A Farm/Ranch/Equine specialty book that a previous owner gave up migrating. A black-box source system with no documentation. Six months to deliver what others estimated would take twelve.
The book was agricultural specialty — Farm, Ranch, and Equine lines acquired from an insurer that had tried and failed to migrate it. The previous owner estimated twelve months to move the data, assuming a well-documented source system. When they discovered the source was effectively a black box — no schema documentation, no vendor support, no data dictionary — they abandoned the effort entirely and sold the book.
The acquiring carrier needed the book on their target platform within six months. The acquisition economics depended on consolidation. Every month on dual platforms eroded the value of the deal. They had a hard deadline and a source system that nobody had successfully mapped.
Farm/Ranch/Equine is not a standard commercial lines book. Policy structures reflect the realities of agricultural risk — livestock valuations that fluctuate seasonally, equipment schedules that span decades of machinery, property coverage for structures that range from single-barn operations to multi-thousand-acre ranches. The endorsement patterns are domain-specific and deeply nested.
The black-box source compounded every challenge. Field names were cryptic. Data types were inconsistent. Business rules were embedded in application logic that no longer existed. Previous migration attempts had relied on interviewing the original underwriters — but those underwriters had left with the selling insurer.
KeystoneMigrate's discovery engine mapped the black-box source system programmatically. Rather than relying on documentation that didn't exist or interviews with people who had left, the platform analysed the actual data — inferring field purposes from data patterns, identifying relationships between tables through value correlation, and building a working schema model from the ground up.
AI-powered semantic similarity was used for reference data deduplication — identifying that 'Equine - Thoroughbred', 'Horse - TB', and 'Thoroughbred Horse' were the same risk class across different policy vintages. AI-powered unknown field inference used proximate data clusters to determine the purpose of cryptic fields that had no documentation.
Evidence produced
A complete source system data model built entirely from data analysis — validated against the actual policy records, not against non-existent documentation.
Every record was migrated programmatically. No manual rekeying. No underwriter data entry. No spreadsheet-based workarounds. The migration rules were codified, versioned, and repeatable — meaning any anomaly could be traced back to its source and the rule adjusted without affecting previously validated records.
The 100% programmatic approach was critical for a book this size and complexity. Manual rekeying would have introduced errors at scale and made the six-month deadline impossible. The programmatic methodology meant that when edge cases surfaced — and agricultural specialty books have many — the fix was a rule change, not a data entry correction.
Evidence produced
Full migration audit trail showing zero manual data entry. Every record transformation traceable to a versioned migration rule.
KeystoneMigrate achieved 96% source-to-target data mapping coverage — from a system that had no documentation and no vendor support. The remaining 4% comprised fields that were genuinely obsolete or contained no meaningful data (empty columns, deprecated flags, legacy system artefacts with no business value).
The 96% figure is particularly significant given the source system. Migrations from well-documented source systems typically target 90-95% mapping coverage. Achieving 96% from a completely unknown source demonstrated that the AI-powered discovery approach could exceed the results of traditional documentation-dependent methods.
Evidence produced
Field-level mapping report showing every source field, its mapped target, the mapping method (direct, transformed, inferred), and the rationale for unmapped fields.
Data mapping coverage
Achieved from a black-box source system with no documentation or vendor support. The remaining 4% were genuinely obsolete fields with no business value.
Delivery timeline
Delivered in 6 months from a completely unknown source system — half the 12-month estimate that assumed a documented source.
Programmatic migration
Every record migrated through versioned, auditable migration rules. No manual data entry at any stage of the engagement.
Manual rekeying
Zero rekeying across the entire book. Every transformation was programmatic and traceable to a versioned migration rule.
Client and target platform anonymised. All metrics are from the actual engagement.
Every migration starts with a different source system, a different book, and a different deadline. But the methodology is the same: discover, map, migrate programmatically, and prove it worked before cutover.