AI & LLM

Artificial intelligence in products and processes

We do not build demos, we build features that go to production and stay up. We start from a concrete use case, measure it and then extend it.

In your products

Features your users can see

AI becomes part of the software: an assistant inside the application, a search that understands questions, a function that prepares documents in place of whoever used to fill them in by hand.

  • Assistants and copilots inside the application
  • Search and analysis over documents and data
  • Content generation with human review

In your processes

Repetitive work that stops weighing you down

AI stays behind the scenes and lightens the activities that take up time today: administration, customer support, operations, data checks.

  • Sorting requests and documents
  • Extracting data from files and forms
  • Automations connected to existing systems

Our areas of work

We work with Claude, GPT and open source models. The choice of model comes after the choice of problem, and can change over time without rewriting everything.

LLM integration in products

Conversational assistants, natural language search, content generation and classification inside your applications.

  • Tailored assistants
  • Semantic search
  • Text generation and summarisation

Agents and automations

Agents that carry out repetitive tasks end to end: sorting requests, filling in documents, updating systems, drafting replies.

  • Automation of internal processes
  • Sorting and triage
  • Agents with tools and permissions

RAG on company knowledge

We connect the models to documents, manuals, contracts and internal data, so answers cite your sources and not the web's.

  • Document indexing
  • Answers with source citations
  • Continuous updating of the knowledge base

MCP and system connections

We give the models controlled access to the systems you already use, management systems, CRM, databases and external services, with explicit permissions.

  • Custom MCP servers
  • Access to internal data and tools
  • Traceability of operations

Consulting and adoption

We identify where AI brings a measurable benefit and where it does not pay off, with priorities, estimates and usage rules for the team.

  • Use case map
  • Cost and benefit estimates
  • Internal guidelines

Evaluation, quality and security

A model that answers is not enough: you need to know how often it answers well. We define tests, metrics and usage limits.

  • Test sets and evaluations
  • Cost control per request
  • Sensitive data and compliance

How we start

From use case to production

An AI project that starts too big almost always stalls. We prefer a short, verifiable path, with an explicit decision at every step.

  1. 1

    Use case assessment

    We look at the real processes and choose where AI brings a measurable benefit. Discarding the weak cases is part of the job.

  2. 2

    Measurable pilot

    We build a first, limited version, with metrics decided up front: answer quality, time saved, cost per request.

  3. 3

    Going to production

    Integration into systems, handling of permissions and sensitive data, cost control and monitoring of behaviour over time.

  • Your data stays yours: explicit technical and contractual choices about where information goes
  • No lock-in to a single vendor: the architecture allows you to change model
  • Predictable costs: we measure the cost per request before opening the service to everyone
  • Human oversight: where a mistake matters, the final decision stays with a person

Inside LuckySeven

Development with AI, human oversight

AI agents are part of the way we develop: they support us in writing and reviewing code, in testing and in documentation. We use them to lighten repetitive work and give more attention to design and quality. It is also why we know where these tools work and where they do not.

  • Prototypes to evaluate

    We use working prototypes to evaluate ideas and guide the choices that follow.

  • Code review

    Senior developers review the code and assess its quality and fit for the project.

  • First-hand experience

    Daily use helps us know the possibilities and the limits of the tools we propose.

Connect AI to our other services

An AI project rarely lives on its own. Around it there is the software that hosts it, the people who will use it and the technical choices to make together: three services we often run alongside the work on models.

Software development

The software AI goes into: new products to build or existing applications to extend and integrate.

Training

Courses on bringing AI into business processes and into developers' daily work.

Technical consulting

We support your team on architecture, technical choices and integrating models into existing software.

The next step? We take it together

Whether you want to build software from scratch or grow a project that is already under way, we want to understand what you are trying to achieve. You do not need to have everything figured out: tell us about your idea, your doubts and the parts still to be defined. We guide you, with the right questions and concrete proposals, to work out where to start and how to move forward.

Let's talk about your project →