Enterprise AI Platform

Build AI agents on your systems.
In days, not months.

PaLoMa AI is the platform to bring generative AI inside your processes: technical documents, ERP, PLM, business applications, any system with an API. Configured from a single admin, multi-LLM, enterprise governance from day one.

Request a demo See how it works

MCP-native Multi-LLM, no vendor lock-in Deploy on-prem or EU cloud

In production

Already live in an automotive enterprise with custom MCP toward internal business systems, models on Azure OpenAI and multimodal technical documentation (text + images + diagrams).

The problem

Generative AI promises a lot. Implementing it well in the enterprise is a different story.

Limited connectors

No-code tools generally only speak with applications external to enterprise environments. Your custom business application, proprietary PLM, SAP database stay out. And without access to real data, AI is useless.

Vendor lock-in

The platform is hard-wired to GPT-4 or only Azure. Tomorrow a better, cheaper model comes out or you no longer want to depend on a single cloud: you have to redo everything from scratch.

Six months before seeing results

Custom development with LangChain + vector DB + RAG pipeline = 3-6 months of technical work, external consultants, and a fragile solution to maintain. Time-to-value kills the project.

The solution

From document to chatbot in 5 minutes.

Open PaLoMa AI. Create a workspace, name it, choose the LLM model. Upload your documents. PaLoMa runs indexing, vectorisation, OCR on scanned content. In 5 minutes you have an AI agent that answers on your content, citing sources.

45 SECONDS · WORKSPACE CREATION → DOC UPLOAD → LLM CONFIGURATION → PUBLISH

Key features

One platform. Everything you need.

Six capabilities that together answer "why PaLoMa AI instead of no-code alternatives or building it in-house?".

MCP-native, MCP custom on demand

Connect any system, even the most exotic one.

Native Model Context Protocol — 2025 standard for enterprise LLMs
We develop the MCP server tailored to your stack: REST, SOAP, GraphQL, direct DB
Your internal team writes zero integration code
Already operational: ERP, PLM Teamcenter, WMS, ticketing, Outlook

Detail of a custom MCP server (ERP Connector) configured in PaLoMa AI

Multi-LLM, no vendor lock-in

Switch models with a dropdown.

Claude 4.x via Anthropic or Bedrock for complex reasoning
GPT-4o / GPT-5 on Azure OpenAI for data sovereignty
Gemini Pro for multimodal tasks and extended context window
Llama / Mistral on-prem for cases with classified data
Choose from UI per agent or per task. Change whenever you want.

Multimodal RAG

Do your technical manuals have diagrams and photos? PaLoMa indexes text, tables, schemes and images. The answer cites the paragraph and shows the right diagram.

Specialised agents

One bot for HR, one for service, one for sales — same admin, separate knowledge bases, granular permissions. Each one talks to relevant systems and sees only the data it has access to.

No-code

Create agents, manage knowledge bases, configure permissions, monitor usage from a single interface. IT does not become a bottleneck.

Function calling

Agents don't just read — they take actions: open tickets, search inventory, book calendar slots, kick off workflows. With permissions and audit.

Flexible deploy

Managed EU cloud, on-prem on your infrastructure, or hybrid. Compliance, data sovereignty and isolated network when needed.

How it works

PaLoMa at the centre, your systems around it.

The intelligence hub that talks to your systems via MCP, dispatches requests to the right model, and keeps governance under control.

YOUR SYSTEMS ON THE LEFT · PALOMA AT THE CENTRE · LLMS ON THE RIGHT · GOVERNANCE ABOVE · USERS BELOW

In production

Automotive technical documentation, natural language search.

CASE STUDY · AUTOMOTIVE SECTOR

From static archive to conversational knowledge.

A multinational in the automotive sector uses PaLoMa AI to give access to thousands of internal technical documents — manuals, specifications, diagrams, repair procedures — to operators who previously had to memorise the archive or call an expert.

The LLM model runs on Azure OpenAI for compliance and data governance reasons. PaLoMa AI is connected via custom MCP to internal business systems, developed bespoke by FITEC. Technical documentation includes diagrams and photographs: PaLoMa indexes the multimodal content and cites the precise source in responses.

Zero learning curve

Technicians speak to the platform in natural language: no new system to learn, no product codes to remember, no interfaces to navigate. They ask as they would ask a colleague.

Faster operations

Time to find the correct information — the part, the procedure, the right diagram — drops dramatically. The user goes from question to answer in seconds.

Knowledge preserved

The expertise of seniors, scattered across company documents, becomes accessible to all — even those who joined yesterday. Turnover no longer wastes critical knowledge.

The comparison

PaLoMa AI vs the alternatives.

The options that the enterprise buyer evaluates in parallel, compared on the criteria that really matter.

	In-house build (LangChain + ...)	PaLoMa AI
Time to production	3-6 months	Days
Custom system connectors	To be developed	✓ Custom MCP on demand
Multi-LLM (vendor neutrality)	Depends	✓ Switch from UI
Multimodal RAG (PDF images)	To be developed	✓ Native
Enterprise governance / audit	To be developed	✓
Ecosystem dependency	Independent	Independent
Data sovereignty (on-prem / EU)	✓	✓
Support and maintenance	Your team	FITEC (Turin)

Frequently asked questions

The questions we get most often.

How long does it take to go into production?

For the first agent with your own documents: 5-10 business days. For custom MCP integrations toward proprietary systems we add 2-4 weeks of dedicated development, in parallel with the rollout.

Which LLMs does PaLoMa AI support?

All the major ones via API: Anthropic (Claude 4.x), OpenAI (GPT-4o, GPT-5), Google (Gemini), and via Azure OpenAI for data sovereignty requirements. They are selected from UI per agent or per task. We also support open source on-prem models (Llama, Mistral).

Does my data stay in Europe?

Yes, by choosing a model on Azure OpenAI EU region or an on-prem deployment. PaLoMa AI can be deployed in EU cloud or on customer infrastructure. For high sensitivity scenarios (healthcare, defence, banking) the on-prem deploy + open source on-prem model combo is the recommended choice.

What does "MCP custom on demand" mean?

Model Context Protocol is the 2025 standard for connecting LLMs to external systems. If your business application has an API (any technology: REST, SOAP, GraphQL, direct database), we develop the MCP server that exposes it to PaLoMa. Your internal team writes no integration code.

Does PaLoMa AI replace ChatGPT in the company?

It serves a different purpose. ChatGPT is a generic public-web assistant. PaLoMa AI is a platform to build business assistants that know your documents, speak with your systems, respect your permissions. They are complementary: ChatGPT for generic tasks, PaLoMa AI for business processes.

How much does PaLoMa AI cost?

Pricing depends on the number of workspaces, active users, custom MCP integrations needed. Contact us for a tailored proposal: in the first call we analyse your case and provide a range within 48h.

Can I see it before committing?

Yes, we organise a 30-minute live demo on your real use case. No commitment, no commercial pitch. Demo + technical Q&A + concrete feasibility assessment. If the match isn't there, we'll tell you.

Let's meet for 30 minutes.
We'll show you PaLoMa AI on your use case.

No commercial pitch. Live demo, technical questions, concrete feasibility assessment on your scenario. If the match isn't there, we'll tell you.

Request a demo

Or write directly to paloma@fitec.it

Build AI agents on your systems. In days, not months.