AI Integration & Automation

Overview

We add language and data models to real workflows: document handling, search over your own content, assistants, classification and process automation. The goal is a feature that works reliably inside your product, with evaluation, fallbacks and review steps, instead of a demo that only works on good days.

What we do

• Search and question answering over your own documents and data
• Assistants and copilots inside existing applications
• Document extraction, classification and summarisation
• Workflow automation across internal systems
• Evaluation sets and quality checks for model output
• Cost, latency and data-privacy considerations

What we build with AI

• AI development

  Models built into products your team uses daily, on Python and FastAPI.

• Generative AI and LLM integration

  Claude, OpenAI and Gemini wired into real workflows with fallbacks.

• AI agents and workflow automation

  Chat driven tools like Furnways, our WhatsApp sales system.

• Recommendation and search

  Ranking, matching and search on Postgres, Elasticsearch and Solr.

• AI consulting

  A one to two week review of where AI fits your process, with a plan you own.

How we approach it

1. 1

   Pick a task you can measure

   We start with one task where success can be checked, not with a general ambition to use AI.

2. 2

   Prove it on real data

   A prototype is tested against your own examples, including the awkward ones, before anything is built around it.

3. 3

   Harden it for production

   Fallbacks, human review where it matters, logging and cost limits turn the prototype into a feature you can rely on.