Technical DD & Advisory

Context

Technical decisions become expensive when they are made too late: the wrong first hire, a brittle architecture, an AI feature built on messy data, a vendor commitment that should have been a prototype, or a prototype stretched into production.

I help investors, founders, and operating teams make those calls earlier. Having sat on both sides of the table - as a startup CTO building under pressure and as an engineer inside Iterativeiterative.vcEarly-stage VC fund investing up to US$500K in startups twice a year, working closely with founders for 3 months — 70+ portfolio companies across Southeast Asia and South Asia.Iterative evaluating hundreds of startups and advising 70+ portfolio companies across Southeast Asia and South Asia - I assess teams, codebases, data models, and architectures with both investor skepticism and founder empathy.

The advisory lens is practical: understand the business workflow first, inspect the actual technical system, then decide what deserves software, what should remain manual, what should be bought, and what needs to be rebuilt.

What I Did

For Investors

• Pre-Investment DD: Review codebases, architecture, infrastructure, AI claims, data pipelines, security posture, and team execution risk before investment decisions.
• Technical Moat Assessment: Separate real engineering advantage from rehearsed technical narratives: proprietary data, workflow depth, model dependence, integration complexity, defensibility, and maintainability.
• Portfolio Assessments: Evaluate build-vs-buy decisions, scalability risk, roadmap realism, and whether a company's internal systems can survive the next stage of growth.

For Founders

• Architecture & Stack Advisory: Make stack, system design, vendor, AI, and automation decisions while the choices are still cheap to change.
• Hiring & Team Design: Shape first-engineer hiring, technical leadership needs, fractional CTO scopes, and build plans that match the stage of the company.
• Fundraising Technical Prep: Turn fragile technical claims into a clear architecture narrative investors can scrutinise.
• Operational System Design: Decide when to use off-the-shelf tools, when to automate, and when to build custom software because the workflow needs stronger configuration, auditability, or control.

Outcome

• Investors get a clearer read on technical risk before capital is committed
• Founders make architecture, hiring, AI, automation, and product decisions earlier
• Costly missteps caught before they become rewrites, vendor lock-in, or brittle production systems
• Technical narratives become easier for investors, operators, and teams to trust
• Decisions grounded in the actual workflow, not just the pitch or the codebase

Stack & Use Cases

Next.jsnextjs.orgReact framework for production web apps — server rendering, routing, and the fastest path from idea to deployed product.Next.jsPythonpython.orgThe lingua franca of data and AI engineering — used here for ML pipelines, voice processing, and backend services.PythonPostgreSQLpostgresql.orgThe world's most advanced open-source relational database — the default choice for production data that has to be correct.PostgreSQLAirtableairtable.comSpreadsheet-database hybrid — the operational data layer for deal flow, portfolio tracking, and internal tooling at fund scale.AirtableNotionnotion.comConnected workspace for docs, wikis, and databases — where team knowledge lives and what internal tools read from and write to.NotionSlackslack.comTeam messaging platform — the front-end where automations deliver their output: alerts, digests, and natural-language queries.SlackMCPmodelcontextprotocol.ioModel Context Protocol — the open standard for connecting AI models to external tools, data sources, and systems. The backbone of production agent integrations.MCPTechnical due diligenceArchitecture reviewAI feasibilityBuild-vs-buySecurity postureHiring planRoadmap validationFundraising prep