AI Integration Services

What AI integration adds to the product you already run

AI integration means adding a language model feature to software you already have, instead of building a new product. Arcesso works inside your codebase, your cloud and your design system, so the feature feels native to your users.

Common first features are search that understands questions, summaries of long records, draft replies, document extraction and an in-app assistant that knows your product. Each one starts with a clear job and a way to measure it, such as time saved per task or tickets deflected.

We read your code before we propose anything. Some products need a small API layer and a few prompts. Others need a retrieval pipeline, background jobs and new permissions. The scope document tells you which, with a fixed price and the expected monthly model cost, before any build starts.

OpenAI vs Claude vs Gemini vs open-weight models

Arcesso picks the model per feature using evals on your own data, not brand preference. Each provider has strengths, and many products end up using two: a stronger model for hard tasks and a faster, less expensive one for routine calls.

The table is a starting point as of October 2026. Model lineups change often, so we rerun the evals before each major release and switch when the numbers justify it.

Open-weight models such as Llama, Mistral or Qwen make sense when data cannot leave your infrastructure or when volume is high enough that a fixed hosting cost beats per-call pricing. They need more engineering to run well, so we only recommend them when the eval results and the monthly numbers both point that way.

RAG that answers from your own data

Retrieval-augmented generation (RAG) lets a model answer from your documents and databases instead of its training data. Arcesso builds RAG pipelines that split your content into searchable pieces, store them in a vector index such as Postgres with pgvector, and pass the most relevant pieces to the model with each question.

Good RAG is mostly data work. We clean and structure the source content, respect the same access rules your app already uses, and show citations so users can check every answer. A refresh job keeps the index current when documents change.

We measure RAG quality with an eval set of real questions and known answers. If retrieval misses, we fix the data or the search before touching the prompt. That order of work keeps answers accurate and stops the model from guessing when the answer is not in your content.

MCP server development for Claude and other models

A Model Context Protocol (MCP) server exposes your tools and data to AI models through one open standard. Arcesso builds MCP servers so Claude, OpenAI models and AI agents can look up records, create tickets or run reports in your systems without custom glue code for each model.

Every MCP tool we build has authentication, input validation, rate limits and logging. Tools that change data can require a human approval step. You decide which tools exist, and each model client only sees the tools it is allowed to use.

MCP is useful when several AI features or agents need the same capabilities, or when your customers want to connect their own AI assistants to your product. If you only need one feature calling one API, a direct integration is simpler, and we will recommend that instead.

How we control AI cost, quality and vendor risk

Arcesso writes evals before prompts, so every AI feature has a measured quality bar before launch. An eval set is a list of real inputs with expected outputs, scored automatically on each change and each model update.

• Cost control: caching, shorter prompts, smaller models for routine calls and per-feature spending caps with alerts.
• Fallback design: a second provider or a non-AI path when a model is slow, down or unsure.
• Privacy: personal data masked before model calls where possible, and no-training settings where vendors offer them.
• Observability: logs of inputs, outputs, latency and cost per request, stored in your accounts.
• No lock-in: prompts, evals and routing live in your repository, so switching providers is a config change, not a rebuild.

When Arcesso is not the right choice for AI integration

Arcesso is not the right choice if you need to train a large model from scratch or run a research program on custom model architectures. That work needs a specialist machine learning lab, and we will point you to one.

We are also not a fit if the AI feature has no clear user job yet. Adding a chat box because competitors have one rarely moves a metric. In that case, our AI readiness assessment ranks your options first.

Arcesso is a good fit when you have a working product, a team that wants AI features shipped in weeks, and a need to keep customer data under control. You work with senior engineers at a US company with offices in Jacksonville, Florida and Arlington, Texas, during US business hours.