Integrations & APIs
Your systems don't share data. Orders fall through.
We connect your CRM, billing, warehouse, and support tools so information moves without a human copying it between tabs.
What we do · AI
Automation that ships.
Your team copies data between systems. Your tools do not talk to each other. AI pilots stall before reaching customers. We fix all three, with one delivery team from brief to launch.
−38%
Typical drop in manual support tickets after intake automation ships
10d
Typical window from signed scope to first automation running in production
Named
Lead on every engagement, from brief to launch
How we work
Tell us what is eating your week.
We start with the boring parts. Data moving between systems, triggers that actually fire, and a named person when something falls outside the rules. No new platform to learn. We work inside what you already have.
Most AI projects look fine in a demo. They break when real volume shows up, or when an edge case nobody thought about lands in an inbox nobody owns. We build for week three, not presentation day.
Usually it is the same few places: sales ops updating the CRM by hand, support answering repeat questions, finance pulling numbers from five spreadsheets, marketing waiting on IT for a connection. If that sounds like your Monday, we have done this before.
Three ways we typically tackle it: Integrations & APIs to get systems talking, workflow automation for the repeat tasks, and AI inside existing tools where your team already works.
The full AI picture sits on the overview page. WordPress-specific work is on the WordPress agency page. If you need the strategy layer first, Tech Strategy & Consulting is the place to start.
What we cover
Three delivery lanes.
Workflow automation
Your team does daily what software could handle
Order intake, supplier routing, approvals, status updates: automated where volume justifies it, with a named human for anything that needs judgment.
LLM inside existing tools
AI that works where your team already works
Your CRM, helpdesk, and internal docs get AI-assisted drafts and answers. Tested against real content before it touches a customer.
What ships
What you get when we are done.
Demo day is easy. Week three is when volume spikes, AI spend creeps up, or a queue backs up with nobody watching. **We document what happens when things go wrong, before launch.**
✓
Connections to your source systems, with a written map of what data moves where
✓
Webhooks safe to rerun, plus a clear place for failed jobs and how to replay them
✓
Processing sized for your busiest week, not the traffic in a presentation
✓
An exception inbox with routing rules and a named human for each type of edge case
✓
Checks that AI instructions still work after changes, with spend caps per team
✓
A dashboard your ops lead can read: speed, error rate, and cost per automated job
✓
Written notes on token handling, personal data boundaries, and how long data is kept
Fit
Who this work serves.
Good fit
Teams where manual work is eating budget
Sales teams copying data into CRM by hand. Support teams answering the same ten questions. Finance teams assembling reports from five spreadsheets. Marketing teams waiting on IT to connect the new tool. If that describes your week, this is the right conversation.
- EU companies with real customer volume and compliance constraints
- Leaders who will name an owner for exceptions before we write code
- Stacks where getting systems to talk is the real product, not an afterthought
Probably not us
When we say no early
If you need a slide deck to justify a vendor you already picked, or a twelve-month AI programme with no date for going live, we will tell you upfront and save everyone the quarter.
- Nobody owns data quality or what happens when automation hits an edge case
- Procurement needs a big-four logo more than something running in production
- The project cannot name a measurable outcome within 90 days
Recent results
What changed after we shipped.
What broke
A B2B services client had intake buried in email
Support copied order details from inboxes into three systems by hand. Errors showed up Friday afternoons. Nobody owned the handoff when a field did not match.
What we built
Intake automation with a named exception owner
We wired email intake to the CRM and warehouse tools, routed mismatches to one ops lead, and capped AI spend per workspace. First production path went live in twelve business days.
What moved
Ticket volume dropped 38 percent in 60 days
Manual intake tickets fell by a median 38 percent within two months. The ops lead still handles exceptions. The team stopped copying the same fields every morning.
FAQ
Questions teams ask before approving automation scope.
What is included in AI and automation engagements?
We map how the work runs today, connect the tools, build the automated path, and leave you with reporting on volume, cost, and reliability. One named lead from brief to launch.
How quickly can we launch a first automation path?
A first production path typically ships in ten to twenty business days when access, data ownership, and staging parity are already in place.
How do you avoid AI pilots that stall?
We scope only use cases with clear owners, measurable success criteria, and a production cut line. If those conditions are missing, we stop before build starts.
What does an AI automation workflow for a European SME actually look like end-to-end?
We start by watching how the work actually runs: which tasks repeat, how long they take, where errors show up. Then we wire triggers, data moves, exception handoffs, and monitoring on top of tools you already use. First production path typically ships in 10 to 20 business days. A named human stays in the loop for anything requiring judgment.
How do you connect AI automation to legacy systems without a full migration?
We build a connector layer that maps your existing data structures and API surface. The automation tool talks to the adapter; the legacy system does not change. We document the deprecation path so you can migrate cleanly later, but nothing breaks in the interim.
How does LLM integration inside existing tools work in practice?
We embed LLM capabilities inside your CRM, helpdesk, or internal knowledge base via API. Before touching real customers, we run a prompt regression suite against representative data, set hard token cost caps per workspace, and define PII boundaries so model inputs and outputs stay GDPR-compliant. The result is AI-assisted drafts and answers inside the tools your team already uses, not a new platform to manage.
- 10d Typical window from signed scope to first production integration path live behind a feature flag
- Assumes access and staging parity
- −38% Median ticket volume drop after intake automation on a recent B2B services client
- Measured 60 days post-launch
- Hard caps Token and job budgets enforced per workspace before assistants hit production traffic
- Finance-ready reporting on model spend
Concrete solution
Bring the operational risk.You get a clear diagnosis and a concrete next step.
We are the right fit if you want a team that pushes back when it matters.
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