Regulated enterprises — banks, insurers, wealth managers — run on paperwork that requires human judgment: loan applications, claims, onboarding forms, compliance reviews. Generic GenAI tools promise to automate this, but fall apart in production, where every action has to be explainable, auditable, and right. A bank can't deploy a black box that occasionally hallucinates a number on a loan file.
Fisent built BizAI, an agentic AI platform purpose-built for what they call the "last mile" of enterprise automation — classifying, splitting, extracting, tabulating, verifying and analyzing unstructured content, with a confidence score attached to every output and zero data retention by default. It's designed from the ground up for environments where "probably right" isn't good enough.
In brief:
"Enterprises are moving beyond AI experimentation and choosing the capabilities they can trust to operate at scale."— Adrian Murray
Adrian Murray — Founder & CEO. Visionary leader focusing on "last mile" enterprise AI automation; committed to building trusted, scalable AI solutions for regulated industries.
Brent Baiotto — COO. Experienced operational leader driving Fisent's growth, customer success, and strategic partnerships; instrumental in scaling Fisent's enterprise platform to meet the demands of highly regulated markets.
Retention numbers that speak for themselves. 173% net revenue retention and zero churn for three straight years is a rare combination — it means existing customers aren't just staying, they're expanding usage, which is the clearest signal a product actually works in production.
Built for the industries everyone else avoids. Most horizontal GenAI tools sidestep regulated, high-stakes document workflows because the tolerance for error is so low. Fisent built specifically for that environment — confidence scoring, model flexibility, zero data retention — rather than trying to retrofit a consumer tool for compliance.
Proof at Fortune 50 scale. Landing a first Fortune 50 customer in 2026, with more in the pipeline, shows this isn't just working for mid-market pilots — it holds up under the scrutiny of the largest, most risk-averse buyers.
A platform, not a point solution. BizAI's six-action framework (classify, split, extract, tabulate, verify, analyze) covers the full content-processing pipeline rather than one narrow task, which gives Fisent room to expand within every account it lands.
How it works, in plain terms. BizAI ingests unstructured content — documents, forms, claims, applications — and runs it through six core actions: classifying what it is, splitting it into its component parts, extracting the relevant data, tabulating it into structured form, verifying the result, and analyzing it for downstream decisions. Every output carries a confidence score, so anything uncertain gets flagged for a human rather than passed through silently.
The problem it solves. Regulated enterprises can't hand sensitive documents to an opaque model and trust the output blind — they need to know how confident the system is, be able to audit what it did, and keep control over where their data goes. BizAI's confidence scoring and zero-retention-by-default design are built to close exactly that trust gap.
Key features and benefits:
