Agentic AI & Knowledge Systems
Agentic AI & Knowledge Systems
We build intelligent agents, RAG systems on internal documents, and AI assistants integrated into real workflows. Not generic chatbots.
The typical problem
Agentic AI & Knowledge Systems
We build intelligent agents, RAG systems on internal documents, and AI assistants integrated into real workflows. Not generic chatbots.
What we build
We build intelligent agents, RAG systems on internal documents, and AI assistants integrated into real workflows. Not generic chatbots.
What we deliver
- AI agent on internal knowledge base
- RAG system on contracts and technical docs
- Workflow automation with LLM
Faster answers, always-available context, less time wasted searching for information.
A clear method, from analysis to adoption.
We don't start with code. We start with context, data, and the people who will use the system.
- 1
Understand context
We listen to goals, constraints, existing tools, and real friction points.
- 2
Map data and processes
We reconstruct flows, sources, manual steps, and blind spots.
- 3
Design the solution
We design architecture, logic, and interfaces before building.
- 4
Build and integrate
We develop incrementally, connecting what already exists with what is missing.
- 5
Add intelligence
We introduce automation, BI, or AI where they deliver real utility.
- 6
Enable adoption
Documentation, onboarding, and support to make sure the system is truly used.
- 7
Evolve over time
We measure, collect feedback, and improve without starting from scratch.
AI agents on documents and data sources
- Problem
- Critical procedures and technical references were hard to search, slowing daily execution.
- What changed
- We deployed retrieval-augmented AI assistants grounded on internal documents and structured sources.
- Why it matters
- People recover context in minutes, reduce mistakes, and keep decisions anchored to verified information.
Frequently asked questions.
What technologies do you work with?
It depends on the project. We are not tied to one stack. We use TypeScript, Angular, Node.js, Python, SQL, BI tools like Power BI and Metabase, LLMs like OpenAI and Anthropic, and the main cloud platforms. We choose what works.
How long does a typical project last?
It varies widely. A targeted integration can take 4–8 weeks. A full CRM system or AI platform 3–9 months. We always define scope and timelines before signing.
Do you work remotely or on-site?
We work remotely by default, with structured video alignments. We are available for on-site sessions for kickoffs, analysis workshops, or final reviews.
Can I also rely on you for post-launch support?
Yes. We offer evolutionary maintenance contracts and ongoing support. The software we deliver is documented and maintainable, not a black box.
How does a collaboration begin?
With a discovery call. No generic commercial presentations: we talk about your context, your real problems, and how we could help. If it makes sense, we build a proposal.
How do you define a proposal?
After one or more analysis conversations, we produce a document with scope, deliverables, timeline, and costs. No generic quotes: every proposal reflects the specific project context.
Can we start with a workshop or a short phase?
Yes. If the context is not yet clear, we can start with an analysis workshop or a short standalone discovery phase, before committing to a larger project.