis.
Decision support / 01
Know what you are underwriting and what comes next.
A useful diligence review does more than count defects. It identifies which technical realities could affect growth, margin, resilience, customer commitments, or the ability to execute the value-creation plan. Findings are prioritized by materiality and translated into practical next actions.
02
A credible post-deal plan
Turn findings into sequenced priorities for the first 100 days, with dependencies, ownership, and likely delivery implications.
03
A shared technical picture
Give investment teams, boards, founders, and technology leaders a common evidence base for the decisions ahead.
Assessment scope / 02
The technology, team, and operating system behind the business.
The scope follows the investment thesis and company stage. We go deep where the decision requires it rather than applying a generic checklist.
/01
Architecture & scalability
Assess whether the current system can support expected growth, product expansion, availability needs, and integration complexity.
- System architecture
- Scalability constraints
- Cloud infrastructure
- Integration dependencies
/02
Product & roadmap
Test whether product capability, roadmap priorities, and technical choices support the commercial plan and customer commitments.
- Product maturity
- Roadmap feasibility
- Customer commitments
- Build-versus-buy choices
/03
Engineering organization
Review delivery performance, leadership, team structure, development practices, and concentration of critical knowledge.
- Team capability
- Delivery predictability
- Quality practices
- Key-person dependency
/04
Security & resilience
Identify material gaps in security, access, recoverability, operational resilience, and the evidence expected by customers.
- Security posture
- Access controls
- Incident readiness
- Business continuity
/05
Data & AI
Examine data rights and quality, model dependencies, evaluation practices, governance, defensibility, and production readiness.
- Data provenance
- Model architecture
- Evaluation evidence
- AI governance
/06
Cost & technical debt
Clarify where infrastructure costs, licensing, maintenance burden, or accumulated debt could constrain the plan.
- Cost drivers
- Vendor exposure
- Debt materiality
- Remediation effort
How we deliver / 03
Focused on the investment question.
The process is evidence-led, proportionate, and designed to minimize disruption to management while still testing the important claims.
01
Frame
Align on the thesis, deal context, material questions, scope, access, and reporting needs.
02
Investigate
Review documentation, architecture, code and delivery evidence, and interview the people accountable for the technology.
03
Test
Challenge key assumptions, trace dependencies, and distinguish confirmed findings from areas of uncertainty.
04
Advise
Deliver a concise risk-ranked report, decision implications, and practical post-deal priorities.
Questions / 04
What investment teams need to know.
01What is technical due diligence?
Technical due diligence is an independent assessment of a company's technology, product, data, security, and engineering capability. It helps an investor or acquirer understand material risks, strengths, dependencies, and the work required to support the investment plan.
02When should technical due diligence happen?
It is most useful before an investment or acquisition, but it can also support follow-on funding, board reviews, leadership changes, major platform investments, or post-deal value-creation planning.
03Do you review source code?
Where source access is appropriate, code and repository evidence can form part of the review. The depth depends on the investment question, timeline, company stage, and agreed scope.
04Can you assess an AI-native company?
Yes. AI diligence can cover data provenance and rights, model and vendor dependencies, evaluation quality, human oversight, security, unit economics, operational monitoring, and whether claimed differentiation is supported by the system.
05What does the final output include?
The output typically includes an executive view, evidence-backed findings, materiality and confidence ratings, implications for the investment thesis, and prioritized actions for management and the first 100 days.
Start with the real problem
Bring us the investment question.
Share the deal context, company stage, thesis, and technical concerns. We will shape a proportionate scope around the decision you need to make.
Talk to KeshoLet's build / 05