SVC-04 / AI Solutions
AI Solutions
Models, RAG pipelines and decision systems wired into real business data.
The situation this addresses
A demo answered five questions correctly and nobody can tell you what it does on the other ten thousand. You need that answer before it touches a customer.
How we approach it
In order.
- 01Build the evaluation set before the system. If we cannot measure it, we do not ship it.
- 02Start with retrieval over your own documents, not with a bigger model.
- 03Put a person in the loop wherever being wrong is expensive.
- 04Instrument it, and keep scoring it after launch.
What this looks like in practice
Every AI project we are called into has already had a demo. The demo went well. That is the problem: a demo is a sample of size five, chosen by the person who built it, and it tells you almost nothing about the ten thousand cases that follow.
So we build the evaluation set first. A few hundred real cases from your own data, graded by people in your business who know what a right answer looks like. It is the least glamorous fortnight of the project and it is the reason the rest of it can be argued about with evidence instead of impressions.
Retrieval before scale
The common failure is reaching for a larger model when the actual gap is that the system cannot see the document that holds the answer. Retrieval over your own material — the contracts, the manuals, the ticket history — usually moves the score further than a model upgrade, and it costs less per call to run.
A person where being wrong is expensive
Not every decision should be automatic. We draw the line explicitly: this class of case goes straight through, this class goes to a human with the model's reasoning attached, and this class the system refuses. Refusing is a feature.
Measured after launch, not just before
Quality drifts — your data changes, the provider updates the model underneath you. The endpoint reports cost, latency and quality on the same evaluation set you signed off, so a regression shows up as a number rather than as a complaint from a customer.
What you end up with
Three things you can point at when it is finished.
SVC-04-01
An evaluation set and baseline scores you can re-run
SVC-04-02
Retrieval pipeline over your own documents and data
SVC-04-03
Monitored endpoint reporting cost, latency and quality
We provide
- Evaluation harness, scoring and error analysis
- Retrieval, prompts, serving and fallbacks
- Cost, latency and quality monitoring
You provide
- The documents and data the system will read
- Subject experts to grade a sample
- A written definition of what good enough means
- Engagement shape
- Pilot with defined success criteria, then scale-up.
- Indicative duration
- 4–12 weeks to a measured production pilot. Confirmed in writing after the discovery call, not before.
What usually sits next to it
Rarely bought alone.
SVC-05
Automation
Workflow, integration and process automation that removes manual handoffs.
Read the service detail
SVC-06
Chatbots & AI Assistants
Voice and chat agents that handle support, intake, scheduling and triage.
Read the service detail
SVC-03
Hardware Implementation
Specification, procurement, install and commissioning of the physical layer.
Read the service detail
Next step
Bring us the process, not the specification.
A discovery call is 45 minutes. You get a written summary of what we heard and what we would do about it, whether or not you go further with us.