Two cycles already running in our own industry, the kinds of cycle we close for clients, and the symptoms by area. We don't publish results that haven't been measured.
Two cycles already running in our own industry, and the kind of cycle we close for clients. We don't publish results that haven't been measured.
A multi-agent system that senses signals on the floor, interprets priority and coordinates from order to delivery — without depending on anyone remembering each step. Five of seven modules in production.
An agent that fires the request, collects supplier replies over WhatsApp and organizes everything in one place — closing in minutes what used to take days of back-and-forth. Processing real quotations.
The order needs engineering to close scope and price, and sits for days between the two. The agent classifies, extracts data, builds a checklist of what's missing and chases the owner.
See all casesThe people designing your cycle run a real industry — engineer-to-order, production planning, B2B sales, ISO 9001 quality. We prove every agent in our own operation before bringing it to yours. This isn't slideware: it's AI that has already been through the factory floor.
Agentfy applies its methodology to commercial cycles, administrative workflows, recurring decisions, system integrations, and human-approved automations. We do not publish results that have not been measured.
Context
B2B operation with technical demand, multiple input sources, and required alignment between sales, engineering, and operations.
Solution implemented or blueprint
Request classification, data extraction, automatic missing-information checklist, engineering summary, suggested commercial next action, and status logging.
Tracked indicators
Time to first response, complete requests at intake, engineering return SLA, and quote-to-order conversion.
Context
B2B commercial operation with an active customer base and recurring purchasing behavior.
Solution implemented or blueprint
Recurrence monitoring, automatic alert, suggested message, owner task, and reason logging.
Tracked indicators
Days since last purchase, expected versus actual reorder, recovered opportunities, and recorded loss reason.
Context
Operation with recurring consumption, deadline pressure, and dependency on timely purchasing decisions.
Solution implemented or blueprint
Consumption/history reading, risk alert, impact estimate, purchasing task, and owner approval.
Tracked indicators
Urgent purchases per month, alert lead time in days, stockout avoided or recorded, and lead-time variation.
Context
Operation requiring traceability, evidence, and fast response to deviations.
Solution implemented or blueprint
Automatic classification, owner trail, evidence request, deadline alerts, and recurring cause logging.
Tracked indicators
Time until owner assignment, evidence attached on time, recurrence by cause, and days until corrective action closure.
Quote request → technical interpretation → historical reference → preliminary costing → commercial approval → follow-up → margin learning.
Expected recurring order → customer has not purchased → commercial alert → suggested outreach → reason logged → next action triggered.
Ticket received → urgency classification → SLA check → technician allocation → customer communication → root cause recorded.
Deviation detected → impact calculated → owner notified → customer informed → corrective action registered → pattern updated.
Request or quality deviation → traceability → batch analysis → responsible approval → internal communication → recurrence learning.
Field issue → image or message captured → classification → owner assigned → deadline tracked → evidence archived.
The first conversation is used to understand whether there is a clear, valuable, and feasible cycle to attack with process, data, automation, AI, and human approval.