Applied AI

Twenty-four years of what works.
Three years pointing it at AI.

We learned how change actually succeeds inside organizations long before this technology arrived. These are the products that came from applying those lessons to AI, in our own business first, then in our clients’.

Book a Scoping Call What we learned ↓

Since 2002
Making enterprise software work: Three years
Applying AI to achieve business impact: Quantified Impacts
Real Return on Investment: Five examples
Real World AI Work Driving Customer outcomes

The through-line

The principles did not change. The speed did.

We have been doing digital and business process change since 2002, across thousands of organizations. The same lesson kept arriving: when it failed, it almost never failed for technical reasons. It failed because nobody agreed what the process was, or because the work landed on people who had no say in designing it, or because the result lived somewhere the team never went.

AI did not repeal any of that. It compressed the timeline, which raised the cost of getting the organizational part wrong. A model can now produce a confident answer faster than your business can decide whether the answer is one it wants.

So we did not start over. We took the discipline that already worked and pointed it at a new technology, and we tested it on ourselves before we charged anyone for it.

What twenty-four years teaches you

Which parts of a process are genuinely hard, and which only look hard because nobody has written them down. That judgment does not come from the technology. It comes from having been wrong about it before.

Why we built rather than only advised

Shipping something forces decisions that advice can defer. You cannot hand-wave the edge case when a customer hits it on a Tuesday. That pressure is what turns an opinion into a principle.

What running it on ourselves proved

Every product below is operated by someone other than the person who wrote it, inside a business that has to make money. That is the same test your organization applies, so it is the one worth passing first.

First, on ourselves

We rebuilt our own delivery before we recommended it to anyone.

Our roadmapping, our client reporting, and our commercial quoting all run on software we wrote. If a thing could not survive our own operations, we had no business putting it in front of a client.

StrategyAI

80% Reduction in Cost for Clients

Proves: Building capability drives scalability

Capability roadmapping. It benchmarks where each capability in a business honestly sits today, sets the right target for each one given how much of the business rests on it, and sequences the work by value. Industry-specific capability assessments that drive your roadmap.

We say the goal is durable capability rather than activity. The maturity curve we run engagements on is not a slide template we fill in — it is software, and it produces the same assessment whoever runs it.

See the method it runs →

CustomerCenter

Real-time Work Visibility, agent-enabled

Proves: Deliverables, not recommendations

The workspace every managed services client gets. Real work items, what is waiting on you, what is ready for review, what moved this week. Built because the honest answer to "what did we pay for last month" should take five seconds, not a meeting.

We say you exit with working capability rather than slides about it. The same standard applies to how we report: you inspect the actual backlog rather than a summary somebody wrote about the backlog.

See how managed services works →

ROSQuoter

Full CPQ capability, without the Salesforce bill

Proves: The failure is organizational, not technical

Our own quoting engine, used to build every ResellerOS quote we send. We run our commercial quoting on software we wrote.

The model was the easy part. What took the time was agreeing what a quote actually contains, who approves it, and what happens when the answer is wrong. That is the same work we do with clients, and it is why we do not lead with the technology.

Then, in other people’s constraints

The interesting problems are the ones a generic model has nothing to say about.

Reseller margin that depends on somebody else’s pricelist. Steel priced by grade, dimension, processing, and freight. These are not demo problems, and the answer in both cases was mostly deterministic software with a model doing the narrow part it is good at.

ResellerOS

Intelligent RevOps Automation for IT Resellers

Proves: Applied AI drives measurable results

An AI platform for IT resellers, serving federal and commercial. QuoteAI turns any vendor or distributor quote into a priced proposal in minutes. DealRegAI automates deal registration, tracks approvals, and protects discounts already earned.

A one-time uplift ends when the engagement does. This is a product with its own customers and its own roadmap, in a market we have advised on for two decades. It gets better every quarter whether or not anyone is paying us for a project.

reselleros.com → How we work with VARs and SIs →

SteelSalesAI

AI + Legacy Software Drives Impact Without Disruption

Proves: Focused AI Activation Produces Near-term Results

Quoting built for steel, where the price depends on grade, dimension, processing, and freight rather than a line item off a pricelist. Running at Cyclone Steel.

Most of this system is deliberately boring: deterministic rules doing the arithmetic, with a model only at the edges where judgment helps. We had no license to sell and no platform to defend, so we built the smallest thing that was actually correct.

cyclonesteel.com → How we work with manufacturers →

What going first taught us

The failure modes are not theoretical to us.

We have shipped models that were confidently wrong. We built the review steps that catch it, explained the miss to the person it affected, and carried the support load afterward.

Which is why our recommendations are usually narrower than you expect. We will tell you which parts of a process should stay deterministic, and we will say it early, because we have already paid to learn where the line sits.

We know what it costs to run

Not the licence price. The evaluation work, the review steps, the support load, and the maintenance after the demo ends.

We build for the team that inherits it

Every product here is operated by someone who did not write it. That constraint shapes how we build for you, because it is the constraint you actually live with.

We will say when AI is the wrong answer

Several of these systems have deliberately boring, deterministic cores with a model only at the edges. That is usually the right shape, and it is rarely the one being sold to you.

From the Blog

Want this pointed at your business?

30 minutes. Bring the process you think AI should be able to do. We will tell you whether it can, and what it would actually take. No slide deck.

Book a Scoping Call See the principles behind it →

Twenty-four years of what works. Three years pointing it at AI.