CLIEncoders · Services
AI integration and automation services
Adding AI to a working product is mostly an engineering problem, not a modelling one. The model is a dependency; the hard parts are evaluation, cost, latency and what happens when it is confidently wrong.
Start with the failure case
Every AI feature has a wrong-answer rate above zero. The question that decides whether it should ship is what that wrong answer costs. Suggesting a slightly odd product is survivable; approving the wrong invoice is not.
So we design the failure path first — confidence thresholds, human review where the stakes justify it, and an audit trail of what the model saw and produced. Features whose failure mode is unacceptable and unguardable are better not built, and we will say so before you fund one.
Evaluation before enthusiasm
A demo proves a feature can work once. An evaluation set proves how often it works, and it is the only thing that lets you improve a prompt or swap a model without guessing. We build one early from your real data, keep it in version control, and run it in CI.
That evaluation set is also what makes the model layer replaceable. Providers change pricing and deprecate versions; a system with an eval suite treats that as a routine migration rather than a crisis.
Cost and latency are design constraints
Token cost scales with usage in a way most feature budgets do not anticipate, and a feature that is delightful in a demo can be uneconomic at real volume. We model this up front — including caching, routing simple requests to smaller models, and the option of a self-hosted open-weight model where volume makes it cheaper than per-token API pricing.
Provider choice is a decision we make with you rather than for you: Claude, GPT, Gemini, DeepSeek, Qwen, or something running entirely on your own hardware.
Questions
What people ask before starting
Which model should we use?
Whichever wins on your evaluation set at acceptable cost and latency — which is why we build the eval set before committing. Preferences stated in advance of measurement are just preferences. In practice most systems end up mixing models, routing straightforward requests to something small and cheap and reserving the capable model for cases that need it.
Will our data be used to train someone else's model?
Not under the enterprise and API terms of the major providers, which differ importantly from their consumer products. We will read the specific terms for the tier you are on and tell you what they say. Where the obligation is strict or the data is genuinely sensitive, a self-hosted open-weight model removes the question entirely.
How do you stop it from making things up?
You reduce it rather than eliminate it. Grounding answers in retrieved documents with citations, constraining outputs to a schema, verifying claims against a source of truth before acting, and refusing to answer below a confidence threshold all help substantially. Any vendor claiming to have eliminated hallucination is describing a marketing position, not a technical one.
Can you add AI to a product without rebuilding it?
Usually, yes — most AI features attach at the API layer and need no rewrite. What sometimes does need work is the data: a feature that answers questions about your content needs that content reachable and clean, and that preparation is frequently the larger half of the project.
Related
Where this usually connects
Tell us what you are building
One technical call is usually enough to tell you whether this is straightforward, genuinely hard, or the wrong approach entirely. We would rather say so early than quote for the wrong thing.
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