We don’t start with AI.
We start by watching how your business actually runs. The AI and automation come in only where it pays.
We build automation systems around real operating workflows.
The work nobody documented
Most operations accumulate a hidden layer of workarounds as tools get added, teams adapt, and the connections between systems never quite get built.
You can’t automate a workflow you haven’t understood. That’s why we map the work before we recommend the technology.
Three steps, in order. Because automation only works when it starts with the work.
Map
We sit with the people whose work touches the process and build a workflow register — each task, how often, how long, and what breaks. Not a survey. Observation.
Triage
Every workflow gets scored on volume, cost, and automation feasibility given the systems you’re locked into. You get a ranked shortlist with dollars attached — and an honest list of what to leave alone.
Build & measure
We build one or two workflows first and measure them against the baseline before anything else rolls out. If the numbers don’t hold, we say so.
You own the output at every stage. If you stop after the map, you still leave with an actionable operating picture — even if we build nothing further.
Built around the process, not the company.
Operations Diagnostic
A structured read on where your team’s time and money actually go, and what is worth automating. Yours to keep, whether or not you work with us again.
- Workflow registers for every role in scope
- Automation triage scored on cost and feasibility
- A ranked shortlist — and an honest list of what to leave alone
- ROI ranges with the assumptions shown, not hidden
Pilot Build
One or two workflows from the diagnostic, built and deployed so you can judge the result before committing to more.
- Does the workflow work in the systems you already use?
- Does it improve the baseline we measured?
- Do the economics still hold after real-world use?
- Should we scale, adjust, or stop?
Managed Intelligence
Ongoing operation of what we build — monitoring, upkeep, and adjustment as your systems and workflows change.
- Monitoring deployed workflows
- Upkeep as source systems change
- Adjustments when performance drifts
- Regular checks that the value still holds
Diagnostics are fixed-fee and scoped to the process, not the company. Ask and we’ll size it in one conversation.
We already pay for software that’s supposed to do this.
Usually true, and usually part of the problem. The diagnostic often finds capability you’re already licensed for and nobody turned on. We’ll tell you that before we quote you anything new.
Is our data safe?
We design around your constraints, including agreements that limit where your data can go. Where a workflow can run on infrastructure you own, we’ll build it that way.
How long before we see anything?
The diagnostic itself is a deliverable, not a prelude to one. The first pilot comes after that and is measured against the baseline — you’ll know whether it worked, not just whether it’s live.
What if the answer is that we don’t need AI?
Then we say so and you keep the map. That’s happened, and it’s a better outcome than an automation nobody uses.
One hour changed a product build.
While building an insurance operations platform with our first agency customer, we found that customer-facing staff could not identify who had last worked an account roughly 70% of the time — a gap nobody in the business had named, in a market where a single point of retention is worth six figures.
That finding came from sitting with one frontline user for an hour. It reordered the product build and is now helping shape VelociBind →
| Task | Freq. | Owner |
|---|---|---|
| Renewal follow-up | Daily | Rotates |
| Account handoff note | Per touch | Undocumented |
| Last touched by: unknown — ~70% of accounts | ||
“I genuinely can’t tell you who spoke to this customer last.” — frontline staff, discovery interview
VelociBind
VelociBind is an insurance operations platform being designed from the outset to serve several independent agencies. Live operating workflows shape the product rather than assumptions about how agencies should work.
Other product directions
We are also currently in discussions around building products for the energy utility space and multi-user franchise operations. The common thread is the same: recurring operational problems that can become durable, reusable systems.
QuantizedIQ builds automation systems and software products around the way work actually happens.
It was founded by Morris Kabuage, most recently a Senior Director in Microsoft’s Office of Applied Research — the group that moves AI from research into products people actually use. The work spanned Office, Windows, Teams, and SharePoint, and mostly came down to the unglamorous part: understanding how people really used a product before deciding what the model should do.
Across more than twenty-five years in technology, Morris has seen that gap from multiple sides: building SQL Server as a Microsoft program manager; watching how the business bought and used technology as a field business analyst; producing training data for large AI systems at Mighty AI, later acquired by Uber; and helping move applied AI into Office, Windows, Teams, and SharePoint.
Those vantage points shape how QuantizedIQ builds today: observe the work, identify the friction worth fixing, build against a measurable baseline, and turn repeatable problems into durable systems and products.
Watch the work first. Build second.
Based in Newcastle, Washington, working across the Puget Sound region and remotely.
Start with the map.
Bring one process that is costing your team time, money, or patience. We’ll talk through how it works today and whether an Operations Diagnostic is worth doing.