Demanding technical and document tasks
Code analysis, technical review, documentation writing, reading contracts or long case files. Claude returns structured answers in a required format, which makes them easier for the application to check.
AI integration · Claude
Claude API Development: AI Agents and AI Features in Production
Etixio builds AI solutions with the Claude API: document analysis, business assistance, agents and AI features built into your product. We connect Anthropic models to your data, your rules and your application’s tools, then deliver a production-ready service with evaluations, monitoring and cost control.
Understanding the technology
A model integrated into your application.
Claude models can be integrated into an application through an API to process language, analyze content or assist with an operation. Their role is defined within a software service: what data is sent, what result is expected and what checks come before it is used.
The Claude API is therefore not a product in itself: it is a building block we integrate into business software, into the AI features of a SaaS product or into an AI agent connected to your tools. The work covers everything around the model: data, access rights, controls, interface and operations.
The strengths for your product
Why choose the Claude API?
A model is chosen based on real examples, not on reputation. That said, Claude has strengths that often make it a good candidate for technical and document-heavy use cases.
Long context
An extended context window makes it possible to send a complete case file, a significant part of a codebase or several documents to compare, with less need for chunking.
Tool use for agents
The model can choose and call functions you expose (search for a file, read a record, prepare an action). This is the foundation of agents that work with your information system.
A safety-focused approach
Anthropic emphasizes a safety-first approach to its models. It does not replace your application’s controls, but it makes it easier to frame the expected behavior.
A solid SDK and documentation
Official SDKs, streaming responses, batch processing and prompt caching simplify integration and cost control.
Our scope of work
Choosing a model family for the work at hand.
We compare Claude families on the same set of representative requests: quality, latency, cost and data processing terms. The same models are available through the Anthropic API or through cloud providers such as AWS Bedrock and Google Cloud Vertex AI, which matters for data location. When hosting in Europe or on your own servers is required, we also benchmark Mistral or a self-hosted open-source model.
Claude Fable
Demanding reasoning and long-running agentic tasks.
Claude Opus
Knowledge work and agentic development.
Claude Sonnet / Haiku
Options to evaluate when speed and cost matter.
Our Claude API expertise
What we build with the Claude API.
Business agents and assistants
Assistants connected to your ERP, CRM or document repositories, which call tools under control and ask for confirmation before any sensitive action.
AI features in your product
Search across your documents (RAG), data extraction, summarization, classification or assisted writing, built into your application’s existing screens.
Technical tooling
AI-assisted code review, technical documentation generation, error analysis and migration support, integrated into your development pipelines.
Integration and operations
Anthropic SDK, streaming responses, tool calls, document and image processing, logging, usage tracking and error handling.
We also use Claude in our own engineering, with systematic human review. See our approach to AI-assisted engineering and our article on Claude Code.
In the field
An example processing pipeline.
To prepare a case file summary, we can first retrieve the relevant passages, then request an output in a precise structure. The application keeps the link to the sources and handles missing information. We test contradictions, incomplete documents and out-of-scope requests; a well-written answer is not enough to establish that it is accurate.
From prototype to production
Taking over a POC built with Claude.
A prototype that works on a handful of examples is not enough to make a production-ready service. We take over the POC’s code, instructions and data, then add what is missing for production: authentication and permissions, an evaluation set, error and timeout handling, logs, monitoring and usage caps.
The service is then maintained: tracking model changes, updating evaluations, fixing issues. See our AI POC to production service, our AI solutions for businesses and our article on how to develop an AI agent project.
The choices that matter
Governing tools and context.
With Claude, an agent calls the tools you expose to it: each tool is a permission. We define the list of tools, their allowed parameters and which ones require human confirmation before acting. Permissions are always enforced by the application, never by the model.
A long context gets expensive if it is resent with every call. We structure instructions to take advantage of prompt caching and reserve the most capable models for the steps that need them. Each new model version is replayed against the evaluation set before it is deployed.
From work to deliverables
The software delivered around Claude.
- Tools exposed to the model, with their permissions and confirmation rules.
- Connections to the data and services within the agreed scope.
- Evaluation set, tracking of errors, usage and caching.
- Code, configuration and documentation to run and evolve the service.
Model access goes through a dedicated layer in the code, so the model can be compared or replaced without rewriting the application. The code, API keys and accounts remain yours.
Frequently asked questions
Claude API: your questions.
What is the Claude API?
The Claude API provides programmatic access to Anthropic’s language models. An application sends it instructions, documents or images and receives a response, which can be structured or include tool calls. It is the foundation for building assistants, agents and AI features into software.
Claude, OpenAI or Gemini — which should you choose?
Each family has its strengths. Claude is often chosen for technical tasks, reading long documents and agents; OpenAI for its versatility and ecosystem; Gemini for multimodal use and Google Cloud integration. We decide on a set of representative examples, comparing quality, latency and cost. If hosting in Europe or on your own servers is required, Mistral or a self-hosted open-source model join the comparison.
Which use cases are a good fit for Claude?
Document analysis and summarization, business assistants connected to your tools, data extraction, code review, technical documentation generation and agents that chain several steps. The model is relevant when the task requires precision and the result can be verified.
Does Claude handle programming languages well?
Yes, Claude handles common languages (Python, JavaScript and TypeScript, Java, PHP, Go, Kotlin, Swift…) and their frameworks. For technical use, we integrate it with automated checks (tests, static analysis) and human review before any code is merged.
How can Claude be used for code review?
Claude can analyze a change, flag likely bugs, security or performance issues and suggest well-reasoned improvements. We use it as a review aid, built into merge requests, without replacing validation by a developer.
Is my data protected with the Claude API?
Data processing terms depend on the plan and access method chosen (direct API or cloud provider). We review them with you before starting, then limit the data sent to what is strictly necessary and enforce access rights in the application.
What are Claude’s limitations?
Like any language model, Claude can produce an answer that sounds plausible but is inaccurate. The most powerful models cost more and respond more slowly. Precise instructions, a controlled output format and an evaluation set are needed to deliver a reliable service.
How long does it take to integrate Claude?
It depends on the scope: a simple integration into an existing application takes a few weeks, a more complete solution with agents, data and evaluations several months. A scoping phase sets a realistic initial scope.
Can you take an existing AI prototype to production?
Yes. We take over the POC, measure its results on real examples, then add authentication, monitoring, error handling, evaluations and cost tracking. The service is then maintained over time.
What engagement models do you offer?
A fixed-price project for a solution with a defined scope, a dedicated team to evolve an AI-powered product over time, or a targeted engagement (POC takeover, audit of an existing use case).
Let’s build an AI use case integrated into your business.
Tell us what you want to build, the users involved and your technical environment. Together we will define the initial scope to explore.