Every metric below comes from a system we built, deployed, and handed over to a real client. These aren't projections from a strategy deck — they're production results from AI systems running today.
A global advisory firm’s consulting team was spending weeks manually reviewing 40–50 stakeholder interview transcripts per project. We built a custom AI report generation engine backed by a knowledge graph of 10,000 ingested files. The system now generates first-draft observations, implications, and recommendations.
Projected capacity increase from 10 to 15 concurrent projects per year, without adding headcount.
We built a multi-agent course generation engine that converts dense client documentation — safety manuals, SOPs, technical textbooks — into interactive digital training courses. One client alone had 40,000 SOP documents waiting for digitization — previously an impossible backlog.
Cost per generated course: under £100 in compute, compared to weeks of billable manual labor.
For a catalog of 500,000 automotive parts, we rebuilt the entire data pipeline. Titles, descriptions, bullet points, and meta tags now generate simultaneously across Amazon, eBay, and Shopify. A multi-agent enrichment system resolves ambiguous data automatically.
Full pipeline processing compressed from 9 days to a few hours. 97% of items require zero human review.
Critical signals about client health were buried across Slack, HubSpot, and Airtable. We built a client intelligence platform that ingests data from all sources, scores sentiment, flags risks, and generates bi-weekly intelligence reports delivered straight to Slack.
Total infrastructure cost: ~€150/month. Identified churn risk for a client before they actually churned.
Appraisers were spending significant time categorizing 50+ inspection photos and assessing room conditions. We reverse-engineered the decision frameworks of a senior appraiser with 13+ years of experience, building a multi-agent system that auto-fills property descriptions with color-coded confidence indicators.
Foundation for an "AI associate" vision — producing complete first drafts before a licensed appraiser touches the report.
Five clients. Five industries. One pattern: weeks of work compressed into minutes.
The numbers above tell the story of what happens in month one. But the clients who see the greatest return are the ones who stay — because every system we build is designed as a foundation, not a finished product.
What started as a single client intelligence tool for YOYABA evolved into a company-wide intelligence layer they call "Kaldi." By February 2026, the platform had 15 daily active users and became the backbone of how YOYABA runs its entire client services operation. The original engagement paid for itself many times over because the infrastructure was designed to be extended.
EG.1's knowledge graph didn't stop being useful after the first report. The system continuously ingests new documents as they land in SharePoint. With approximately 10,000 files across 70 projects already indexed, the graph gets richer and more contextually aware with each engagement. The ROI of the initial build didn't peak at delivery — it's still climbing.
Lightbulb Edtech didn't just get faster at making courses. They changed what they could say yes to. A 40,000 document backlog became a viable commercial opportunity. At Autonative, compressing the pipeline from 9 days to hours unlocked the ability to get 500,000 SKUs to market. These clients aren't just doing the same work faster — they're doing work they previously couldn't do at all.
Every system we build is designed to deliver value on day one and compound from there. We're there to identify and build the next high-impact automation before you even know it's possible.
Let's talk about your ROI →Every metric above comes from a system we built, deployed, and handed over. Let's discuss what we can build for you.
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