From Idea to Assistant in Production

Artificial intelligence has stopped being a lab experiment and become another layer of enterprise applications. We treat it as exactly that: with the same architecture, security, testing and maintainability standards we apply to any system expected to run for years. We design and implement AI solutions grounded in your own data, integrated with your systems, and deployed wherever you decide.

Assistants Grounded in Your Own Data

A general-purpose language model knows nothing about your products, your policies or your procedures. We build assistants on Retrieval Augmented Generation (RAG) architectures: we index your documentation, knowledge bases and operational data, and the assistant answers from that information, citing where it came from.

• Ingestion and indexing of documents, manuals, catalogs and databases.
• Semantic search over vector stores, tuned for your domain.
• Traceable answers, with references back to the source that backs them.
• Explicit control over what the assistant may and may not answer.

On the Channels Your Customers Already Use

A good assistant is not much use if people have to log into a portal to reach it. We take it to the channels your customers already have open on their phones.

• WhatsApp, Instagram and Facebook Messenger, through each platform's official integrations.
• Embedded chat on your website or inside your own application.
• Hand-off to people: when a conversation calls for it, the assistant passes it to a human agent without losing context.
• Unified history, usage metrics and an audit trail for every conversation.

From Answering to Resolving

What separates a chatbot from a useful assistant is what it can do, not what it can say. We connect the assistant to your systems so it can check an order's status, book a visit, log a claim or kick off an internal process, always within the permissions you define.

We design the tools the model is allowed to call, the limits on each one, and the validations that run before anything is written back to your systems. AI proposes; your business rules still decide.

AI Inside Our Own Development Process

We use AI every day to build software, not just to talk about it. Coding assistants, automated code review, test generation and AI-assisted migrations are part of our regular workflow.

The effect is measurable: work that used to take a team weeks now lands in days, with broader test coverage and sharper reviews. For you that means fewer billed hours for the same scope and shorter delivery cycles. Our blog documents concrete cases, including the migration of an eight-year-old application across major framework versions.

How We Approach an AI Project

1. Use case before technology. We work out together where there is a real problem with a measurable outcome, and drop whatever sounds good but moves nothing.
2. A narrow proof of concept. Small scope, your real data, to validate feasibility and answer quality before you commit a budget.
3. Production to the usual standards. Observability, per-conversation cost control, continuous quality evaluation and a maintenance plan.

Privacy, Cost and Control

We work with multiple model providers and with open-weight models running on your own infrastructure, depending on what your data and your regulations demand. The design does not lock you to a single vendor: the integration layer lets you switch models without rewriting the application. And because the cost of every conversation matters, we measure and optimize it from day one.

Our AI Stack

• Java integration: Spring AI, LangChain4j, Spring Boot
• Models: commercial providers and self-hosted open-weight models
• Retrieval: vector stores, hybrid search, reranking
• Channels: WhatsApp Business and Meta APIs, embeddable web chat
• Deployment: Docker, AWS, Cloudflare or your own infrastructure