It finishes the sentence.
That is all an AI model does. It reads the words so far, picks a likely next one, and goes again. Press the button and watch it write.
The invoice is due on
It never looked at an invoice. It wrote a likely ending.
A scripted example. The odds are ours, to show the idea.
Sounding right is not being right.
A customer asks about a refund. See what the model says on its own, then switch on what we build around it.
The model on its own
CustomerWas my refund processed?
Yes. Your refund of €240 was processed yesterday and will reach your account in three to five days.
Wrong. There is no refund.
Same model as the first answer. Only the engineering changed.
We don’t chase every new tool.
Most weeks something new promises to replace everything before it. We keep what holds up on real work. Two talks worth your team’s time, and where we stand on each.
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Do you still need a vector database?
Sometimes. We start with the simplest search that finds the right page, measure it on your questions, and add more only when the numbers ask for it.
Are Vector Databases Still Needed? RAG, AI Agents & Search · IBM Technology
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Skills, MCP, RAG or memory?
They are not rivals. Each answers a different question: what it knows, what it can reach, how it works, what it remembers. We choose by the job, not by the trend.
Skills vs MCP vs RAG vs Memory: What AI Agents Need to Know · IBM Technology
What we can build for you.
Six things companies put AI to work on today. Each one is a model with the right pieces around it.
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AI assistants and chatbots
Answer staff and customers from your own documents, with the source shown on every answer.
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AI agents and workflow automation
Agents look things up, fill in the form and raise the ticket. A person approves the steps that matter.
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Intelligent document processing
Invoices, contracts and emails read, sorted and turned into records your systems accept.
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Conversational analytics
Talk to your data. Ask your warehouse a question in plain words, and see the query behind the answer.
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AI copilots in your product
Search, drafting and summaries built into the web and mobile apps your customers already use.
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LLM evaluation and observability
An eval suite built from your real cases and run on every change, a trace of every answer, and a known cost per task.
Hosted, or on your own servers with open models and vLLM. Fine-tuned only when the test set says it pays.
Everyone gets the same model. The build is what sets you apart.
Most companies find that out the hard way.
- 95%of generative AI pilots show no measurable profit-and-loss impactMIT NANDA, 2025
- 48%of AI agents in production run unmonitoredGravitee, 2026; PagerDuty, 2026
- 73%of organisations say AI costs exceeded their original planState of FinOps 2026; KPMG
Without the build around it, AI stays a demo.
That build is our job. Front end, data, back end and AI in one senior team, so the model is wired into your business and tested on your real cases before anyone relies on it. Then every line of it is yours.
Talk to the AI teamAgents, retrieval and evaluation that survive contact with real users and real data.
AI consultancy. Agents, agentic AI, retrieval-augmented generation, RAG, context engineering, tool use, MCP, evals, LLM evaluation, guardrails, human in the loop, observability, document processing, text-to-SQL, copilots, AI governance, EU AI Act, Claude, OpenAI, LangGraph, pgvector, vLLM, fine-tuning, inference cost, model operations, senior AI engineers, documentation, handover.
Services: Agent systems, Retrieval & knowledge, Evals & guardrails, Model operations.
Stack: Claude, OpenAI, LangGraph, pgvector, vLLM.