Failed DIY
You built an AI pilot with n8n, Make, or a freelancer. It demos. It never shipped.

We build custom AI agents, build working AI strategies, and AI automation for any company across any industry. From first use case to full adoption, in just weeks.





Most teams recognise at least one of these. Here is where AI quietly starts to pay off.
You built an AI pilot with n8n, Make, or a freelancer. It demos. It never shipped.
Revenue is up, hiring can't keep pace. You need more output, not more headcount.
Ops or sales people spending 25+ hours a week moving data between systems by hand.
Five AI subscriptions, nothing measurable to show for them. High adoption, low results.
GDPR questions on the table, EU AI Act on the calendar, ungoverned tools running internally.
A bloated SaaS bill is up for renewal. A custom agent would cost a fraction of it.

concept → production
Owned by your team
Full source + docs handed over.
AI isn't magic, it's engineering. We build systems that deliver measurable results.
We're built to be the 5%. Every agent we build runs in production.
From scoping your biggest win to a live agent owned by your team, in eight weeks.
Full source, docs, and a trained team at handover. No lock-in, no black box.
Trusted by teams shipping AI in production
Built for any industry across any region. Five ways to work with us, matched to your ambition and budget.
Custom agents that take real work off your team, scoped, built, and shipped to production with you owning the result.
▋We connect your tools and remove the copy-paste tax, automating the workflows that quietly eat hours every week.
128 items / min · 94% auto-resolved
6% escalated · avg 0.6s per step
Connected · Hubspot · Snowflake · Slack
Bespoke AI software built like real engineering, tested, maintainable, and made to last beyond the demo.
# ops agent, graph w/ handoff
from wn.agents import Graph, Memory
g = Graph(state=OpsState, llm="sonnet")
g.add("intake", ingest, tools=[Zendesk])
g.add("retrieve", lookup, memory=Memory.vector)
g.add("reason", classify, retries=2)
g.add("handoff", page_human, when=conf < 0.82)
await g.run(on="new_ticket")A clear, prioritised plan for where AI pays off first, grounded in your operations and your numbers.
Get your data ready for AI, structured, governed, and compliant by design so every agent has solid ground to stand on.
A look at the agents and automations we have shipped, and the numbers they moved.
cheaper than manual monitoring
per week returned on manual work
A clear, four-stage path with no surprises, you always know where the work stands.
We dig into your operations to find the win worth building first.
We design and prototype the agent around your real workflows.
We build it like engineering, tested, integrated, production-grade.
We ship it live and hand it over, owned and run by your team.
With What's Next we help you turn questions on AI into reality.
Six weeks, that's how long it took Anthropic to ship Opus 4.8 after 4.7 landed.
Read →of AI pilots fail to ship. We write about being the 5%.
What's Next moved us from endless AI workshops to a live agent in production in under three months. They were the partner who actually shipped, and stayed to make it stick.