Service 03

Artificial intelligence for business: from the endless pilot to AI that actually works.

We implement artificial intelligence in your operation: language models, RAG systems over your documentation, and agents that execute real tasks. With evaluation and controls from day one, not as an afterthought.

Talk to us about your case See how we deliver it

Artificial intelligence or automation: which do you need

AI Adoption

We make possible what you can't do today: reading thousands of documents, understanding natural-language requests, reasoning about cases that don't fit a rule.

Automation

If the task has clear rules and a predictable outcome, you don't need AI: you need Process Automation, which is faster and cheaper.

Does your business need artificial intelligence?

  • You have an identified use case and need to take it to production, not run another pilot.
  • Your critical knowledge lives in documents, contracts and manuals no one has time to read.
  • You receive natural-language requests (email, WhatsApp, tickets) and classifying them consumes your team.
  • You tried a generic AI tool and it didn't work because it doesn't know your business.
  • You need the AI to meet your privacy, traceability and control requirements.

What artificial intelligence implementation includes

Language models applied to your process

LLMs integrated where they generate value: classifying requests, extracting data from documents, drafting responses, summarizing case files. Connected to your systems, not in a separate tab.

RAG over your documentation

A knowledge base built on your policies, contracts and manuals, so every answer is grounded in one of your documents that can be cited. Less invention, more traceability.

Agents that execute, not just respond

Agents with bounded permissions that query systems, execute steps, and escalate to a person when the case warrants it. With the level of autonomy defined with you by task type.

Knowledge graphs

When what matters isn't the data itself but how it relates (customers, contracts, assets, case files), we model those relationships in a graph so the AI can reason over them.

Evaluation and guardrails

A set of real cases that measures answer quality and runs on every change, plus the boundaries of what the system can and can't do. Without this, you don't know if you improved or got worse.

Deployment, observability and handoff

Production rollout with cost, latency and quality monitoring, technical documentation and training for your team. Everything stays auditable: what it answered, with what source, and how much it cost.

21-day sprint

How we implement artificial intelligence in 21 days

  1. 01

    Use case specification (days 1-5)

    Before building, we write the specification: what the system needs to achieve, with what data, within what limits, and how we'll know it works. We work with specification-driven development, so that document is the sprint's technical contract.

  2. 02

    Data, architecture and evaluation set (days 6-10)

    We prepare the knowledge sources, define the architecture (model, retrieval, orchestration, where the data lives) and build the set of real cases quality will be measured against.

  3. 03

    Build and measured iteration (days 11-17)

    We build the system and iterate against the evaluation, not against impressions. Every adjustment is accepted only if the number improves. We integrate with your systems and configure permissions and human escalation.

  4. 04

    Production and handoff (days 18-21)

    It goes live with cost, latency and quality observability. We deliver technical documentation, the evaluation set, and training so your team can operate and extend it.

Artificial intelligence use cases by industry

We've implemented AI for businesses in sectors like healthcare, hospitality, accessibility and logistics, among others. What changes between industries is the vocabulary; what stays constant is the need for the system to be auditable.

Typical result

We automate at least 30% of the process from the first sprint, with AI in production and measured.

Not a pretty demo for the committee: a system connected to your operation, with its own evaluation set and its own cost-and-quality dashboard.

Frequently asked questions about artificial intelligence for business

How is this different from a chatbot?
A chatbot answers questions. An agent executes tasks: it queries your systems, makes bounded decisions, writes to your databases and escalates to a person when appropriate. The practical difference is that a chatbot tells you how to complete a process, and an agent does it. We build both, but the value is almost always in the second.
Will our data be used to train public models?
No, unless you explicitly decide to. We work with enterprise plans where the provider does not train on your data, and when sensitivity requires it, we deploy open-source models on your own infrastructure or a private cloud. Where your data lives is decided with you before we write a single line of code.
What is RAG and why do we need it?
RAG (retrieval-augmented generation) is the technique that makes a language model answer using your documents instead of its general knowledge. Without RAG, the model doesn't know your policies, contracts or manuals, and it makes things up. With RAG, every answer is grounded in one of your documents that can be cited and audited.
What happens if the AI gets it wrong?
It will get things wrong, which is why the design assumes that from the start. We define the level of autonomy per task type with you, add human validation where the cost of error is high, and build an evaluation set of real cases that runs on every change. There's also observability to review what it answered, with what source, and why. You can read our full stance in AI Ethics.
What models and technologies do you work with?
Claude, GPT, Gemini and open-source models, depending on what the case demands in cost, latency, privacy and quality. We're not a reseller for any provider, so the choice is justified on technical criteria and documented so you can change it later.

Have a use case and want to see it in production?

Tell us what it is. We'll respond within 24 business hours with a concrete proposal.

Let's talk

Other services

Consulting, process automation and custom software

Digital Transformation Consulting

What to change, in what order and with what return. Roadmap in 2 weeks.

Process Automation and Continuous Improvement

We run the repetitive work for you and measure the result.

Custom Software

Web, apps, ERP, CRM and integrations built for your operation.