AI agents for unstructured data extraction, validation, and automated ETL.

Watches every inbound channel (email, portals, SFTP) and classifies what arrives without templated sorting. Identifies invoices, claim forms, manifests, and handwritten notes, routing them automatically. Ambiguous items go to a human reviewer while everything else flows.

Validates customer and vendor forms at submission. Missing fields trigger automatic, plain-language follow-ups. Complete submissions pass directly to processing, while incomplete ones sit in a managed queue, not someone's inbox.

Reads incoming documents and decides which workflow they belong to — distinguishing an invoice from a PO, a new policy from a renewal, or a complaint from an enquiry. Routes items into the correct downstream process automatically.

Extracts structured data regardless of format or complexity. Understands totals vs. line items, issue dates vs. expiry dates. Maintains accuracy across hundreds of pages. Every extracted field includes a confidence score.

Specializes in financial models, rent rolls, loss runs, and multi-period P&L statements embedded in PDFs. Handles merged cells, multi-row headers, and inconsistent formatting across dozens of pages to produce clean structured data.

Checks extracted data against mathematical consistency, cross-system verification, approval criteria, and anomaly detection. When an item fails, it explains the issue in plain language, holds the record, and routes it with full context to the right person.

Runs continuously across your CRM and ERP identifying duplicates, inconsistent entries, and fragmented data. Merges misspelled variants into single clean records and standardizes formatting. Operates as a permanent digital data steward.

When internal records miss contact details, company profiles, or financial data, this agent queries external sources sequentially until verified values are found. It writes enriched data back with an audit trail, keeping records continuously updated.

Replaces manually coded data pipelines. A business user describes what they need in plain English ('combine sales data with inventory'). The agent builds, tests, and deploys the code. If source system formats drift, the agent adapts automatically.

Logs into enterprise systems to enter validated data. Maps fields correctly handling variations in names or schemas. Creates new records, updates them, and confirms completion. Uses APIs where available or screen-level interaction for legacy systems.

Monitors connected systems continuously and generates reports proactively. When it detects an anomaly (revenue drop, supply delay), it runs hypothesis testing and writes a natural language report explaining what happened and why.

Sits above the agent pipeline. Pauses actions when confidence drops. Distinguishes timing lags from errors, suppresses false alarms, and escalates true exceptions with full context and recommendations to human reviewers.