Problem
A Big 4 Consulting Firm works with several clients, ingesting data from dozens of ERP systems, sub-ledgers, and regional databases. Each new client brings thousands of columns with inconsistent or misleading names (“Acct_No,” “LedgerKey,” “Col_1”), sometimes with no semantic clues at all. Analysts often had to infer meaning from sample values and related tables, a slow and error-prone process. Traditional fuzzy logic and regex mapping broke down at scale - unable to interpret fragmented context or ambiguous overlaps.The result: highly skilled auditors were spending disproportionate time on grunt work, while audit timelines stretched unnecessarily.
Context-Aware Mapping
Cimba interpreted meaning not just from column headers but also sample values, related tables, and domain playbooks to infer true intent.
Adaptive LLM Intelligence
Agents distinguished ambiguous fields (e.g., “ID” as Invoice Number in one dataset vs Customer ID in another), succeeding where fuzzy logic failed.
Human-in-the-Loop Confidence
Mappings with high confidence were auto-accepted, while edge cases were flagged for review. Every correction fed back into the system, improving future accuracy.
Impact
Faster, Smarter Audits
Cimba transformed data preparation from a bottleneck into a finance enabler, cutting time-to-audit and reducing reconciliation errors at scale.
~50%
Reduction in data ingesting and mapping time - audits began in record time.
30–40%
Fewer reconciliation errors downstream, reducing audit adjustments and strengthening regulator and client confidence.
