Workflow audits that show how work actually happens — while simultaneously pre-training the AI agents that will automate it. When the audit is done, your agents are ready to deploy. Faster than any consulting company.
$2.4M savings
Board-ready value model
3.2 mo payback
Fastest: AP processing
Works across
The tools your team already lives in every day.
A scoped workflow audit that reveals how work actually happens — while simultaneously pre-training the AI agents that will automate it.
The audit is just the beginning. Pre-trained agents move into Roadmap, Deployment, and Monitor — inheriting everything they learned during the audit period.
Every competitor treats the audit and the deployment as separate phases. We treat the audit as the training period — so when the roadmap is approved, the agents already know exactly what to do.
While we observe your workflows, the same operational data is simultaneously training the AI agents that will automate them. Every handoff pattern, every process step, every exception your teams encounter — the agents are learning it in real time.
Traditional consultants hand you a report and walk away. With Mitori, the audit deliverables include pre-trained agents that already understand your processes, your tools, and your edge cases. Deployment is configuration, not construction.
Because agents were trained on 30–90 days of your actual operational data, monitoring detects every deviation and exception against a precise observed baseline — not assumptions, not industry averages, not guesswork.
Traditional path: Audit → Report → Hire integrator → Rebuild from scratch → Deploy → Hope it works
Mitori path: Audit & Train → Deploy pre-trained agents → Monitor against real baseline
Speed to deployment
From audit to live agents in weeks, not months. Why wait when the data is already training your agents?
Far better accuracy
Agents trained on your real operational data outperform any human consulting estimate. Evidence over assumptions.
Reduce your AI spend
Enterprise clients save on tech subscriptions through Mitori’s bundled infrastructure. One platform, not ten tools.
Mitori turns workflow reality into an AI rollout plan that operations, finance, and technology leaders can defend.
Observe & Train
Capture real workflow patterns across your entire operation — while simultaneously pre-training the AI agents that will automate them.
Desktop agent observes and trains
Capture application usage, timing, handoffs, and workflow patterns — the same data simultaneously pre-trains your deployment agents.
You control the access scope
We only observe what access you provide us with. Toggle capture surfaces on or off to match your organisation’s policy.
Your existing stack stays intact
The audit works across the tools already in use, so there is no migration project before insight begins.
Observed
24 / 24
Systems
12
Handoffs
147
Diagnose
Map bottlenecks, handoffs, manual effort, and where AI can realistically automate or augment work.
Workflow bottlenecks surface fast
The audit shows where approvals stall, where context switches pile up, and where duplicated effort lives.
AI feasibility gets grounded
Each workflow is classified by what can automate now, what needs augmentation, and what needs redesign first.
Dependencies stay visible
Security, compliance, integration, and change-management constraints appear before anyone starts selling fantasy.
Friction
18
Patterns
42
Risk
11
Prioritize
Model value, effort, dependencies, and risk so the roadmap is grounded in payback, not enthusiasm.
ROI comes with confidence bands
Every automation path is scored by expected savings, effort to deploy, and confidence in the underlying pattern.
Sequencing is built in
Quick wins, hard dependencies, and longer-cycle bets are staged into a roadmap leaders can follow.
The board case is already written
Assumptions, sensitivity ranges, and expected payback are framed for finance and operations decision-making.
ROI
94%
Sequence
6 waves
Board
High
Deploy
Deploy pre-trained agents that already understand your workflows. Configuration, not construction.
Agents already know the work
Pre-trained during the audit on 30–90 days of real operational data, agents deploy with operational knowledge built in — not generic templates.
Controls come with the rollout
Escalation paths, approval boundaries, and auditability evidence are part of the delivery package.
IT gets a scoped path
The deployment team receives a practical connector and integration brief instead of a new discovery exercise.
Waves
3
Controls
9
Owners
12
Optimize
Track what changed, compare forecast to realized impact, and refine the next wave of automation work.
Mitori manages your AI operation
Ongoing copy changes, flow adjustments, and rule updates are handled by Mitori — your team never manages the automation directly.
Platform costs stay bundled
All infrastructure — data pipelines, messaging, voice AI, monitoring — is managed and included. No surprise vendor bills.
Returns keep compounding
As each wave stabilizes, Mitori identifies the next set of automation candidates. The deeper we integrate, the more value compounds.
Review
Monthly
Change
Live
Next
11
Step 01
Capture real workflow patterns across your entire operation — while simultaneously pre-training the AI agents that will automate them.
