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AI & Automation

LLM tooling, document workflows, RAG, and process automation that actually ships.

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The problem

Most 'AI projects' fail because they start from the model and not the workflow. A useful AI deployment looks like fewer screens, fewer copy-pastes, fewer phone calls — not a chatbot bolted onto a homepage.

How we approach it

  1. 01Pick one painful, high-volume workflow. Instrument it. Then automate.
  2. 02Use retrieval-augmented generation against your actual documents, not generic models.
  3. 03Keep humans in the loop where the cost of a wrong answer is higher than the cost of a slow one.
  4. 04Measure deflection, throughput, and error rate — not vibes.

Capabilities

  • Document intake, classification, and extraction
  • RAG over internal knowledge bases and procedures
  • Workflow automation across CRM, email, Slack, Teams
  • Agent-assist for support, sales, and operations
  • Voice and call center automation (English, Swahili, French, Arabic, Portuguese)
Stack
  • Lovable AI Gateway for model access (Gemini 2.5, GPT-5)
  • Vector search with pgvector or Weaviate
  • LangGraph / temporal-style orchestration where state matters
  • Evaluation harness with golden-set regression tests
Deliverables
  • Automation deployed against a measured baseline
  • Eval suite that catches regressions before customers do
  • Cost ceilings and circuit breakers per workflow
  • Documentation your ops team can actually read
Fit for
  • — Operations with a known repetitive workflow above 500 events/month
  • — Support teams drowning in Tier-1 tickets
  • — Document-heavy industries (legal, insurance, logistics, finance)
Not for
  • — 'Let's add AI to our app' with no workflow named
  • — Teams that won't share real data for evaluation

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