Field note from the c51 / REALM build: what we learned designing the AgTech Commercialisation Navigator.
We just shipped the REALM Australia AgTech Commercialisation Navigator as a free public resource. The AI lesson underneath it is the interesting part for this community.
Link (free, no email wall): navigator.realmgroup.global
The surface problem looked like search:
- 20+ programs, 9 states, $2B+ in funding, 5 commercialisation stages
- Founders, farmers and researchers asking the same question: "which one is for me?"
The naive build is a database with filters. The reason that fails is the same reason most "AI for ecosystems" tools fail: program data is heterogeneous, eligibility logic is fuzzy, stage taxonomies disagree, and the user often doesn't know their own stage.
What we ended up building underneath:
1. A canonical entity model for "program" - one identity, multiple aliases across funder language
2. A canonical stage taxonomy (Discover -> Validate -> Pilot -> Deploy -> Scale) that programs and users both map onto, instead of free-text
3. A provenance trail on every program record - source, last verified, eligibility caveats
4. A small, deterministic Quick Start Wizard (4 questions) instead of a chat-style LLM front door, because for funding navigation you want predictable matching, not creative interpretation
5. AI sits on top for ranking and adjacency suggestions, but never as the source of truth
The broader pattern, which is the same one we apply inside REALM360 / KAOS:
- Identity before optimisation
- Provenance before prediction
- Deterministic logic where users have to trust the output
- Generative AI where exploration helps, not where accountability matters
A "free, no application, no cost" tool can only be trusted if the substrate underneath is auditable. That's the actual work.
If you're building ecosystem-mapping tools, funding navigators, or any AI layer that has to sit on top of messy multi-source program data - DM open.
#AIArchitecture #AgTech #REALM360 #KAOS #EcosystemMapping