Live audit signal
Observed work becomes both a clear operating baseline and agent training data: which systems are active, where handoffs repeat, and how agents should handle each pattern. You control what the agents see.
The output should feel like delivery is already underway: evidence, prioritization, controls, and a handoff that reduces argument instead of creating more of it.
01
By team, role, and handoff so time, cost, and automation surface area are visible immediately.
02
Automate, augment, and redesign paths scored by value, effort, and confidence.
03
With assumptions, confidence bands, and sensitivity ranges finance leaders can defend.
04
Sequenced around dependencies, integration readiness, and where fast payback really lives.
05
Controls, escalation paths, and auditability evidence for executive and board sign-off.
06
Approved opportunities packaged for implementation workstreams, controls, and Mitori Monitor.
Proof before promise
Faster task completion
GitHub + MIT, controlled experiment
Saved per knowledge worker
Microsoft Copilot, 6,000-person study
Annual enterprise AI value potential
McKinsey Global Institute
Collections recovery improvement
Mitori client, healthcare staffing
“The question isn’t whether to act on AI. It’s where to start.”
Companies want full visibility into how work happens. Mitori gives you the controls to define exactly what we access — and the technology to turn that access into deployment-ready agents.
You control what the agents see.
Toggle capture surfaces on or off to match your organisation’s policy. We only observe what access you provide us with.
Enterprise-grade access controls.
Configure scope per department, role, or team. Adjust access surfaces at any time during the audit window.
Full-spectrum observation when you need it.
Keystrokes, screen recording, email content, documents — everything on company-managed devices. The more access, the better the agents.
Access scope configuration · You control what we observe
Implementation readiness
Mitori observes work wherever it happens, then packages those findings into implementation-ready workstreams without forcing a new system migration first.
Mitori connects Audit, Roadmap, Deployment, and Monitor so observed evidence becomes an approved deployment plan, a governed rollout, and an operating loop that keeps improving.
Observe, analyse, and pre-train
A full-spectrum audit that captures how work actually happens — while simultaneously pre-training the AI agents that will execute it. By the time the audit is complete, your agents already understand your workflows, your tools, and your edge cases.
Decide what to automate first
Audit evidence feeds an opportunity matrix that ranks automation candidates by ROI, effort, dependencies, and governance risk. The roadmap tells you what to automate first, and why.
Deploy agents that already know the work
Agents inherit operational knowledge from the audit — they’ve already been trained on 30–90 days of real workflow data. Deployment is configuration, not construction. Governed rollout controls ensure nothing launches without approval.
Track deviations against real data
Because agents were trained on your real operational data during the audit, monitoring detects every deviation and exception against a precise observed baseline — not assumptions or generic benchmarks. The loop feeds back into the next audit cycle.
AI transformation is too important to base on assumptions. Mitori exists because we believe every deployment decision should be grounded in observed evidence — not consultant opinion, not vendor promises, and not internal guesswork.
Every audit follows a structured, repeatable process — full-spectrum observation, four-layer analysis, and board-ready deliverables. No workshops, no guesswork.
Purpose-built by a founding team with over a decade of shared engineering history. The observation agent, analysis pipeline, and governance layer are ours — not stitched together from third-party tools.
Our leadership has spent 15 years inside technology organisations — building products, managing teams, and navigating the complexity that enterprise AI programmes demand.
We identify the workflow family, team scope, and governance constraints. You leave with a clear audit proposal.
Full-spectrum observation captures real operational evidence. You receive workflow maps, friction analysis, and agent-ready JSONL packs.
Ranked opportunities with ROI models, sequencing, and a board-ready investment case. This is where insight becomes an approved decision about what to automate first and why.
Approved workflows move into implementation sprints with structured handoff packets and governed rollout. Mitori Monitor tracks outcomes, tunes exceptions, and feeds the next optimization wave.
Unsure where you stand? Take our free readiness assessment to score your organization before your first call.
Starter Audit — 30-day audit, 1 workflow cluster
SMB Audit — 60-day audit, full ROI model
Enterprise Audit — 90-day, board-ready roadmap + full lifecycle
Typical Big 4 AI readiness assessments cost $50,000–$500,000 and take 3–6 months. Mitori delivers faster, with more data.
Book an audit scoping call to discuss your team size, workflow scope, and timeline. Every engagement starts with the evidence.
Not ready to talk yet? Take our free readiness assessment