INNOVA Applied innovation
& technology
, home page
Start a conversation

INNOVAWorkflow OS

INNOVA AI Workflow OS

From business processes to measurable AI automation.

Workflow OS overview for the demo company Northstar Professional Services: 30 people, 5 departments, 9 systems, 15 mapped workflows and the top-ranked automation opportunities.
Prototype · company overview of Northstar Professional Services, a fictional 30-person firm, with its automation opportunities ranked by score (illustrative demo data).

Working prototype. Invite-only demo online since 1 October 2026.

Built for firms of 20 to 200 people.

Request a demo invite

You receive a personal link to the working prototype, preloaded with a fictional sample company. The walkthrough takes about two minutes.

Record
INV-04 · Prototype
Technology
React + TypeScript · Deterministic scoring engine (14 factors + risk penalty) · AI process discovery with rules-engine fallback · Architecture recommender (6 patterns, 5 human-control levels) · ROI model · 226 automated tests

Six questions every automation plan has to answer.

Workflow OS answers each one from the company’s own process data, and each answer links to the module that produces it.

What exactly should be automated?

Module 2 scores every process on 14 factors in three groups (value, feasibility, strategic fit), subtracts a risk penalty, and shows the reason behind each number.

Module 2

Which process comes first?

Processes are ranked by priority score, and the plan starts with the top two or three.

Module 2

Where is AI actually useful?

Where drafting, classifying, summarizing or a bounded task with tools saves real time. Stable, rule-based work goes to deterministic software.

Module 3

Does this need an agent, or ordinary automation?

Module 3 recommends one of six patterns, from traditional automation to supervised agents, with one of five levels of human control.

Module 3

What will implementation cost?

Module 4 turns the design into tasks, owners, integrations and milestones, which is the basis of the estimate.

Module 4

Did the automation produce measurable value?

Module 5 projects the return from stated assumptions, then records actual results against the baseline after go-live.

Module 5

Five modules.

Each module hands its output to the next. Risk controls run through all five.

Module 1 of 5

Process discovery

Capture departments, roles, systems and processes by hand, or paste a written company description and review the draft process model the AI proposes.

  • Company
  • Departments
  • Roles
  • Systems
  • Processes
  • Process steps
  • Pain points
  • Volume and minutes per case

Module 2 of 5

Automation opportunity map

Workflow OS Opportunity Map: 15 processes plotted by business value and implementation feasibility, with Client Onboarding ranked first at 88 and its score broken into value, feasibility, strategic fit and risk.
Opportunity Map for the demo company Northstar Professional Services (illustrative demo data). Every score shows its parts.

Score every process on 14 factors in three groups (value, feasibility, strategic fit), subtract a risk penalty, and show the reason and evidence source behind each number.

Priority = Value × 0.50 + Feasibility × 0.30 + Fit × 0.20 − Risk penalty

Value

  • Time consumed
  • Volume
  • Rework
  • Waiting time
  • Client and revenue impact

Feasibility

  • Standardization
  • Data readiness
  • System readiness
  • Rule clarity
  • Exception predictability

Fit

  • Repeatability
  • Scalability
  • Employee pain
  • Management priority

Risk penalty

  • Privacy
  • Regulatory
  • Consequence
  • Change complexity

Module 3 of 5

Automation designer

Workflow OS Automation Designer for Client Onboarding: effort per case from 100 to 40 minutes, current and proposed process flows with human, system and AI lanes.
Automation Designer: current state against proposed state, with the human approval gate kept. Illustrative demo data.

Recommend one of six patterns for the selected process, draw its current and proposed flow, and set a level of human control for every component.

Six patterns

  • Traditional workflow automation
  • AI assistant with human execution
  • Bounded AI agent
  • Multi-agent system
  • Human-in-the-loop AI support
  • Hybrid AI-assisted automation

Five levels of human control

  • Fully automated
  • Review after execution
  • Approval before external action
  • Approval before critical action
  • Human only

Module 4 of 5

Implementation workspace

  1. Validate
  2. Prototype
  3. Pilot
  4. Production
  5. Optimize

Turn the design into a delivery plan in five phases, with an owner, dependencies, effort, a target date and acceptance criteria for every task.

  • Tasks
  • Owners
  • Dependencies
  • Systems touched
  • Milestones
  • Effort in days
  • Target dates
  • Acceptance criteria

Module 5 of 5

Impact & ROI

Workflow OS business case for Client Onboarding: projected staff hours, payback and Year-1 ROI with every figure labelled calculated, assumption, estimated or projected.
Business case with each number tagged by origin (illustrative demo data).

Project the return from stated assumptions, then record actual results against the baseline after go-live.

  • Monthly case volume
  • Baseline minutes per case
  • Projected minutes per case
  • Loaded hourly cost
  • Implementation and operating cost
  • Payback and Year-1 ROI
  • Actual minutes after go-live
  • Actual volume after go-live

Risk and governance · every module

  • Privacy
  • Security
  • Regulatory
  • Financial
  • Legal
  • Reputational
  • Operational
  • Model hallucination
  • Unauthorized tool use

Where Workflow OS ends.

Workflow OS decides what to automate and how. The automations themselves run in the client’s existing platforms or in INNOVA products such as AICRMIUS.

That boundary keeps the planning tool independent of any single vendor, including us.

The demo runs on fictional data. Scoring, design and ROI are calculated without AI. Optional AI discovery sends the description you paste to an external AI model hosted outside Canada; when it is off or slow, a rules engine drafts the process model instead.

Our AI transformation method