Jsonify Turns the Unreliable Web Into Financial-Grade Intelligence Infrastructure for the AI-Driven Enterprise

The most consequential data in financial markets rarely lives behind an API. It sits on a competitor's pricing page, inside a mobile app's promotional banner, buried in a regulatory filing updated without notice. For decades, the infrastructure for capturing that intelligence has been manual, brittle, and fundamentally unserious about verification. Jsonify, founded in Lisbon in 2023, has built a pipeline platform that treats the public web not as a scraping target but as a production data source—one that must be checked, versioned, and repaired when the world changes beneath it.

The company's timing reflects a broader reckoning in enterprise AI. Large language models can reason over information, but they cannot guarantee that the information itself is current, complete, or accurate. Jsonify's answer is architectural: a deterministic extraction pipeline built and maintained by its AI data engineer Jason that applies the same fields and checks on every scheduled run, publishes immutable dataset versions, and leaves a source, timestamp, and pipeline version for every row it delivers. When a source changes, Jason diagnoses the failure, repairs the pipeline, verifies a sample, and resumes the schedule. Nothing unverified reaches a client.

That discipline has attracted teams at Bacardi, PwC, Geopost, and Norlys, and produced a reported 98.7% field-level extraction accuracy across pipelines processing millions of rows monthly. The platform is SOC 2 Type II audited and GDPR-ready, with pricing tied to verified rows rather than pages fetched or credits burned. For financial intelligence teams navigating the 2026 mandate to ground AI outputs in auditable data, Jsonify's proposition is less about access than about trust.

The Pipeline as Financial Infrastructure

The distinction between a scraper and a pipeline matters more than it appears. A scraper returns pages. A pipeline returns a dataset that has survived type, null, and duplicate checks, with provenance attached to every value. Jsonify's architecture enforces this separation at every layer: the brief defines sources, fields, and refresh cadence; Jason builds extraction steps and confirms them against a sample; scheduled runs apply identical logic and publish versioned outputs to Sheets, Snowflake, S3, Postgres, or directly into Claude, ChatGPT, and Copilot over MCP.

For pricing and category teams, the revenue implication is direct. A competitor's promotional shift detected at 06:00 and delivered to a pricing dashboard before the morning review is worth more than a weekly snapshot that arrives after decisions have been made. Jsonify reports median pipeline repair time of 18 minutes and 46 seconds, meaning a broken source does not translate into a broken intelligence feed. The company's row-based pricing model reinforces the alignment: retries, blocked pages, and repairs carry no charge. Clients pay only for rows that pass verification and reach their dataset.

Decisions Built on Checked Rows, Not Optimistic Assumptions

The harder problem Jsonify addresses is behavioral. Financial intelligence teams have learned to distrust automated collection because the cost of a wrong number a stale price, a misparsed promotion, a missing stock signal outweighs the benefit of speed. Jsonify's response is to make verification visible. Every dataset version is immutable and comparable. Every row carries a source page, a fetch timestamp, and a pipeline version. When a repair occurs, it ships as a new revision with a changelog recording what changed and whether recovery was confirmed.

This transparency changes how teams allocate capital and attention. An analyst who can click any row and see the exact page it came from spends less time auditing inputs and more time acting on outputs. A pricing manager who knows that a pipeline held its last good result during a failed repair, rather than publishing a partial dataset, can trust the feed through a source redesign. Jsonify's enterprise agreements extend this assurance with named escalation contacts, coverage hours, and response times, and can include a perpetual license to the pipeline's Python source a continuity provision that acknowledges the difference between renting intelligence and owning the infrastructure that produces it.

The Verified Row as the Unit of Enterprise AI

Jsonify's competitive position becomes clearer against the alternatives. Fetch APIs return source content; teams still build and maintain the transformation logic. No-code robots record actions; they break silently when interfaces change. Generic AI assistants can collect a sample; they cannot sustain thousands of pages with consistent checks and repair cycles. Jsonify's unit of account the verified row is also its strategic argument: in an era when AI can generate plausible outputs from unverified inputs, the scarce resource is not intelligence but trustworthy intelligence.

The company's expansion into insurance-grade use cases, including monitoring AI Act guidance and benchmarking competitor policy language, signals where the market is heading. Financial institutions do not need more data. They need data they can defend to a regulator, a board, or a client. Jsonify has positioned its pipeline platform as the infrastructure for that defense, one checked row at a time.

Paul Hunkin, Founder & CEO

"The opportunity is validated. You did a very good job: your flexibility, the extractions, the speed… it's quite impressive. This could really become a new capability for Bacardi."

Company description:  Jsonify is operated as a data pipeline platform designed to convert public websites and mobile applications into verified datasets for pricing, category, and market-intelligence teams. Deterministic extraction steps are built by an AI data engineer and applied on scheduled runs, with every published row checked for type, null, and duplicate conditions before delivery. Pipelines are repaired automatically when sources change, and enterprise agreements can include escalation coverage and a perpetual license to the pipeline source.