The entry engagement
Find the opportunity before you fund the build.
The AI Opportunity Sprint turns an operational bottleneck into a decision: implement, defer, buy, or stop—with value, risk, dependencies, and measurement made visible.
The decision stage
Map. Measure. Check. Decide.
The work is examined as an operating system—not as a shopping list of models or tools.
Make the current workflow visible.
Trace the people, systems, information, handoffs, exceptions, and ownership around the work as it operates today.
Establish what makes the change valuable.
Define the frequency, delay, quality impact, baseline, and practical measure that would make an intervention worth pursuing.
Test feasibility and responsibility.
Examine data sensitivity, access, dependencies, human review, adoption risk, and what could go wrong before a build is recommended.
Recommend the smallest responsible next step.
Implement, defer, buy an existing tool, or stop—with the reasoning, dependencies, and measurement approach made visible.
Scope and fit
A focused assessment needs a concrete place to start.
Best fit is a service business with a named operational bottleneck, an accountable sponsor, access to the people doing the work, and representative process information.
- The current workflow and its handoffs
- Up to three candidate opportunities
- Value, feasibility, and dependencies
- Data sensitivity and operating risk
- Adoption, ownership, and measurement
A named sponsor, the right people in the workshop, representative process samples, relevant system constraints, and written permission before client data enters an AI tool.
Do not send confidential, regulated, personal, or sensitive information through the public inquiry form.
What you leave with
Decision evidence, not production software.
- A current-state process map and pain-point summary
- An opportunity scorecard for up to three candidates
- A recommended first use case
- An indicative implementation roadmap
- A measurement plan
- A readout and one revision round
Production software, integrations, data migration, legal or compliance opinions, security certification, custom model training, guaranteed savings, and unlimited revisions.
Timing, commercial terms, workshop length, and payment terms are intentionally not published while the offer is being operationally approved.
Questions
Before the first decision.
What if we do not know which workflow to choose?
That is a reason to begin with the Sprint. We compare a small set of candidate workflows and make the decision criteria visible before recommending a build.
Could an existing tool solve the problem?
Possibly. If a suitable tool fits the workflow, data, team, economics, and risk, buying it may be the better decision. The goal is a useful outcome, not a custom build by default.
Will the Sprint produce production software?
No. Production software, integrations, data migration, custom model training, and security certification are outside the Sprint. A build is considered only after the case and boundaries are clear.
How is our data handled?
The answer depends on the workflow and tools. Before representative data is used, the engagement should document the data flow, access, retention, human review, and contractual requirements.
Can Radius guarantee savings or revenue?
No general outcome guarantee is offered. The Sprint can define assumptions, a baseline, a measurement plan, and a clear recommendation; business outcomes also depend on implementation and adoption.