Turn dirty data into your most reliable asset.

Duplicate records affect 10–30% of CRM databases, fragmenting pipeline data and inflating marketing spend across contacts that represent the same person or company. AI-powered fuzzy matching and entity resolution identify and merge duplicates across name variants, email aliases, and company hierarchies — reducing duplicate rates to under 2% and recovering pipeline value that was previously split across fragmented records.

Industry estimates suggest undeliverable-as-addressed mail costs businesses billions annually. Up to 20% of customer addresses contain errors — typos, outdated postcodes, missing unit numbers — that cause failed deliveries and returned shipments. AI agents validate addresses against postal authority databases in real time, standardise formatting across international schemas, and append geocoded metadata — significantly reducing shipping return rates and failed deliveries.

Enterprise product databases with 100K+ SKUs commonly suffer from inconsistent naming, missing attributes, and conflicting taxonomies across suppliers. Poor product data drives search failures and misclassification that directly erode online conversion rates. AI agents standardise product titles, extract structured attributes from free-text descriptions, and map items to unified taxonomies — reducing catalog cleanup time from months to days and improving product discoverability across channels.

Marketing databases decay at roughly 22–30% per year as contacts change jobs and emails go stale. Sending to invalid addresses pushes bounce rates above the 2% threshold that triggers ESP blacklisting, tanking deliverability across all campaigns. AI agents continuously verify email validity, detect role-based and disposable addresses, and enrich contacts with current title, company, and firmographic data from public sources — keeping bounce rates under 0.5%.

Manual reconciliation of financial records across ERP systems, bank statements, and ledgers consumes up to 30% of a typical finance team’s time. AI agents perform automated three-way matching across invoices, purchase orders, and receipts, automating 90–95% of routine transaction matching and compressing month-end close cycles from 6–10 days to under 4 — freeing finance teams to focus on analysis rather than data wrangling.

Duplicate patient records exist in 8–12% of hospital databases according to AHIMA, costing health systems over $1 million annually and creating patient safety risks from incomplete medical histories. AI-powered master patient index matching uses probabilistic algorithms across name, DOB, and demographic variants to achieve 98%+ match accuracy — dramatically reducing duplicate creation rates and preventing costly claim denials from identity mismatches.

Sales reps spend only 35% of their time actually selling — the rest is lost to admin and chasing poorly qualified leads. B2B databases contain 30–40% incomplete records missing key firmographic fields. AI agents enrich inbound leads in real time with company size, revenue, tech stack, and intent signals from public filings and web activity — increasing MQL-to-SQL conversion rates by 25–35% and reducing cost per qualified opportunity by up to 40%.

Enterprise vendor master files accumulate 20–25% anomalous or redundant records from acquisitions, regional variations, and manual entry errors, leading to duplicate payments estimated at 1–2% of total accounts payable spend. AI agents consolidate vendor master files by matching across TIN, name variants, and banking details — identifying duplicate payment exposure and standardising records to enable better spend analysis and contract negotiation leverage.

System migrations and ERP consolidations require transforming millions of records across incompatible schemas — large IT projects run 45% over budget on average according to McKinsey and Oxford research. AI agents learn source-to-target field mappings from sample data, automatically handle date formats, currency conversions, unit standardisation, and code translations across legacy systems — reducing manual data transformation effort by up to 70% and significantly compressing migration timelines.

Financial institutions must screen customers against sanctions lists, PEP databases, and adverse media — traditional systems generate false positive rates of 95–98%, consuming tens of millions annually in manual review labour at large banks. AI agents apply contextual entity resolution and semantic matching to distinguish true matches from coincidental name overlaps, reducing false positives by 60–70% while maintaining regulatory detection standards.

Poor inventory data quality causes a 3–5% revenue loss across retail and manufacturing, driven by phantom inventory, mismatched counts, and inconsistent item masters across warehouse systems. AI agents continuously reconcile stock records across POS, WMS, and ERP systems, flag statistical anomalies in count data, and standardise unit-of-measure conversions — improving inventory accuracy from typical 63% to above 95% and reducing stockout events by 30%.

Up to 80% of enterprise data is unstructured — locked in PDFs, scanned contracts, emails, and legacy documents — costing knowledge workers 2.5 hours per day searching for information. AI agents use document understanding models to extract structured fields from contracts, invoices, and forms with 95%+ accuracy, classify documents by type and urgency, and feed clean records directly into downstream systems — eliminating manual data entry that costs $2–5 per document at scale.