Data and insight systems
Account data intelligence platform
A scalable processing system that turned large account datasets into normalized, evaluated, and useful sales intelligence.
- Audience
- Sales organizations, account teams, and business leaders
- Role
- Architect, technical lead, and principal builder
The problem
Account coverage was too expensive to scale through manual enrichment.
Thousands of accounts required consistent normalization, research, evaluation, and insight generation. Manual handling made portfolio-wide intelligence slow, uneven, and cost-prohibitive.
The solution
A repeatable pipeline separated processing from judgment.
I built a modular data intelligence system for import, normalization, recursive chunking, vector processing, evaluation, and insight generation. The architecture made each stage observable and repeatable while supporting high-volume account coverage.
Outcomes
The result was a change in operating capacity.
These outcomes describe what this implementation made possible. They are not generic product promises.
5,000+
accounts processed
Moved account intelligence from isolated research to repeatable portfolio-scale processing.
~95%
lower operating cost
Reduced the economics of enrichment and insight generation through a purpose-built pipeline.
How it worked
The architecture followed the work.
The system was organized around an operating sequence people could understand, govern, and improve.
Normalize
Bring inconsistent account records into a reliable common structure.
Process
Chunk, enrich, and evaluate source material through independent pipeline stages.
Generate
Produce useful account insights with traceable inputs and repeatable behavior.
My contribution
I designed the system around scale, cost, and repeatability.
My work connected the processing architecture to the operating requirement: extend useful intelligence across the portfolio without scaling manual effort at the same rate.
Return to all work