Reconciliation & Data Harmonisation

Your data lives in several places (the core system, the spreadsheet, the portal, the inbox) and they disagree. The same customer, vehicle, policy, or matter has three slightly different versions, and nobody is sure which is right. Every downstream automation, report, and decision inherits that mess. You can't safely integrate or orchestrate anything on top of data that contradicts itself, which is why this is the rung where most ambitious AI projects quietly stall.

• A harmonisation layer that ingests your sources and matches records across them, including the fuzzy, near-duplicate cases exact matching misses.
• Conflicts resolved according to rules you set and we encode.
• Where rules can't decide, the conflict is surfaced for a human to resolve, not silently guessed.
• A golden record per entity, the single trusted version, with lineage preserved so you can see where each field came from and why it won.
• The 'systems agree with each other' rung, and the foundation that makes write-back and portal agents safe.

• A source-by-source data audit identifying overlaps, conflicts, and gaps
• An entity-matching engine, including fuzzy and probabilistic matching for near-duplicates
• A conflict-resolution ruleset, encoded and version-controlled, with human escalation for unresolved cases
• Golden records per entity, with full field-level lineage
• A reconciliation dashboard showing match rates, conflicts resolved, and exceptions outstanding
• An ongoing harmonisation process so records stay clean as new data arrives