Product
The agent that helps, measures, and protects.
OLiis a desktop AI agent that acts on the work itself — no prompting, no waiting. It shows up in the moment with the right help, records what actually happened through the activity graph, and keeps every tenant’s data inside its own boundary. Three capabilities, one agent, one substrate.
10% more productive time in 30 days, guaranteed.
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Pillar 1 · Help
Help in the moment.
OLi recognizes the work and surfaces the right thing the moment it matters. Today, mostly content — micro-learning when a skill gap is detected, a knowledge- base article when the task calls for one, a break reminder when continuous focus patterns warrant it. Shipping across five-plus years of production deployments.
Increasingly, skills — real actions OLi takes for the user. Generate SOWs from a timesheet and send via Zoho Sign. Record insurance eligibility into an EHR. Summarize an email thread grounded in your work context. Same agent, same trigger substrate, more of what it can do.
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Content — shipping today
Micro-learning, motivation, knowledge-base articles, break reminders, coaching nudges. Multi-tenant, in production.
Skills — rolling out now
Tenant-customized actions wired to your stack. Built by Dataken engineering today. Self-serve Skills API coming soon.
Pillar 2 · Proof
Proof of where work really happens.
Analytics, process mining, and the OLi Analyzer — the only view of work built from the activity graph. Not surveys. Not self-reports. Not time-tracking that depends on people remembering to click a button. The record of what actually happened.
What the graph can prove
- Where time actually goes, across apps and tasks.
- Which processes break, and at which step.
- Whether an OLi intervention actually changed behavior.
Where does the time actually go?
Illustrative. The activity graph records what actually happened. Self-reports, surveys, and time-tracking tools that depend on people remembering to click a button don’t.
Pillar 3 · Privacy
A privacy story your security team will sign.
This isn’t a disclaimer. It’s architecture. Four layers, enforced at the data and system level — not promised in a marketing page or bolted on as a policy overlay.
Every tenant gets their own graph, their own rules registry, their own inference boundary. Cross-tenant data sharing isn’t a feature you have to opt out of — it isn’t possible.
On-device capture
Raw activity is processed on the user's machine. Only structured records leave the device.
Anonymization at the data layer
Anonymized on ingest as an architectural property, not a policy overlay.
Privatized LLM inference
Zero retention. No training on tenant data. Open-source isolated deployment available for security-sensitive tenants.
Per-tenant boundary
Your rules registry, skills, and activity graph are isolated to your tenant. Cross-tenant data sharing is not a feature.
End-to-end
One agent. One graph. One coherent story.
Each pillar above connects to a deeper page on the site. The moat that makes all three possible, the mechanism that runs underneath, and the outcomes they drive.
The moat
The missing-axis argument
Why activity context is a separate axis from RAG, and why Dataken is the only one operating it in production.
Read moreThe mechanism
The five-step loop
Capture → Graph → Recognize → Act → Learn. The activity graph runs through every step.
Read moreThe impact
The outcomes it drives
Productive time recovered, engagement raised, behavior changed — measured, not estimated.
Read moreOne agent. One graph. Three capabilities no one else has together.