DataForge

First Projects

First projects. Real data.

No overclaiming. No figures that cannot be verified. Only what ProcessForge has made visible in real organizations — as it was.

Three projects. Three data sets. The same structural gap — made visible in the data.

01

industrial/sustainability

Starting situation

An industrial manufacturer was preparing for a new round of sustainability commitments. Their reporting covered emission outputs and energy consumption, but could not connect these to which upstream processes drove them. Decisions about where to invest were made on assumption.

What ProcessForge made visible

Six process clusters accounted for the bulk of the measurable sustainability impact. Three of the organization's active initiatives mapped to high-impact areas — two did not. An additional optimization pathway had not been in scope because no model had identified it as relevant. ProcessForge made the causal structure readable.

The data was there. The link between action and impact was not.

02

healthcare/cost-optimization

Starting situation

A care provider was under sustained budget pressure. Two previous optimization projects had been commissioned — neither produced implementable recommendations. The models had not accounted for the specific care pathway dependencies, staffing constraints, and regulatory requirements that defined how this organization actually operated.

What ProcessForge made visible

A set of handover and scheduling dependencies that created predictable downstream overhead. These were not visible in aggregated cost reports or activity logs. Addressing them did not require operational restructuring — only making the connections between decisions visible.

The savings potential was already there. It had never been specifically located.

03

sales/process-intelligence

Starting situation

A B2B sales team had a documented process and wide result variance. The gap between top and bottom performers was significant and consistent. No one knew what drove it. CRM data showed activity volume — not interaction quality.

What ProcessForge made visible

Distinct structural patterns in how high-performing interactions handled qualification, objections, and next-step progression — identifiable, consistent, based on actual conversation data. The pattern was not visible through observation. It only became legible when the full interaction data was read systematically.

What high performers did differently had never been extracted. Now it could be taught.

Show us one of your data sources.

We show you what ProcessForge uncovers. No presentation. Your data — our analysis.