Inncivio offers an AI‑driven, agentic layer for transaction‑focused fintech platforms that analyzes real‑time user behavior and contextual UI data to deliver personalized micro‑interventions—such as educational snippets, insights, or call‑to‑actions—directly within the existing interface. By providing just‑in‑time guidance at critical decision moments, it helps reduce hesitation and abandonment, boosting transaction volume and revenue without requiring product redesign.
Funding
$150K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Fintech platforms such as trading apps, crypto exchanges, and lending marketplaces often see users hesitate or abandon transactions at critical decision points because the interface lacks real‑time, context‑aware guidance. This friction reduces transaction volume and overall revenue per user.
Solution
Inncivio provides an AI‑driven, agentic layer that sits on top of transaction‑driven fintech products. By ingesting real‑time user behavior (“decision traces”) and a contextual graph of the UI, product state, and knowledge sources, the system predicts the next step a user is likely to need. It then injects personalized micro‑interventions—educational snippets, informational summaries, or actionable call‑to‑actions—directly into the existing interface without requiring product redesign. The guidance is delivered at the precise moment of decision, adapting continuously through reinforcement learning to each user’s tolerance and preferences, thereby converting intent into completed transactions and improving transaction quality.
Target Audience
Primary customers are fintech platforms that generate revenue through user transactions, including retail and institutional trading platforms, crypto exchanges, FX/remittance services, lending marketplaces, and betting/gaming products.
Features
- Real‑time analysis of decision traces combined with a context graph to understand user intent and platform state
- Proprietary AI stack: UI clustering model, LLM + RAG for contextual content generation, and reinforcement‑learning engine for optimal micro‑intervention timing
- Non‑intrusive, agentic UI injection that surfaces tailored education, insights, or CTAs without modifying the host product
- Simple integration: single JavaScript snippet for web or SDK/API for mobile applications
- Privacy‑first data handling with anonymized traces, encrypted transmission, and configurable regional data residency
- Built‑in guardrails and tone configuration allowing clients to set content policies and limit exposure