We integrate AI into your go-to-market teams, from lead scoring and personalization to agentic workflows that run tasks autonomously. Practical, measurable, and built into your existing stack.
We build AI steps that run in your daily process and keep working after the demo:
For teams losing time to repetitive work that want to deploy AI with control, measurable quality, and managed data risk.
For the workflow layer we often use n8n, for enrichment Clay. For meeting intelligence and scorecards see AI notetaker.
With repetitive work before and after customer contact: researching accounts, enriching and scoring leads, summarizing call notes, and preparing follow-ups. That is where the gain is fastest and the risk smallest.
No. Good AI use removes the manual work, research, enrichment, first drafts, so people have more time for real conversations.
We are model-agnostic: LLMs like Claude and OpenAI models, n8n for workflow orchestration, Clay for data enrichment, and HubSpot as the source of truth. We choose models and tools per use case.
We design workflows to be data-sparing: as little personal data as possible sent to models, EU hosting where possible, and clear logging of what goes where. We discuss that explicitly in the design.
Share briefly where time leaks in your team, I am happy to think along about where AI makes the difference.
matthias@tech-stack.nl