INNOVAAI Transformation
AI transformation means redesigning how work happens.
Companies often add AI tools one at a time, with no map of the processes underneath. We start from that map, so every automation has an owner, a reason and a number to beat.
In brief
INNOVA is an Ottawa company that plans and builds AI automation for organizations in Canada. Work starts with a map of real processes and a measured baseline. Each process then gets the simplest approach that fits, from plain software to a supervised AI agent, with approvals and audit trails built in, and results are reported against that baseline.
Seven steps, one baseline.
The baseline measured in step one is the number we report against in step seven.
- 1
Process discovery
We map departments, roles, systems, documents and the processes that connect them, with volumes and pain points.
- 2
Opportunity mapping
Each process is scored for value, feasibility and strategic fit, minus a risk penalty. Every score shows the reasons behind it.
- 3
Prioritization
We rank opportunities by value and feasibility and agree on the first two or three to act on.
- 4
Solution architecture
For each chosen process we pick the simplest approach that works, from deterministic software to supervised agents.
- 5
Implementation
We build, integrate and test inside the tools your team already uses.
- 6
Governance
Approvals, permissions, audit trails and data controls are designed in from the first sprint.
- 7
Measurement
We compare cycle time, cost, error rate and adoption against the baseline taken during discovery.
Stage names follow the INNOVA stage line. Modules are those of INNOVA AI Workflow OS: 1 Process discovery, 2 Automation opportunity map, 3 Automation designer, 4 Implementation workspace, 5 Impact & ROI.
The right tool for each process.
We use AI where it earns its place. When ordinary software does the job better, we build ordinary software, and say so.
Deterministic software
Rules are clear and stable. Plain code is cheaper, faster and fully predictable.
Conventional automation
Data moves between known systems on known triggers.
AI assistant
People stay in charge; AI drafts, summarizes and looks things up.
Multi-agent workflow
Several specialized agents hand work to each other under one supervisor.
Human-in-the-loop system
High-stakes steps always pass through a named person before they take effect.
Scale: from fully predictable to supervised autonomy
How we build AI agents
Each INNOVA agent gets a bounded task, named tools and permissions, and a person who approves its output.
In AICRMIUS, agents follow one cycle: propose, validate, approve, apply, undo, with every step in an audit trail. In FINMOZG, deterministic accounting engines keep the ledger correct, and every posting lands in a hash-chained audit log.
- Propose
- Validate
- Approve
- Apply
- Undo
How a Workflow OS project runs
Each step runs on INNOVA AI Workflow OS, from the first process map to the measured result. Four steps; each one ends in a result you can review before the next one is agreed.
-
Step 1
Opportunity assessment
Fixed scope. You get a process inventory, a scored opportunity register, technology options for each process, a privacy and security constraint map and a sequenced roadmap.
-
Step 2
Proof of concept on your historical data
About three weeks. It answers the feasibility question before any build budget is committed.
-
Step 3
Single-process pilot
About 12 to 15 weeks from discovery to a supported production process. Measured results arrive 4 to 6 weeks after go-live.
-
Step 4
Support
Monthly monitoring, fixes, model-version regression tests and small improvements.
Timelines are for one bounded process. Integration, the number of exceptions, privacy review depth and data readiness move them most.
What we need from you
A process owner and two or three of the people who do the work, about 2 to 4 hours each during discovery; one 3-hour workshop; 4 to 8 hours of acceptance testing; real sample transactions, including the awkward ones; and a named person inside your company who owns the automation.
What you keep
Licences stay in your name and your tenant. You own the output.
Security and your data
How data, access and models are handled by default.
- Where your data lives
- By default we build inside your own cloud tenant, for example Microsoft 365 and Azure in Canada Central or Canada East, under your licences. Data, backups and logs stay in your environment, and INNOVA keeps no copy. If a service you need runs only outside Canada, we say so at design stage. Data leaves Canada only with your written approval.
- Access
- We work under accounts you issue, time-limited and least-privilege, and you revoke them when the engagement ends.
- Models
- Your data is never used to train or fine-tune a model. Model versions are pinned, and a version change is retested before it goes live.
- Documents are data
- Instructions hidden in a document are treated as text. Agent tools are allow-listed step by step.
- Human approval
- Any action that changes a record or affects a person passes a named approver, and every step is logged.
- Privacy review
- Where personal information is involved, a privacy impact assessment is planned into the pilot from the start.
What we measure.
Outcomes are agreed before the build starts and reported against the discovery baseline.
See AI Workflow OS- Shorter cycle time
- Fewer repetitive tasks
- Lower operating cost
- Faster response to clients
- Visibility into every process
- Better-informed decisions
- Fewer manual errors
Send us the process that takes the most hours.
We reply in writing with whether and how we would start.
Send a process