Internal assistants
Find procedures and business information from the sources the user already has access to. IT support, HR questions, internal documentation or assistance for sales teams.
Our service
AI Agents and Enterprise Chatbots: From Pilot to Production Service
Etixio designs, builds and maintains enterprise AI agents and chatbots connected to your data and business tools. A useful assistant links the models, the knowledge and the authorized actions within your information system: we build that link, from the first pilot to a monitored service in production.
For
Companies and SaaS vendors that want an assistant or agent connected to their data and business tools
What you get
An AI agent or chatbot connected to your sources, with access rights, approved actions and monitoring
Format
Discovery and architecture, pilot, then scale-up and maintenance
Getting started
A first pilot usually within a few weeks, depending on integrations and data
What we build
A scope tied to your business.
An AI chatbot helps people find information; an AI agent can also call tools to carry out an operation. The scope must spell out that difference. We start from real requests and authorized actions: looking up a record, drafting a reply, qualifying a request or triggering a process after approval.
Customer service
Answer common requests, qualify a need and hand over situations that require a human.
Business actions
Connect CRM, ERP or support tools with explicit permissions and approvals, to create a ticket, update a record or track an order.
Agents built into your product
A software vendor can add an assistant to its SaaS application. The agent becomes a product feature, with per-customer rules, usage tracking and its own release cycle. See our enterprise AI solutions.
Under the hood
How do our AI agents and chatbots work?
Our AI agents rely on large language models (LLMs) and a RAG (retrieval-augmented generation) architecture that retrieves the relevant passages from your sources before drafting an answer. The model is only one component: quality depends mostly on the data, access rights and controls around it.
Connection to your sources
Files (PDF, Word, Excel), internal knowledge bases, ERP, CRM and business tools through their APIs.
Indexing and access rights
Content indexing, filtering by profile and connection to your identity management (SSO, Azure AD). Each user only accesses the information they are already entitled to.
An interface suited to the use case
A widget on your website or intranet, Teams, Slack or another collaboration tool, or a dedicated screen in your web or mobile application.
Monitoring and continuous improvement
Conversation traces, quality tests, cost tracking and gradual enrichment of the knowledge base.
From work to deliverables
How the engagement unfolds.
We identify the knowledge sources, the available APIs and each user’s rights. A first user journey is used to check retrieval, answers and escalation cases. For actions, we define the accepted parameters, the required confirmations and the behavior in case of failure or repeated requests. The interface makes the limits and the outcome of each operation visible.
Discovery and target architecture
Business workshops and conversation design, review of data, risks and compliance, target architecture (RAG, tools, channels) and pilot roadmap.
Pilot and launch
A first AI agent or chatbot connected to your data, with its connectors (Zendesk, Intercom, Slack…), its actions (tickets, CRM, orders), its dashboards and its quality tests.
Scaling and maintenance
Observability, guardrails and adversarial testing, cost and latency optimization, human handoff, operating procedures and service commitments.
What your team receives
- Prioritized use cases
- Business test set
- Integrated application and monitoring
From prototype to production
Does your AI agent already exist as a prototype?
An agent that works in a demo is not yet a service. We take over AI agent and chatbot POCs to make them production-ready: authentication and access rights, error handling and retries, evaluations on real requests, monitoring, cost control and documentation. We keep what holds up and rebuild what needs to be rebuilt.
Our article on taking an AI agent project from POC to production describes the approach.
Delivery in practice
AI agents already built by Etixio.
An agent that summarizes medical records
For a hospital, the agent explores the information in the patient record and gives anesthesia teams a chronological summary, as a reading aid.
Bobby: querying our ERP data
An internal project that gives access to project, leave or sales tracking information through a conversational interface connected to the ERP.
The choices that matter
The points to decide with your team.
An agent’s relevance is measured on representative requests, not on a demo conversation. We examine errors, cost, response time and handoff to a human. Permissions granted to tools stay limited to what is needed; instructions sent to the model do not replace application-level controls.
Technologies and integrations
The models and tools we integrate.
We adapt the technology to your security, governance and budget constraints, whether that means an open-source solution, an off-the-shelf service or a hybrid approach.
Models
OpenAI, Anthropic (Claude), Google (Gemini, Vertex AI), Azure AI, Cohere or models compatible with the OpenAI API.
Development
Node.js and TypeScript, Python, React, vector databases for RAG, tracing and observability, security guardrails.
Conversational platforms
Rasa, Botpress, Intercom Fin or Zendesk AI when your support already relies on these tools.
How we work together
Preparing the first conversation.
You can walk us through how things work today, the users involved and the difficulties you face. The documents, examples and access required are specified afterwards, depending on the agreed scope. The first goal is to understand the work to be done and the dependencies that may affect how it unfolds.
Scoping and the pilot can be delivered as a fixed-price project; the agent’s long-term evolution and maintenance are handled by a dedicated team led by a tech lead.
Frequently asked questions
AI agents and chatbots: your questions.
What is the difference between a chatbot and an AI agent?
A chatbot answers questions based on a knowledge base or predefined rules. An AI agent can also carry out actions, interact with your business tools and automate certain tasks, within the permissions and approvals that have been set.
Which use cases are best suited to an AI chatbot?
Customer support and IT support, access to internal documentation, HR questions, business assistance and qualifying requests before a human steps in. Our article on the key functions of an AI chatbot covers these uses in detail.
How long does it take to deploy an AI chatbot?
A first pilot can usually be set up in a few weeks. The timeline depends on the use cases, the integrations required, the quality of the available data and the level of customization expected.
Can an AI agent be connected to our business tools?
Yes. We connect agents to CRMs, ERPs, ITSM tools, document repositories, intranets or business applications through their APIs, to look up information or trigger actions. Operations that modify data can require explicit confirmation.
How do you keep company data secure?
The agent is connected to your identity management and authorization rules, so each user only accesses the information they are already entitled to. Conversations are logged and tool permissions stay limited to what is needed.
Which artificial intelligence models do you use?
We choose based on your technical, security and governance constraints. Solutions can rely on OpenAI, Anthropic, Azure AI, Vertex AI or other compatible models, compared on the same set of real requests.
Can you take over an AI agent developed as a prototype?
Yes. We analyze the POC’s code, data and results, then add what is missing for production — access rights, error handling, evaluations, monitoring and cost control — before launch and maintenance.
Do we keep ownership of the code and data?
Yes. The code produced, the configurations and the data remain yours. Documentation and access are set up so that your team, or another one, can take over the service.
Let’s talk about your AI agent project.
Tell us what you want to build, who the users are and what your technical environment looks like. Together, we will define the first scope to explore.