Use-case selection
A ranked shortlist scored on value, feasibility, data readiness and risk. Most of the value is in what we agree not to build.
Build track: Artificial intelligence
Ship AI that survives contact with real users.
Demos are easy. The thousandth request is the hard part: the ambiguous input, the tool that times out, the answer nobody can verify. We build for that part first, because that is the part your customers actually meet.
The problem
The expensive way to learn this
Most AI builds start with a model and look for a job for it. Six months later there is a working prototype, no owner, no evaluation set, and nobody able to say whether it beats what the team did before. We start from the workflow and the measurement, then pick the technology that clears it.
What you get
Four things, done properly.
Evaluation before engineering
A test set drawn from real inputs with a defined pass mark. If the system cannot clear it, we change the approach rather than the demo script.
Guardrails and handoff
Permissions, escalation, retry behaviour, cost ceilings and the exact moment a human takes over. The unglamorous work that decides whether it stays switched on.
A first release, in production
One workflow, live, instrumented and owned, not a pilot that needs another pilot.
Process
How the work runs.
- 01
Map the workflow and establish the current baseline.
- 02
Agree the measure that decides whether this worked.
- 03
Prototype the narrowest useful version.
- 04
Test against real inputs, including the awkward ones.
- 05
Release to a bounded group with monitoring in place.
- 06
Improve from production data rather than opinion.
Fit
Worth a conversation when…
- A workflow with real volume behind it
- Access to the data the system needs
- A named owner on your side
- Willingness to measure the result honestly
Not a fit
Probably not us when…
- A mandate to “add AI” with no workflow attached
- A launch date fixed before the problem is defined
- Data nobody is able to share or describe
Questions
What people ask first.
Do we need an AI strategy before we start?
No. A strategy written before you have shipped anything tends to describe the market rather than your business. One working release teaches more than a deck.
Can you work with our existing tools?
Usually. The preferred approach is to work with the CRM, helpdesk, data warehouse and calendar your team already uses rather than replacing them.
Who owns the model choice?
You do, and it should stay reversible. We design behind an interface so the underlying model can be changed as capability and pricing move.
Can you work with regulated data?
Potentially, after a data-flow, vendor and compliance review. Nothing is promised about regulatory status before the exact deployment and contractual controls have been assessed.
Compliance status depends on the final architecture, vendors, contracts and your own controls. EBG designs systems to support applicable security and compliance requirements; it does not certify systems and does not claim regulatory compliance on your behalf.