Solutions / Agentic AI
An agent is worth building when a task takes many steps across several systems and a person still owns the outcome.
These scenarios are illustrations. None of them describes a client.
Banking and finance
What we would build
An agent that gathers the client file from each system, checks expiry dates and missing items, and drafts the review summary with its sources. The analyst signs off on the complete file, and the agent has no permission to contact clients or approve anything.
LangGraph, Python, PostgreSQL, Avaloq API
Watchmaking and luxury
What we would build
An agent that watches selected marketplaces, matches listings to references from their photos and descriptions, and checks any visible serial or warranty card against authorised-dealer allocations. It builds a case file for each suspicious listing, and the team decides what to pursue.
Playwright, DINOv2, Python, PostgreSQL
Pharma and life sciences
What we would build
A review agent that reads scanned batch records, compares each step with the master record and lists deviations, missing signatures and out-of-range entries with their page. The reviewer works through that list, and release stays with the person who certifies batches.
Qwen3-VL, LangGraph, Python
Pharma and life sciences
What we would build
A literature agent that queries PubMed every week for each team's topics, screens abstracts against their criteria and sends a short digest with links and the reason each paper was kept. Scientists rate the picks, which tunes the next week's screening.
PubMed API, BGE-M3, LangGraph, Microsoft Teams
Retail and e-commerce
What we would build
A service agent that reads each ticket, looks up the order and the return policy, and drafts the reply in the customer's language. Routine answers go out after a quick check; refunds and exceptions wait for a person, with the case already summarised.
LangGraph, Zendesk API, FAISS
An agent is a language model given tools: it can search documents, call your APIs, fill forms and decide which step comes next. We build agents as explicit workflows with fixed permissions, so every action is logged and stays within what the workflow allows, and the decisions that matter go to a person.
It can only use the tools and permissions we give it, and actions with consequences wait for human approval. Every step is logged with its inputs and outputs.
Usually LangGraph or plain Python state machines, with open-weight or hosted models depending on your data. We connect tools through the Model Context Protocol (MCP) when it suits your systems, and the workflow logic stays in code you own.
Yes. Open-weight models can run on your servers or with a Swiss provider, and the tools the agent calls stay inside your network.
A focused prototype on one workflow usually takes a few weeks. Production adds integration, monitoring and the approval steps your process requires.
We start with a feasibility study on your own data, so you see results before anything is promised.