Most agentic apps look impressive until you realize they have no real brain.
They generate answers, then the knowledge disappears when the session ends. The dataset never improves. The agent never makes the system smarter.
That is the exact gap we tested with EasyBD.
EasyBD is a functional Web3 partner-match showcase app. Enter any project website and it scans it, creates a structured profile, matches it against a living Web3 intelligence dataset, scores partner fit, explains collaboration angles, flags risks, and generates ready-to-send BD briefs.
We built the first version in under 30 minutes using ChatGPT, Emergent, and Inflectiv.
It is still a showcase, not production, but the core loop already works.
The Stack
The build was simple:
ChatGPT free version for the product blueprint and prompts.
Emergent’s $1 first-month offer with 100 credits for the app layer.
Inflectiv’s free starting tier with 500 credits and 10 API credits for the intelligence layer.
Small budget. Real agentic product.
Step 1: ChatGPT Created the Blueprint
Web3 BD is still painfully manual.
Teams waste hours jumping between websites, docs, X, GitHub, CoinGecko, ecosystem pages, funding news, and old partnership posts, only to still ask the same question:
Who should we actually partner with?
ChatGPT turned that chaos into a clean flow:
Scan a project website → understand what it does → build a structured profile → match against a Web3 dataset → return fit scores, risks, BD angles, and ready-to-send briefs.
Step 2: Emergent Built the App Layer
Emergent turned the blueprint into a working app in minutes: homepage, partner match flow, scanned profile view, match cards, score UI, and BD brief generator.
The speed was impressive, but the UI was not the unlock.
The unlock was what the app was connected to.
Step 3: Inflectiv Gave EasyBD a Brain
EasyBD runs on a living, structured Web3 project intelligence dataset.
Check it here: https://app.inflectiv.ai/marketplace/211
The agent does not just read from it. It writes back too.
Scan a project → the agent checks Inflectiv.
Doesn’t exist? It creates a new structured profile.
Data changed? It updates the record automatically.
Every user query becomes new intelligence. A scan becomes a profile. A match becomes a signal. The dataset compounds with every run.
What the Output Looks Like
Instead of a random list of “possible partners,” you get something like:
“92% fit. Shared developer audience. Strong co-marketing angle around data infrastructure. Suggested approach: joint builder campaign. Risk: limited recent public activity, verify before outreach.”
That is a real BD starting point.
Why This Pattern Matters
Most AI apps are one-way: read context, generate an answer, stop.
EasyBD shows the other pattern: read structured data, reason, then write learnings back through Inflectiv’s bi-directional API.
The same architecture works for grant discovery, VC matching, compliance, customer intelligence, internal knowledge bases, and any vertical where data gets smarter over time.
The Takeaway
We did not build another pretty wrapper.
We built a showcase of what happens when an app is connected to a living intelligence layer from day one.
ChatGPT gave the blueprint.
Emergent gave the app layer.
Inflectiv gave it memory and the ability to improve.
Try it here: https://partner-match-13.emergent.host/
Should we finish EasyBD and give it away free to every Web3 BD team? Let us know
