Transform plant data, workflows and decisions into a proprietary learning layer that compounds over time, while remaining independent of any single model.
A vibration sensor can tell you a bearing is degrading. It can't tell a technician how to fix it, see a defect on the line, read a process drift, or raise the work order. That gap, not a lack of AI, is where most industrial intelligence programs stall.
SIX SOURCES. ZERO SHARED MEMORY.
SOPs, manuals, deviation records, work orders, process and energy tags, brought into the same picture as the sensor data you already run.
An ISA-95 hierarchy, enterprise to asset, described once. Every document and work order hangs off it, inherited by every use case after.
Retrieval over your own plant knowledge, vision on your own defects, and cross-signal detection linking vibration with process and energy drift.
Work orders, alert triage and setpoint recommendations, gated by human approval on every write. Advisory first, autonomous only when you choose.
Your maintenance subscription doesn't change. This is bought with different money (the AI budget), and nothing here is contingent on the maintenance line.
Most industrial AI stalls on data. You already have twelve months of signal history on sixty assets, that's why you start ahead of a greenfield site.
One site, one measured result, no multi-year lock. You decide again every year.
This is the difference between hiring a consultancy and owning a learning layer. Three things sit inside your tenant boundary, not ours, and none of them leave if you switch models or switch vendors.
The asset, area, line and site structure built in Release 1 lives in your tenant. It's yours to keep, extend, and reuse, with us or without us.
Every defect image used to train Vision QC, and every label attached to it, stays in your environment. It's a training asset you keep building.
The evaluations that measure whether an answer, a triage, or a recommendation was actually right are yours, and we work with any model, so you're never stuck if one gets pulled or repriced.
Every prompt teaches someone.
The question is: who keeps the lesson?
Five things every enterprise moving into the AI era has to hold onto, deliberately, not by accident.
Build your own private evals, they define what "good" means inside your organisation. Retain ownership of memory, traces, feedback and institutional context.
Build proprietary learning environments inside your own tenant boundary, where models learn against real workflows without exposing your knowledge to anyone else.
Keep the orchestration layer decoupled from any single model. If one is taken away tomorrow, your ability to operate against your own evals stays intact.
A decoupled orchestration layer lets you combine context, models and tasks in whatever mix is most efficient, without ever trading away quality to get there.
Bring the four together and you get a continuous learning loop, a hill-climbing machine that compounds the value of every AI investment you make.
GPT, Claude, Gemini, Llama: any of them can sit behind your orchestration layer. Swap one out and your evaluations, your memory and your workflows don't move. The generalist changes; the veteran capability you've built stays with you.
WogiAI is the learning layer applied to a real plant floor, sitting on top of the vibration monitoring you already run, closing the gap between a sensor that detects a problem and a technician who knows what to do about it.
FIGURES REFLECT A SINGAPORE REFERENCE SITE: ARITHMETIC, NOT A PROMISE. THE LANDING ENGAGEMENT REPLACES EVERY INPUT WITH YOURS.
The physical assets, lines and sensors your operators already run every shift.
Every system of record you already have, connected as-is, nothing ripped out.
Enterprise, site, area, line and asset, described once so every layer above inherits it.
The tenant-boundary layer where models learn against your real workflows, not a vendor's.
Every prompt, decision and outcome kept as institutional memory, not lost between sessions.
Tests you write, scoring what "correct" means on your floor, not a generic benchmark.
Routes work to whichever model fits, so no single vendor becomes a dependency.
Interchangeable behind the orchestration layer, swapped without touching what you've built.
Start as recommendations a human reviews, graduate to autonomous only when you allow it.
Nothing touches a system of record without a person signing off first.
↻ APPROVED ACTIONS RETURN TO THE PLANT, THE LOOP CLOSES, THE LEARNING COMPOUNDS
We'd rather show the arithmetic than assert a number. Eight inputs about your site drive every line below: change one, and the whole model moves.
This sits on the AI budget, not the maintenance budget. Your existing subscription is untouched, and nothing here depends on renegotiating it.
Tooling only pays off if technicians actually use it. Release 1 includes floor-level training, a shift champion on each crew, and usage tracked alongside the ROI metrics above, so low adoption shows up as a number, not a surprise at renewal.
We're asking for CMMS write access and plant documents, so these get answered before your security review stalls the deal, not during it.
BRACKETED FIELDS NEED CONFIRMED ANSWERS FROM ENGINEERING BEFORE THIS SECTION GOES LIVE.
Every workflow becomes experience.
Every experience becomes intelligence.
That's the asset. Not the model.
Bring your eight numbers. We'll rebuild the model on your site, live, in one session. One site, one named AI sponsor, two hours.