Work
Case studies from my product work, each built around one hard call. Kaleidofin results are shown as percentages, multiples and ratios.
Platform and growth
Kaleidofin From custom models to a multi-product lending platform 6–12 months ~1 month · Partner onboarding Scienaptic AI Land and expand: taking a decisioning platform from $2M to $10M $2M $10M · Platform revenue
Stop custom builds: said no to per-client models despite near-term revenue risk, in favour of configurable products.
Expand before chasing new logos: weight the roadmap toward products the existing 100+ lenders could add.
AI in credit
Kaleidofin Dada Biashara: a GenAI financial coach on WhatsApp
Coach first, lend second: treat a decline as the start of a plan, not the end of the funnel.
+33% · Approvals after coaching Scienaptic AI iCUE: putting LLMs into production for US lenders, starting where it was safeStart LLMs at monitoring booked loans, not at application decisions, and bundle three tools into one module.
80% · Client adoptionCapital markets and securitisation
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