How it Works
Scope. Measure. Report. Implement. Re-measure.
A fixed-fee assessment measures how the work is actually performed — continuously, at the desktop, with OLi. You get a written report and an executive readout. Act on it and the same agent implements the changes and measures whether they worked.
Or skip the assessment and deploy directly: 10% more productive time in 30 days, guaranteed.
The engagement
Five steps, one instrument.
- 01
Scope
A 30-minute call to agree on the team, the question, the seats and the measurement period. Your security review starts in parallel with contracting — in regulated teams it is the longest step.
- 02
Measure
The OLi agent measures the work continuously for two to four weeks, depending on the assessment: passive, across the whole team, anonymized at capture, inside your tenant.
- 03
Report & readout
A written report and an executive readout — the numbers, what they mean, and the changes we recommend.
- 04
Implement with OLi
Phase 2, if you choose it: OLi delivers the automations, help at the point of work and schedule changes. The fee is credited if you sign within 60 days of the readout.
- 05
Re-measure
The instrument that produced the baseline measures the change. Measured, not estimated.
Week 0
Scoping call, security review, employee notice, and a small pilot install to verify capture.
Weeks 1–3 or 1–4
Continuous desktop measurement across the whole team — passive, anonymized at capture, inside your tenant boundary.
The week after
Written report and executive readout: AI use cases and automations ranked by measured hours, headcount, schedule, training, governance.
Phase 2
Implement with OLi — skills, automations, help and training at the point of work, schedule changes — then re-measure against the baseline.
Why weeks, and why the whole team? Interviews, workshops, and observation studies change how people work while they are in the room. Passive measurement for weeks lets the novelty wear off — weekly totals settle into a narrow band, and the report shows it. Assessments and pricing →
Under the hood
How OLi measures the work — and then helps.
Measuring how work is performed at scale takes three things: a desktop agent on every in-scope seat, machine learning over millions of desktop events, and the right algorithms to turn them into decisions. Once deployed, the same system recognizes the moments where it can help and shows up with the right content or action — no prompts, no dashboards.
Walk the mechanism →
Every step runs on the activity graph
The spine
Every step runs on the activity graph — Dataken’s first-party record of real human work.
10B+ records · 5+ years of continuous operation · 1M records per day.
Capture — it starts at the desktop.
A lightweight agent on the user's machine writes structured activity records — which app is active, how long the user stays in it, and what type of content is on screen. On-device capture means raw data never leaves the machine unprocessed. Roughly 1M records per day in a typical deployment.
Technical detail
Each record is a structured tuple: timestamp, application, window title, dwell duration, detected content type. No keylogging. No full-screen capture. The signal is what the user is doing, not what the user is typing.
Graph — records flow into your tenant's activity graph.
Activity records stream into the graph, where they're anonymized on ingest and bounded to your tenant. Your graph is yours — isolated, per-tenant, and built entirely from your own work. The graph is what makes OLi's suggestions specific rather than generic.
Technical detail
Apache Spark ingest pipeline. Per-tenant data boundaries enforced at the storage layer — not as a policy overlay, but as an architectural property. Anonymization runs inline on ingest. The rules registry that drives recognition is also per-tenant.
Recognize — OLi sees the work, not just the window.
Pattern recognition runs against the graph in real time. OLi detects friction, recognizes tasks, and identifies context — three tab-switches in 90 seconds on a compliance form, a department timesheet opening, a knowledge-base article that matches the active task. Recognition is the trigger; nothing happens until OLi sees something worth acting on.
Technical detail
Recognition rules are configured per tenant. A healthcare tenant's rules detect EHR workflows. An insurance tenant's rules detect claims processing. Same agent, different context, different actions — because the rules registry and the graph both live inside your tenant boundary.
Act — help lands at the point of work.
When OLi recognizes a moment worth acting on, it surfaces help as a desktop toast — a small notification at the point of work. Today that's usually content: a micro-learning refresh, a knowledge-base article, a break reminder. Increasingly it's skills: real actions OLi takes for the user, like building SOWs from a timesheet and routing them through Zoho Sign.
Technical detail
Skills are tenant-customized. They integrate with your stack — your EHR, your CRM, your document-signing workflow. This is what separates OLi from a generic AI assistant: the action is wired to how your organization actually works.
Learn — the loop closes back to the graph.
Every interaction feeds back into the activity graph. Recognition patterns improve with real usage, because the graph records whether each intervention actually changed behavior. Closed-loop improvement, not open-ended dashboards.
Technical detail
Privatized LLM inference by default. Any skill or Ask OLi call that invokes an LLM uses the provider's zero-retention, no-training-on-tenant-data mode. An open-source isolated-deployment option is available for security-sensitive tenants.
The full loop
One real moment, end to end.
Abstractions aside — here’s the Capture-to-Learn loop for a single sales moment.
Quote to SparkCo opened 4× with no reply.
Records the 4 opens of the quote PDF over 3 days.
Links the PDF opens to the original email thread and the contact.
Matches the 'warm lead, no reply after repeated opens' rule.
Drafts a nudge citing the slide the contact re-read most.
Records whether the nudge lands a reply — and refines the rule.
Privacy is architecture, not policy.
Activity-graph anonymization. Privatized LLM inference by default. Per-tenant data boundaries. On-device capture where possible. These aren’t promises — they’re how the system is built.
Read the full privacy story →Two ways to start.
A fixed-fee assessment, credited toward deployment if you sign within 60 days of the readout — or deploy directly, with 10% more productive time in 30 days, guaranteed. Choose one.
How the mechanism works
Frequently asked
How does OLi know what someone is working on?
OLi captures application and task context on the desktop — what kind of work is happening, in what sequence, for how long — and structures it into the activity graph. It reads work patterns rather than reproducing document contents, which is what lets it recognize a moment without becoming a surveillance record.
What is the activity graph?
A structured, anonymized, time-ordered record of desktop work: applications, task context, transitions, and durations, accumulated continuously since 2020. It is the record OLi draws on to decide when and how to help. It cannot be bought or synthesized — only accumulated — which is why it is difficult for a new entrant to replicate quickly.
How is this different from process mining on our ERP logs?
System-log process mining sees what your systems recorded. It cannot see the twenty minutes someone spent in a spreadsheet, a PDF, and three browser tabs between two system writes. Task mining captures that desktop layer between system events, which is typically where the majority of real cycle time sits. Dataken does both from one graph.
How long until we see results?
Initial findings typically appear within two weeks, because repeated task patterns become statistically meaningful quickly. On a direct deployment, the 30-day guarantee is measured against a baseline agreed at kickoff, so the measurement method is settled in advance rather than argued afterward.