AI where it saves hours, not where it shows.
Agents, RAG over your documents and process automation with guardrails and metrics.
Measure the process first, automate second. If the pilot does not move the metric we agreed on, it gets dropped — not dressed up with a nice demo.
Almost every AI project that fails started with the hardest case and no baseline. Here we pick three processes by return, measure what they cost today, and attack the easiest one with the highest volume first.
What it includes
- Assistants with RAG wired to your knowledge base, with source citations.
- Agents that execute tasks: read email, extract data and update your CRM.
- Internal copilots for support, sales or back office.
- Data extraction from PDFs, invoices, contracts and forms.
- Automatic classification of tickets and email.
- Semantic search across internal documentation.
- Guardrails: spend limits, content filters and escalation to a human.
- Observability: full logging, periodic evaluations and cost control.
How we work
- Opportunity audit. We identify 3–5 processes where AI saves measurable hours.
- Prioritisation by return. The fastest payback goes first as the pilot.
- Pilot in 2–4 weeks. A working proof of concept with before-and-after metrics.
- Production integration. Connected to your systems: WhatsApp, CRM, ERP or database.
- Guardrails and observability. Logging, spend limits and continuous evaluation.
- Phased roll-out. Start with one team, expand on adoption data.
Usual stack
Claude · GPT · LangChain · pgvector · Qdrant · n8n · Python
Frequent questions
- Are my data exposed?
- We work with enterprise providers that do not train on your data. When the requirement is zero cloud, we run open-source models on your own infrastructure.
- How much does an AI project cost?
- An automation starts at 1,200 USD. Inside another project, the assistant is added from 600 USD. Token usage is separate, typically 100–800 USD a month depending on volume.
- Will AI replace my team?
- No, and it should not. It removes repetitive work — frequent answers, classification, extraction — so people can focus on hard decisions and on customers.
- What about hallucinations?
- They are handled with RAG (answers anchored to your sources), mandatory citations, validation guardrails, automated evaluations and confidence thresholds. When the system is unsure, it escalates to a person.
- How long until it pays off?
- For well-chosen cases — repetitive answers, extraction, classification — 4 to 8 weeks. For complex cases with multiple integrations, 3 to 6 months.