AI agents that take work off you — not just answer questions.
We build AI agents that reliably take over tasks in sales, support and operations — with eval suites, monitoring and staged roll-out from suggest mode to full automation.
An AI agent isn't a better chatbot. It's a system that decomposes a goal, picks tools, executes actions and evaluates intermediate results — with a language model as the control plane. The key difference: agents **act**. They send emails, update CRMs, call APIs, qualify leads.
Three out of four SMB AI-agent projects fail — not because of technology, but because of use-case choice, data foundation or change management. We build setups that systematically avoid those traps: a small, clearly bounded use case first, a clean data foundation, an eval suite from day one, and a staged roll-out.
Our stack is provider-agnostic: OpenAI Agents SDK, Anthropic Agent SDK, LangGraph, n8n, Make — we pick what fits your use case and existing infra. Privacy, data residency and the EU AI Act are architecture decisions, not afterthoughts.
Why AI agents matter for SMBs in 2026
SMBs have a structural capacity gap: too much repetitive work, too few people. Classic automation (Zapier, Make) solves linear workflows. AI agents solve work that requires language understanding, classification and uncertain decisions. A well-built agent can take over 60–80 % of tier-1 support or lead qualification — without service quality dropping.
Start small, build clean, then scale.
Find the right use case
In a 90-minute workshop we find the one task in your day-to-day that burns the most time and is clearly defined — typical examples: sorting incoming requests, scheduling meetings, qualifying leads. Sounds unglamorous. Decides whether the project makes money in the end.
Data check & prep
Before we build anything, we audit your data: where is it, who's allowed to access it, is it current? This is the invisible bulk of the work — and the actual reason four out of five AI projects fail. Get this right and the rest runs.
Test agent on real cases
Before any customer talks to the agent, we run it against 30–80 real cases from your business. We see exactly where it lands and where it doesn't — and we fix it before anyone outside notices.
Human approves, AI suggests
First live phase: the agent makes a suggestion, your team reviews and clicks approve — like a junior who needs sign-off on every step. We measure weekly how often the suggestion is right.
Full auto with a safety net
Once the agent is right in over 85 % of cases, it takes over by itself. Complex or unclear cases get passed to a human — like a good employee who knows when to ask. Trust is built on numbers, not hope.
A productive agent — not a demo video.
Use case definition
Clearly bounded scope with success criteria, KPIs and escalation paths.
Custom agent (prototype + production)
Implemented on your stack, connected to your CRM/ERP/knowledge base.
Eval suite
30–80 test cases, automatically runnable, with pass/fail reporting on every model or prompt change.
Monitoring & alerts
Live logs, per-request cost tracking, anomaly detection, alerts on error rates or unexpected behaviour.
Compliance documentation
GDPR assessment, EU AI Act classification, DPA with model provider.
Handover & training
Your operators know how to run, maintain and pause the agent in case of incidents.
Three numbers that make ROI visible.
Tier-1 load automated
Median across 6 months live.
Lead response time
Vs. 4–24 hours manual.
Eval acceptance before auto
Threshold for full automation.
What we build with.
What people ask most.
When is an AI agent worth it vs. simpler automation?
When the task requires language understanding, classification or context-dependent decisions. Pure workflow steps are often cheaper and more stable on n8n or Make. Agents come in when if-then logic isn't enough anymore.
What does a first agent use case cost?
Fixed price after the use-case workshop. Build and ongoing operations are separate line items — calculated project-specifically and shared transparently before quoting.
Which model providers do you recommend?
We're agnostic. For reasoning-heavy tasks often Claude (Sonnet/Opus). For cheap high-volume often GPT-5-mini or Gemini Flash. For strict data setups, open-source models (Llama, Mistral) on your own infra.
What happens to our data?
We use API endpoints with no-train policies (OpenAI Enterprise, Anthropic Workbench, Google Cloud) or self-hosted models exclusively. Your data doesn't enter model training. DPAs are standard.
How do you prevent hallucinations?
Three levers: tight use-case definition (models hallucinate on broad tasks), tool calls instead of free-text (models pull facts only via defined APIs), and eval suites that actively measure hallucination rate.
How long does an agent project take?
From briefing to suggest mode typically 6–10 weeks. To stable full automation another 4–8 weeks. Faster cycles are possible, but usually at the cost of eval quality.
Ready to become visible?
30-min intro call to scope the use case and check if it's agent-ready.