AI development services — LLM features that survive production | Primevise

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Applied AI

# AI features that survive production

We design and ship LLM-powered features — assistants, automation, extraction, agents — with the machinery that keeps them working: evals, fallbacks, cost control. And if a heuristic would do the job, we'll say so.

[Book an intro call](https://cal.com/elvinas/primevise-exploration) [Read the Loctax case](https://primevise.com/cases/loctax)

Capabilities

## What we build with AI

Features built into the product, not bolted on — each with the operational machinery that keeps it working after launch.

### LLM integration

Assistants, summarisation, extraction, and generation — built into your existing product with schema-validated outputs and explicit degraded modes.

- — Assistants & copilots
- — Document extraction
- — Summarisation & generation

### Agents & automation

Multi-step workflows that act on your systems — with human checkpoints wherever a mistake would be expensive.

- — Workflow automation
- — Internal tooling
- — Human-in-the-loop review

### Evals & reliability

Every prompt change and model upgrade runs against an evaluation set built from your real data. Cost and latency instrumented from the first deploy.

- — Evaluation sets in CI
- — Cost & latency monitoring
- — Fallbacks & kill switches

### AI discovery

A short, fixed-scope discovery that maps where AI genuinely helps your product — and where a rule would do the same job for free.

- — Feasibility assessment
- — Data & workflow review
- — Prioritised roadmap

How it works

## Smallest shippable version first

AI features are cheap to prototype and expensive to perfect — so we sequence for real usage, not a big reveal.

1. 01 ### Discovery

   Map the candidates. Three questions kill bad ideas early: what happens when it's wrong, would a rule do, where does the data come from.
2. 02 ### First version

   One workflow, one model, behind a feature flag — weeks, not months. Real users tell us what to invest in next.
3. 03 ### Evals & hardening

   An evaluation set from real usage, schema validation, degraded modes, cost instrumentation. The unglamorous part that makes it a product.
4. 04 ### Scale what works

   Retrieval, better prompts, or a bigger model — decided by measured usage, not guesses. Model swaps stay a config change.

FAQ

## Questions teams ask about AI work

<details>

<summary>

Which models and providers do you work with?

</summary>

We're provider-agnostic and keep the model call behind a thin abstraction — one module, nothing fancy — so next year's better model is a config change, not a rewrite. Provider choice follows your data constraints, latency budget, and cost curve.

</details>

<details>

<summary>

Can you add AI to our existing product?

</summary>

That's most of the work we do. The best first feature is usually the task your users already do reluctantly and repeatedly — drafting, summarising, categorising, extracting — built into the flows they already use.

</details>

<details>

<summary>

What does an AI feature cost to run?

</summary>

Launch costs are almost always fine; the risk is the shape of the curve as usage grows. We instrument cost per request from the first deploy and design the levers in advance — caching, smaller models for easy cases, batching, usage caps.

</details>

<details>

<summary>

What if AI turns out to be wrong for our use case?

</summary>

Then we'll tell you in discovery, before you've paid for a build. A surprising number of "AI features" are a well-chosen heuristic plus good UX — if that's your case, you'll hear it from us first.

</details>

Let's talk

## For new products, existing codebases, and ideas that aren't scoped yet.

[Book an intro call](https://cal.com/elvinas/primevise-exploration) [Tell us about your project](https://primevise.com/contact)

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