Know your judges before they know your demo.

Ten years inside MongoDB, ex-Principal Consulting Engineer — he has watched 415 people become 6,000. He genuinely cares whether developers will love the thing, and he keeps a repo of hackathon demo apps. Atlas end-to-end, security, and a story you can retell.

Pitch it asTell it like a developer-experience story: what was painful, what MongoDB made easy, who else can use it tomorrow.

An engineering manager judges the system behind the demo — data model, scaling path, failure modes, and how you'd productionise it next week without rewriting everything.

Pitch it asSay how it breaks. Bring a schema diagram and name the bottleneck you'd hit at 10× load and how you'd shard around it.

Founder of the KL MongoDB User Group (800+ members) and a run of monthly hands-on workshops. He learns a curriculum cold before teaching it. Loves best practices, vector search / RAG, and projects a stranger could follow and rebuild.

Pitch it asMake it teachable. If someone could follow your README home, he's already sold.

A solutions architect sells. His whole job is walking a customer from “interesting” to “we'll buy this” — clean data model, obvious business problem, a demo that survives a skeptical room.

Pitch it asFrame it as a solution to a named customer's problem, not a clever build. Polish the 3-minute path.

Ex-Infosys engineer now advising the most strategic healthcare accounts. He runs workshops on “why MongoDB is the database for enterprise GenAI” and “thinking in documents” — and pairs it with Kafka/Confluent streaming.

Pitch it asShow documents doing what tables can't, plus a real-time pipeline. Healthcare or another regulated vertical is a bonus.

A PM sees delivery, not just demo-day magic — scope that held, a team that actually coordinated, decisions made and trade-offs accepted under a clock.

Pitch it asShow the plan and the pivots: what you cut on purpose and why. Coherence under a deadline reads as maturity.

A senior engineer at the database company reads code the way an editor reads prose. Idiomatic drivers, sensible indexes, aggregation instead of N+1 loops, clean architecture.

Pitch it asHave the repo open. Be ready to defend one hard technical decision in detail.

A builder on the database team — he responds to elegant implementation and genuine technical chops rather than slideware. Small, sharp, well-engineered beats big and vague.

Pitch it asKeep it honest and precise. Depth in one area beats shallow coverage of five.

Advocates Java/Spring/PHP and builds things — including a Minecraft ↔ MongoDB integration — to break them on purpose and teach. She's a storyteller: dev experience, open source, demos with personality.

Pitch it asMake the demo entertaining and useful. If it would make a good tutorial video, you've won her.

Six years at MongoDB in technical services across Sydney, Singapore, Ireland, Delhi and Bangalore, now running replica sets, sharding and Kafka pipelines in production. Deep on performance, aggregation and vector search.

Pitch it asExpect real questions about scale, query plans and reliability. Answer with numbers, not adjectives.

The “voice of developers” on the AWS re:Invent leadership team, ex-AWS ML Hero, and an Agentic AI Foundation ambassador who evangelises A2A, MCP and Strands/Bedrock AgentCore. Mathematician, ex-fashion model, self-taught coder — she rewards work that makes agents feel buildable, not buzzworthy.

Pitch it asUse agents for a reason a non-AI person would nod at. Bonus points for MCP or agent-to-agent handoffs done properly.

Ex-AI engineer at Pinecone, TigerData and Infinitus, now “chief propaganda officer” at OpenRouter — he shipped their MCP server himself. He thinks in model routing, price/quality trade-offs, evals and developer tooling.

Pitch it asShow interesting model usage: routing, fallbacks, cost curves or evals. Name the models you chose and why.

An MTS at Vercel in NYC — the company that lives and dies on developer experience. TypeScript, edge/serverless, fast pages, and craft in the details users feel.

Pitch it asShip a genuinely fast, polished UI. Bonus: it's actually deployed, and the DX story is as good as the product.

Built dashboards for McDonald's and Amazon, then realised technical skill was the bottleneck — so he built an agentic, no-code analytics platform that lets non-technical managers “chat with their data”. Ex-CTO, serial founder, deep in sports & live-experience data.

Pitch it asShow the business insight, not the pipeline. Who gets a decision they couldn't make before, and what's it worth?

Advised billion-dollar software M&A at Evercore and Rothschild in London, then crossed to the founding table to build AI-native content systems for B2B teams. Early hire at a YC company, ODF Fellow, and he had to start shipping — he respects a narrative you can distribute.

Pitch it asLead with a crisp founder story and a channel. Where do the first 100 users come from?

Built deep-research agents for HR data at Chapters, sold it to Clay, and now builds agents for GTM teams. Ex-Susquehanna quant, early at Coil, Duke CS — he measures “AI agents for complex people problems” against real production use.

Pitch it asShow agent depth with real data: how it reasons, how you measured it, and why the naive version failed.

NEA's technology team across consumer and enterprise, ex-BofA TMT banker, Columbia. He backs founders at every stage and sources with “genuine curiosity”, so he's pattern-matching markets, teams and timing — not your tech stack.

Pitch it asAnswer why now, why this market is big, and why this team wins. The demo is evidence, not the pitch.

Long Lake is the AI roll-up backed by General Catalyst — $3B+ raised, 35+ service businesses owned, and a $6.3B take-private of Amex GBT. They buy companies, deploy their own AI, and measure the result on the P&L.

Pitch it asQuantify the operational win: hours removed, cost per task down, margin up. He buys cash-flow improvement, not features.