Most AI projects die in the pilot. We ship the ones that reach production.
Agentfy maps your company as a network of decision loops, finds where they break, and closes the highest-value one inside the systems you already run — with an explicit human–AI authority boundary. Weeks, not quarters.
Built and proven inside a working industrial factory — not a slide deck.
The same loops break in almost every company.
Using ChatGPT isn't being AI-first.
The difference was never the tool. Your information already exists — in the ERP, in spreadsheets, in WhatsApp threads. The problem is that it never reaches the decision, synthesized, at the moment the decision is made.
A system of record stores state. A system of intelligence expresses that state in decision-ready form, exactly when it's needed. Being AI-first means closing that gap. We call it closing the loop.
“The data was already there. Nobody was deciding on it in time.”
Every decision runs a loop. It breaks at one of five points.
Sense
Does the signal even get captured?
Interpret
Is raw data turned into a decision-ready read?
Decide
Is the call made fast enough, by the right authority?
Act
Does the decision actually become execution?
Learn
Does the outcome feed the next decision, or vanish?
Data problem vs. intelligence problem
AI can't synthesize what was never captured. First we tell these apart — they need opposite fixes.
Point solution vs. closed loop
Automating a step isn't closing a loop. The loop closes only when the outcome feeds back into the next decision.
value = decision-latency reduction × frequency × decision weightPriority is arithmetic, not opinion.
We find the break before we build anything.
One ring, five stages. For each role we ask which decisions are slow, which fail, and where the loop snaps. Then we mark the smallest valuable slice we can close end-to-end.
Map the loops. Close one. Instrument it.
We map decision loops by role — not activities, not flowcharts. Which decisions get made daily, which are slow, which fail, where each one breaks.
One loop, closed end-to-end, inside the systems you already run, with an explicit human–AI authority boundary. No rip-and-replace.
We measure latency, frequency and outcome so the loop truly closes — and the next loop is chosen by the numbers, not by opinion.
Agents synthesize. Humans decide. One cockpit.
Instead of chasing status across meetings and message threads, the Action Board shows the synthesized situation and a recommendation. The manager approves, adjusts or overrides — and executes from one place. Status questions get answered by continuous synthesis, not by asking around.
3 supplier quotes in for SKU-4471. Lowest total cost: Supplier B at €18,420, 12-day lead, NET-30.
Recommends — Approve PO with Supplier B
Order #2208 at risk — machining stage 6h behind, due Friday. Line 2 has open capacity tonight.
Recommends — Reallocate to Line 2
Batch 88 shows 2 dimensional deviations against SOP-114. Outside tolerance on the second check.
Recommends — Hold batch · request re-inspection
Quote for Andrade idle 4 days. Competitor lead time is shorter; margin still holds at a 5% discount.
Recommends — Send revised quote
Built and proven before it was ever sold.
Both systems run inside real operations. The method came out of the work, not a deck.
Maestro
Industrial manufacturingContext
Coordination lived in one person's head, in WhatsApp threads and spreadsheets — roughly 30 hours a week of management time spent chasing status, and 20–30% of orders hitting delays. The ERP already held the data; it just wasn't decision-ready.
What we built
Seven specialized agents (Projects, Production, Purchasing, Warehouse, Quality, Logistics, Commercial), an Action Board with bidirectional threads, intelligent checkpoints instead of rigid gate approvals, and 203 cataloged SOPs as the agents' operating knowledge.
Result
Coordination time down 50–60%, measured with five of seven modules in production; 70–80% projected at full deployment.
Cotador
Industrial procurementContext
Buyers run dozens of supplier quote threads by hand. Comparing price, lead time and payment terms across them is slow and error-prone.
What we built
An agent that collects quotes over a normal WhatsApp conversation, extracts price, lead time and payment terms, and converges them into a live comparison table. Suppliers change nothing — which is exactly why it works where procurement portals fail: portals demand supplier adoption; the agent adapts to the supplier.
Result
First supplier responses extracted into a live comparison table the same day. The working MVP was built and run over a single weekend.
Three ways to work together.
Audit Sprint
Your decision loops mapped, breaks located and classified, and a plan ranked by value = latency × frequency × weight. Fixed scope, fixed price.
- Decision loops mapped by role
- Breaks located and classified
- Plan ranked by value
- Fixed scope, fixed price
Implementation
Most popularThe highest-value loop closed inside your existing systems, with an explicit authority boundary, shipped in vertical slices.
- Highest-value loop closed end-to-end
- Inside the systems you already run
- Explicit human–AI authority boundary
- Shipped in vertical slices
- Instrumented for latency & outcome
Retainer
Ongoing operation — new loops, maintenance, evolution. For teams running systems in production.
- New loops, continuously
- Maintenance & evolution
- Priority operator support
- For systems in production
Most engagements start with the Audit Sprint. If you move to implementation, the sprint fee comes off the project.
Straight answers.
Find your most expensive open loop.
Two weeks. A ranked map of where your decisions are slow, where they fail, and which loop is worth closing first.