Do you train custom models?

Rarely, and only where the case is genuinely made. For most business problems, retrieval over your own data with a strong general model outperforms a fine-tune on the same budget and stays far cheaper to maintain as models improve. Where fine-tuning is the right answer we will say so, and we will say so when it is not.

Can this run on our own infrastructure?

Yes, where the requirement is real. Self-hosted open-weight models are a reasonable answer to a genuine data residency or regulatory constraint and an expensive answer to general unease. We will work through which one applies before it drives the architecture.

How do you stop it producing wrong answers?

You do not stop it entirely, and any supplier who says otherwise is selling something. You constrain it with retrieval over sources you control, measure it with an evaluation set, design an explicit path for abstaining, and put a human in the loop wherever the cost of being wrong justifies one.

Service

AI Platform Development

A model is not a product. It is one component inside a system that has to retrieve the right context, handle the case where the answer is wrong, show its reasoning to somebody who has to act on it, and cost a predictable amount per month. We build that system.

Models are a component, not the product

The demonstration is easy now. Any capable model wired to a prompt produces something impressive within a day, which is exactly why the demonstration proves so little. The difficulty is everything around it: what happens on the twentieth percentile input, how a wrong answer is caught before it reaches a customer, and what the system does when the provider has an outage.

So the first question on any AI engagement is what decision the output actually informs, and what the cost of being wrong is. A system that drafts an internal summary and a system that flags a transaction for review need completely different tolerances, and pretending otherwise is how AI features get built and then quietly switched off.

Evaluation before scale

An AI system without an evaluation set is not being engineered, it is being adjusted until it looks right on whatever inputs the last person happened to try. We build the evaluation harness alongside the feature: a fixed set of representative cases, scored on criteria agreed in advance, run on every change.

That harness is what makes it possible to change the prompt, the retrieval strategy or the model itself and know whether the result improved. Without it, every one of those changes is a guess, and swapping to a newer model becomes a risk rather than an upgrade.

Reasoning that survives contact with a user

Where a person has to act on the output, they need to see what it was based on. That means citations to retrieved source material, the confidence the system actually has rather than the confidence its prose implies, and a visible path back to the underlying record.

It also means designing the failure case properly. Abstaining is a valid answer. A system that says it does not know is more useful than one that produces a fluent, wrong, unfalsifiable paragraph, and building the abstention path is usually the difference between an AI feature people trust and one they learn to ignore.

Cost and provider independence

Per-request cost is a design parameter, not a bill that arrives later. Retrieval strategy, context size, caching and model selection per task are the levers, and they are decided with the same seriousness as any other capacity question.

The provider boundary stays behind an interface. Model quality and pricing have moved substantially and repeatedly, and a platform welded to one vendor's API surface cannot take advantage of that without a rewrite.

What an engagement includes

Definition of the decision the system informs and the tolerance for being wrong
Retrieval and context pipeline over your own data
Evaluation harness with a representative case set, scored and run on every change
Provider abstraction so models can be changed or compared without a rewrite
Reasoning surfaced to the user: citations, sources and an explicit abstention path
Per-request cost instrumentation and controls
Monitoring for quality drift, latency and provider failure

Common questions

Do you train custom models?: Rarely, and only where the case is genuinely made. For most business problems, retrieval over your own data with a strong general model outperforms a fine-tune on the same budget and stays far cheaper to maintain as models improve. Where fine-tuning is the right answer we will say so, and we will say so when it is not.
Can this run on our own infrastructure?: Yes, where the requirement is real. Self-hosted open-weight models are a reasonable answer to a genuine data residency or regulatory constraint and an expensive answer to general unease. We will work through which one applies before it drives the architecture.
How do you stop it producing wrong answers?: You do not stop it entirely, and any supplier who says otherwise is selling something. You constrain it with retrieval over sources you control, measure it with an evaluation set, design an explicit path for abstaining, and put a human in the loop wherever the cost of being wrong justifies one.

Related practices

Let's Build Something Extraordinary.

Whether you're looking to collaborate, partner with us or simply follow our journey, we'd love to hear from you.

info@starblyn.com