Case studies

What we built, and what it moved.

Real AI engineering engagements with before-and-after metrics—workflow automation, operational dashboards, and adaptive training in logistics, construction, and commercial real estate. Names withheld; reference calls available under NDA.

01 in production

Fleet transportation — customer workflow automation

L1/L2 booking automation plus an ops dashboard and feedback loops for a regional fleet operator—quote-to-booking cut from 6.2 hours to 1.4.

6.2h → 1.4h quote-to-booking · 2.3× volume, same team
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02 in production

Construction — project intelligence & BD engine

WhatsApp project intelligence, a BD scoring engine, and custom drawing-to-BOQ intake for a commercial builder—win rate on qualified tenders rose from 14% to 21%.

Drawing-to-draft BOQ same-day · win rate 14% → 21%
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03 in production

Logistics — AI-driven adaptive learning & ERP training

Role-aware RAG tutoring on a legacy ERP for drivers, dispatch, and sales—onboarding dropped from ~4.5 weeks to ~1.3 and fuel-logging errors fell ~90%.

ERP errors −90% · onboarding ~4.5 wks → ~1.3 wks
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04 in production

Commercial property — tenant maintenance SLA engine

Lease-backed SLA routing across email, WhatsApp, and front-desk intake for a multi-building portfolio—breach rate fell from 23% to 7%.

SLA breach rate 23% → 7% · duplicate dispatches 31% → 6%
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Want to see what this looks like for your operation?

Four engagements, same pattern: build close to the work, measure what moves.

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