AI solutions for business
Etixio designs and builds enterprise AI solutions connected to your information system: AI agents and business assistants, AI features built into your products, document processing. We also take AI POCs all the way to production, then maintain the service. Scoping, data, interfaces, integration and operations are all part of the same project.
Companies and software vendors that want to support a specific business task with AI or finish an AI POC
Assistants connected to your ERP or CRM, document search, document processing, AI product features
A scoping workshop on your goals, data and tools, with no technology chosen upfront
First use case as a fixed-price project, then a dedicated team led by a tech lead over time
What we deliver
A team wastes time looking for a procedure. A department re-keys information from documents. Employees have to cross-check several ERP screens to answer a question. These situations provide a concrete starting point: who uses the solution, on what data and to achieve what result.
We study the current process before choosing an approach. Traditional search, deterministic automation or a change to the application is sometimes enough. When AI brings a useful capability — language understanding, extraction or summarization — we define precisely where it fits in the workflow.
An employee makes a request and gets information from their tools: project status, lead follow-up, details of a file. We develop the interface, the connections to services and the permission management. Operations that modify data can require explicit approval.
To make use of procedures, contracts or case files, we organize document ingestion, indexing and retrieval of relevant passages. A RAG architecture uses these elements to prepare an answer. The sources returned, document freshness and behavior when information is missing are all part of the evaluation criteria.
Classifying a document, extracting fields, reconciling information or preparing a report: we build the complete pipeline, up to delivery into the business application. Format checks, ambiguous cases and human review prevent a model output from being treated as automatically reliable data.
A software vendor can add search assistance, analysis or an assistant to its SaaS. We take charge of the AI service, its integration into the screens, usage tracking and rules specific to the product’s customers. The model becomes a software component, with a controlled interface and release cycle.
The right time
An AI solution creates value when it addresses a specific task, relies on your data and can be run like any other software. Four conditions guide our recommendations.
Support, sales, finance, HR or IT: we start from a recurring task, identified users and a measurable result, such as time saved or response quality.
ERP, CRM, internal documents, product database or support tickets. Their availability, freshness and access rights determine feasibility far more than the choice of model.
Access rights applied to the data accessed, conversation logs, confidentiality rules and compliance are defined before go-live. See the precautions to take with AI in the enterprise.
Monitoring, versioning of models and prompts, regular evaluations and continuous improvement. A prototype that does not plan for operations remains a demo.
From prototype to production
Many AI projects stop after a convincing demo: the prototype answers well on a few examples, but it has no authentication, no monitoring and no cost control. We take over these POCs, whether they were built in-house, by a vendor or with code generation tools, and turn them into a service you can run.
We review the code, the data, the model choices and the results obtained, then list what is missing for production. Our experience report on going from a vibe-coded project to a product in production details these gaps.
Authentication, permissions applied to data, secrets management and protection against malicious instructions hidden in documents.
A set of representative business cases, measurement of response quality, traces of model calls and alerts in case of drift.
Usage tracking, model choice per step, caching and response times acceptable to users.
Model changes, new data sources, user feedback and fixes are handled within a defined maintenance framework.
Delivery in practice
This internal project provides access to project, leave and sales pipeline information through a conversational interface. Building it combined business understanding, conversation design and connection to the existing system. It illustrates the work needed to make company data accessible from a question.
For a hospital, the project explores the information in the patient record and provides anesthesia teams with a chronological summary. The scope combines document search, business data and a dedicated viewing interface. It is an aid for reading the record.
From work to deliverables
We identify the users, the available sources, the expected operations and the access constraints. We select a first testable scope and prepare representative examples, including requests the solution should decline to answer. You get a scope, success criteria and the dependencies to resolve.
The prototype makes it possible to evaluate models, document search and response formats on your business cases. We look at useful answers as well as errors: missing information, conflicting sources, incorrect extraction or inappropriate action. The rest of the project builds on these observations and on the estimated operating cost.
We build the APIs, screens, authentication and tool connections. User permissions remain applied to the data accessed. We prepare tests, logs and fallback paths for when the model or a service does not respond as expected.
Delivery includes the code, configurations and documentation for the scope delivered. Monitoring covers response quality, errors, processing time and usage. New documents, model changes and user feedback drive improvements and new evaluations.
The choices that matter
We work on data preparation, document search, model calls and tool orchestration. For an agent, we define the available actions, their parameters, approvals and execution limits. For an application, we also handle job queues, error recovery and processing monitoring.
The choice between the OpenAI, Claude or Gemini model families depends on the task, how data is processed, latency and budget. We compare configurations on the same set of examples. A more expensive model is not automatically the best choice for every step of a processing pipeline.
How we work together
You can come with a business problem and examples of documents or requests. The first step is to examine data availability and how to measure usefulness. The access required and the confidentiality framework are defined before any use in the project.
The budget covers development and integration, but also model calls, hosting, any storage and maintenance. We separate these items and examine how they change with volume, to compare a prototype with a service in real use.
Your business team validates the expected results; we handle delivery and technical checks for the agreed scope. Support, response times and change management are specified in the collaboration framework. Sources, access and documentation are planned to make it easy to hand over the service.
Two delivery formats. A first use case with a defined scope can be delivered as a fixed-price project. When AI is part of a product roadmap, a dedicated team of French-speaking engineers in Madagascar and Mauritius, guided by an Etixio tech lead, develops and maintains the AI features over time. Our own practices are described in our AI-augmented engineering.
Frequently asked questions
With a scoping workshop based on your goals, your data and your tools. We identify the two or three priority use cases according to their value, feasibility and risks, then a first testable scope with success criteria. You do not need to have chosen the technology.
No. A chatbot is often a first entry point, but document extraction, automatic summarization or an AI feature in a product may be more useful. Sometimes traditional search or automation without AI is enough; we tell you when that is the case.
The POC verifies on your real cases that a use case delivers value, before committing to a rollout. It is tested with a panel of business users. If it is conclusive, we handle integration with your systems (SSO, APIs, monitoring), go-live and then maintenance.
Yes. We analyze the code, the data and the results, then add what is missing for production — authentication, error handling, evaluations, monitoring, cost control — or recommend rebuilding certain parts when that is safer.
User permissions remain applied to the data accessed, conversations are logged and access is defined before any use. The choice of model and hosting mode takes your confidentiality and compliance requirements into account.
It depends on the task, how data is processed, latency and budget. We compare several configurations on the same set of examples drawn from your business, and a single service can use different models at different steps.
The budget combines development and integration, model calls, hosting, storage and maintenance. We separate these items and estimate how they change with volume, to compare a prototype with a service in real use.
Yes. Support and corrective and evolutive maintenance cover response quality, incidents, model changes, new data sources and new use cases added gradually based on user feedback.
One example task, a few data sources and the users involved are enough to start the conversation. We help you identify what is worth prototyping and what needs to be prepared.