Onni Fabric · People
Give everyone an AIthat knows their work.
Fabric turns roles, activities, knowledge, rules and processes into an intelligent workspace for every employee. It knows what each function can and cannot do, uses the right knowledge, measures the gain and learns from real use.
Fabric Understand work. Augment people. Measure productivity. Learn continuously.
Diagnose your teams' work
Onni Fabric in 45 seconds
An AI that knowsthe work.
From the role to the executed activity, measured against the human baseline: real Fabric screens, no mock-ups.
Which AI would you trustin everyone's hands?
- Does it know that person's work?
- Does it know the company's policies?
- Does it know what each role may and may not do?
- Does it use the right knowledge?
- Does it know which activities can be automated?
- Does it measure productivity?
- Does it learn from use?
- Does it respect security and governance?
A day with Fabric · 01 / 04
A day with Fabric
An agent that already knows your work.
Your team asks the way they talk.
It builds the process: the agent executes, a human validates.
Every role with its own skills.
Is it just ChatGPT with prompts? No.
There is work engineeringbehind it.
- APQC PCF v8The process classification framework: how work is organised across the enterprise.
- CBOThe Brazilian classification of occupations: who does the work.
- O*NETTasks, skills and knowledge for each occupation.
- Anthropic Economic IndexHow exposed each task is to AI.
- Legislation and standardsPMI, SHRM, ISO 9001, Hackett, Gartner, SIORG, ENAP.
- Your companyPolicies, documents, rules and data, indexed and kept current.
- Role
- Activities
- Skills
- Rules
- Knowledge
- Execution
Before claiming productivity,measure it.
Every activity carries a manual reference effort. Each real execution is compared with it.
- Timereference vs. real
- Tokensper execution
- Costper activity
- Human interactionwhere people stepped in
- Gainhours returned
Fabric in production
Proof
Mapped for every role
- Activities / skills
- 27,320
- Business rules
- 148,027
- Indexed documents
- 2,392,234
- Learning deltas
- 10M
It doesn't start empty.And it doesn't stop learning.