AI integration: what your company knows, one question away.

An assistant that answers from your manuals, contracts and price lists, and cites the exact paragraph for each fact.

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Thousands of pages, no searching.

It reads your documents and answers from what they say.

Every answer, with its source.

If it cannot find it, it says so and alerts a person.

Fewer interruptions for your experts.

AI integration with your data from €6,000.

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Answers from your own documents

Someone on the team asks in their own words and the assistant searches the terms of sale, the product manual and the price list. The answer says where each fact comes from.

  • Questions in plain language
  • Only your documents as sources
  • Every fact with its citation
Assistant

What return period does a distributor have for a defective product?

A distributor has 30 calendar days from delivery to report a defective product 1. They must request an RMA number and attach the delivery note and photos of the defect; if it is confirmed, EmpresaM S.L. pays the return shipping 2. After that period, the 24-month manufacturer warranty applies 1.

1 Condiciones generales 2026.pdf p. 14 · § 7.2
2 Manual de producto v4.pdf p. 62 · § 11.2
  • Terms of sale The 30 days and the 24-month warranty both come from § 7.2.
  • Product manual The RMA procedure is in a different document, and it is cited too.

And shows you the source paragraph

Each citation opens the original document at the cited page, with the paragraph highlighted. Whoever asked can check the fact without hunting for it.

Condiciones generales 2026.pdf p. 14 of 48
§ 7.1 Ordering error: 14 days
§ 7.2 Defective product: 30 calendar days, then a 24-month warranty Citation 1
§ 7.3 Made-to-order material: no returns
  • Highlighted paragraph This is the text behind the answer, so it can be checked against the original.

And says when it does not know

If the answer is not in your documents, the assistant says so instead of making it up. The question is logged and the team decides which source is missing.

Question log Access: Sales team
09:12 Question: deliveries to Portugal Question
09:12 3 documents searched · no matches No source
09:12 Answer: I cannot find this in the documents No guessing
09:12 Flagged for review Review
11:05 Reviewed: the information is missing from the sources Reviewed

One question, answered with its source

A sales rep asks about a distributor’s return period. The assistant searches the company’s documents and answers, citing where each fact comes from.

  1. Someone on the team types the question in plain language.
  2. The assistant searches the terms and conditions and the product manual.
  3. It answers in seconds: 30 calendar days, with an RMA number, the delivery note and photos.
  4. Each citation points to the exact paragraph of the original document, highlighted.

Something like this for your company?

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What changes day to day

Today

  • You know AI could help but are not sure where to start
  • Generic AI tools do not know your business or your data
  • You worry about accuracy, privacy and unpredictable costs
  • Pilots look good in a demo but never reach production

Once it is running

  • AI that answers from your own documents and data, with sources
  • Use cases chosen for the time they can save your team
  • Quality, privacy and cost controls built in from day one
  • Built to reach production and be used day to day

What is included

The right AI provider for you

OpenAI, Anthropic Claude, Google Gemini or open-source models. We help you choose the provider that fits your needs, budget and privacy requirements, and we integrate it properly.

AI that knows your data (RAG)

We connect the AI to your own documents, databases and knowledge base so it answers from your real information and cites where it got it.

AI agents that get things done

Agents can query databases, fill in forms, call other services and complete tasks that take several steps.

Connected to your tools

We give the AI controlled access to the tools it needs (CRM, calendar, databases, APIs) so it can make changes in your systems, such as creating a contact or booking an appointment.

Quality you can measure

Every integration includes monitoring and evaluations. You know how it performs, you see problems early and improvements are decided with data.

Adapted to your business

When a general model is not precise enough, we adapt it with your examples, terminology and, where it pays off, fine-tuning, so it uses your terms and knows your products.

Typical uses

  • E-commerce and catalogue

    Natural-language search

    Users type a question in plain language and find what they need in your product catalogue, knowledge base or internal documents.

  • Legal and operations

    Questions about your documents

    Contracts, manuals or reports: ask questions in plain language and get answers that point to the exact source, so they can be checked.

  • SaaS and software

    AI assistant inside your product

    An assistant built into your app that knows your business and helps your users with common questions without going through support.

  • Marketing and support

    Drafts of content and replies

    Product descriptions, email replies, reports or support responses drafted in your brand voice, ready for a person to review and publish.

How we keep risk under control

We connect the AI to your own data, with sources and safeguards, so every answer can be checked.

  • Answers with sources

    Every answer cites the document it comes from. When the information is not in your sources, the assistant is set up to say it does not know instead of inventing it.

  • Human review and feedback

    Your team can flag wrong answers; those cases are reviewed and used to improve sources, instructions and tests.

  • Quality and cost monitoring

    We log questions, answers and usage, with quality evaluations and spending limits, so any drift in accuracy or spending is caught early.

  • Data, GDPR and EU options

    Providers with data processing agreements, EU processing, no training on your data and, if needed, models hosted on your own infrastructure.

How we work

The timeline depends on scope and is set in writing in the proposal.

  1. Find the right opportunity

    We look at your business and find where AI saves time or improves quality in a way you can measure. If a use case only looks good in a presentation, we drop it.

  2. Prepare your data

    For AI to be useful, your information has to be in good shape. We organise, clean and connect your documents and data.

  3. Choose the provider and build

    We select the most suitable provider for your use case, build the integration and test it against real scenarios until the quality meets the bar agreed with you.

  4. Launch and keep improving

    We deploy to production, monitor performance from day one and keep refining it with real usage data.

From €6,000

Indicative price for a typical project, VAT not included. Fixed written quote after the first call.

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What a typical project includes

  • Initial analysis and use-case definition
  • RAG over your documents and data
  • AI assistants and agents connected to your tools
  • Quality and cost monitoring
  • Production-ready, documented and handed over

Usual stack: OpenAI, Anthropic Claude, Google Gemini, LangChain, LlamaIndex, Pinecone, pgvector, Python, Node.js, Redis, Docker, AWS.

Frequently asked questions

Which AI provider should we use?

It depends on your needs. OpenAI, Anthropic Claude and Google Gemini differ in reasoning, cost, speed and data policies. We compare them for your case and choose the right fit, or combine more than one.

Will the AI make things up or give wrong answers?

It is a real risk: it can be greatly reduced, but not removed entirely. By grounding the AI in your own data with RAG, citing sources, letting it say "I don’t know" and adding human review where it matters, every answer rests on your documents and can be verified.

What happens to our data? Is it GDPR-compliant?

We work with providers that offer data processing agreements (DPAs), processing within the EU and a contractual commitment not to train their models on your data. We send only the information each query needs and, where full control is required, we can deploy open-source models on your own infrastructure.

How much does it cost to run AI in production?

It depends on usage. We design integrations to spend little: caching repeated requests, routing simple queries to cheaper models and setting spending limits. That keeps the monthly cost bounded and easier to forecast.

How quickly can we have something working?

It depends on the scope and on how ready your data is, so we set the timeline in the written proposal, with milestones. We start with a well-defined case so you can see it working on your own data early.

Do we need our data perfectly organised first?

No. Part of the project is preparing it: we review what you have, decide which sources are reliable and structure them so the AI can use them. We tell you up front if something needs cleaning first.

Where would AI save your team the most time?

Tell us what you would like AI to do. We will tell you in writing whether it makes sense, which provider fits, what controls it needs and what it would cost.

  • Fixed written quote
  • One person accountable
  • Milestone-based payments
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