Case Studies

Proof in practice.

Each study shows the same pattern: map the operating logic, build dependable systems, and connect fragmented data into something the team can actually use.

We've worked with teams at a leading US Polymer Trading Firm, Kinnect, and IIDE.

Commodity Trading

Cash flow that took hours now takes minutes.

US Polymer Trading Firm

The business had fragmented systems, manual forecasting, and critical knowledge concentrated in a few people. We ran a full knowledge audit, mapped the operating logic, and built self-checking pipelines for cash flow forecasting, compliance reporting, and accounting workflows. We also unified entities scattered across multiple tools into a single identity layer and delivered inventory intelligence tools for bundling and scenario-based pricing.

Architecture

Self-validating workflows across forecasting, reporting, and accounting. Entity resolution underneath. Inventory intelligence layer on top.

Digital Creative

AI adoption, made governed and repeatable.

Kinnect

The agency wanted to adopt AI across the team, but usage was inconsistent and there was no shared standard for quality. We built an operating philosophy for how AI should be used, clarified the logic behind data integrity and tool choice, and created reusable templates that made the workflow repeatable across functions.

Architecture

AI governance framework, operating philosophy, reusable prompt and template systems, and process-capture layers built into the existing toolchain.

Education Technology

Raw calls became a decision-making layer.

IIDE

The team had thousands of sales calls but no reliable way to turn them into usable intelligence. We built AI-powered sensors on call recordings, surfaced competitive signals, cleaned up the pipeline underneath, and delivered dashboards that turned noise into daily operational input.

Architecture

Sensor layer on call recordings, upstream data pipeline cleanup, and dashboards surfaced across teams.

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