Solutions — AI assistants & automationSolutions that work.

Not slides but building blocks we have built many times — tailored to your data, processes and systems. Each solution works on its own or combined.

S1 Abstract neural network symbolising artificial intelligence

Company knowledge assistant

The problem

Knowledge lives in PDFs, wikis, emails and heads. New staff keep asking the same colleagues.

Our solution

A chat assistant with RAG over all approved sources, per-document permission checks and answers with citations.

PythonVector DBLLMReactSSO
  • Answers with evidence
  • Permissions preserved
  • Runs locally or in the EU
S2 Close-up of glowing red optical fibres

Intelligent inbox

The problem

Hundreds of emails, forms and PDFs a day are read, sorted and retyped by hand.

Our solution

AI classifies incoming items, extracts data, creates records and drafts replies — uncertain cases go to a human.

LLMOCRNode.jsRESTT-SQL
  • Consistent, validated data
  • Every decision logged
  • Humans stay in control
S3 Historic telephone switchboard — today VoIP and Asterisk

AI phone assistant

The problem

The hotline is overloaded at peak times, requests get lost, notes are missing in the CRM.

Our solution

An Asterisk system with speech recognition and a language model: capture requests, answer standard questions, summarise, route.

AsteriskLuaSpeech-to-TextLLMWebSocket
  • No call without a note
  • Prioritised call-backs
  • Seamless hand-over to staff
S4 Hands using a mobile app on a smartphone

Offline-capable field-service app

The problem

Technicians work on paper, photos get lost in messengers, reports are written in the office at night.

Our solution

A native or Flutter app with offline database, photo documentation, signatures and sync as soon as there is signal.

FlutterKotlinSwiftSQLiteREST
  • Report done on site
  • No lost photos
  • Data straight to the back office
S5 Close-up of a microchip on a dark circuit board

Route & shift optimisation

The problem

Planning happens in spreadsheets, takes hours and covers only part of the constraints.

Our solution

An optimiser based on genetic algorithms that handles time windows, qualifications and capacities at once.

PythonGenetic AlgorithmsGoogle Maps APIsVue.js
  • Plans in minutes
  • All rules respected
  • Explainable proposals
S6 Data-centre aisle with server racks

Private in-house AI platform

The problem

Staff use private AI accounts with company data — a privacy and security risk.

Our solution

A GPU server with local language models, internal chat interface, user management, logging and an API for your own tools.

llama.cppOllamaLinuxDockerSSO
  • Company data stays in-house
  • Predictable cost
  • One interface for everyone

Note: the solutions shown are typical patterns from our work, not references of individual clients.

Delivery

From pattern to solution.

  1. 01Workshop
  2. 02Check data
  3. 03Prototype
  4. 04Pilot
  5. 05Rollout
1–2 weeks

Workshop & data check

Define goal, sources and success criteria.

2–6 weeks

Prototype

A working core with real data.

4–8 weeks

Pilot

Use with one team, measure, refine.

ongoing

Rollout & operation

Expansion, monitoring, evolution.

Durations are typical guidelines and depend on scope.

Ready when you are

Let’s talk.

Describe your project in a few sentences — you’ll get an honest assessment of feasibility, effort and the way forward.

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