Classic automation
Same input, same output, every time.
- Moving and syncing data between apps
- Notifications, reminders, schedules
- Calculations, validation, deduplication
- Cheap, instant and fully predictable
New Agentic AI systems with human checkpoints
AI workflows that do the busywork for your team.
KynoAI designs and runs AI workflows on n8n. Agents qualify leads, triage support, read documents and return calls, with rules, guardrails and a human in the loop where it matters.
Swipe sideways to see the whole workflow →
Execution
Connects to the tools your team already uses, plus any app with an API
HubSpotSalesforceGmailOutlookSlackMicrosoft TeamsWhatsAppGoogle SheetsAirtableNotionXeroStripeTwilioZendeskShopifyPostgresOpenAIAnthropic ClaudeGoogle DriveCalendly
Anatomy of an AI workflow
Six building blocks behind every system we ship.
An AI workflow isn't a chatbot. It's a process: something happens, an agent works out what it means, rules decide what's allowed, and your systems get updated, with a person stepping in only when they should.
01 · Trigger
Every workflow starts with an event.
A form submission, a new email, a missed call, a file in a folder, a row in a sheet, a CRM stage change or a schedule. Webhooks fire in milliseconds, so the work starts while the lead is still on your website.
- Webhooks
- Inbox watchers
- App events
- Schedules (cron)
- Chat & voice
on form.submitted → { name, email, budget, timeline, utm_source }
02 · AI agent
A model that decides, not just writes.
The agent reads the input in context, decides what to do next and calls tools to do it. It classifies, extracts, scores, drafts and plans. We pick the model per task, from frontier models like Claude and GPT to smaller private ones, and keep prompts versioned and tested.
- Classification
- Extraction
- Scoring
- Drafting
- Multi-step planning
agent.run(lead) → { score: 84, reason: "budget fit, 30-day timeline", next: "create_deal" }
03 · Tools & memory
Grounded in your data, not the model's imagination.
Agents act through tools: CRM lookups, calendars, order systems, databases and any HTTP API. Retrieval (RAG) over your documents and policies keeps answers accurate, and conversation memory means a follow-up never repeats itself.
- API tools
- Vector search (RAG)
- Conversation memory
- Structured output schemas
tool crm.findContact(email) → existing: false · kb.search("refund policy") → 3 passages
04 · Logic & routing
Not everything should be AI.
If/else branches, switches, thresholds, deduplication, retries and error paths are plain, predictable rules. They're cheaper, faster and auditable. We use AI for judgement and rules for everything else, and every failure has somewhere safe to go.
- If / Switch
- Thresholds
- Retries & backoff
- Error workflows
- Rate limits
if score ≥ 70 → sales · else → nurture · on error → alert #ops
05 · Human in the loop
People approve what's risky, the system handles the rest.
Low-confidence answers, refunds, compliance-sensitive messages and anything irreversible pause for a person. Approvals arrive in Slack, Teams or email with the context attached: one click to approve, edit or reject.
- Slack / Teams approvals
- Confidence thresholds
- Edit before send
- Escalation rules
wait for approval(#support) → approved by Priya in 2m 10s
06 · Actions & observability
Results land in your systems, and every run is traceable.
The workflow writes back to the CRM, helpdesk, ledger or calendar, and notifies the right people. Every execution is logged with inputs, outputs and timings, so you can see what happened, why, and what it cost.
- CRM & helpdesk updates
- Notifications
- Execution logs
- Cost & accuracy tracking
execution #48213 ✓ success · 6 nodes · 3.4s · £0.004
Automation vs AI
Rules where you can. AI where you must.
The most reliable systems combine both. Knowing which step needs which is most of the design work.
Agents & models
Handles input that rules can't.
- Free text, emails, calls and documents
- Classifying intent and urgency
- Extracting fields from messy files
- Drafting replies, scoring, deciding next steps
Result: automation that copes with real-world mess, without becoming a black box.
Our platform
Built on n8n. Owned by you.
We build most workflows on n8n, the open, extensible workflow automation platform, and drop into custom code wherever the work needs it.
You get a visual workflow your team can read, the flexibility of real code, and no lock-in to a black-box AI vendor.
Talk to us about your stackSelf-host or cloud
Run it in your own UK or EU infrastructure so sensitive data never leaves your control.
Hundreds of integrations
Native nodes for common apps, plus an HTTP node for anything with an API.
Code when you need it
JavaScript or Python steps for custom logic, without leaving the workflow.
AI-native nodes
Agents, memory, vector stores and tools that work with any model you choose.
Full execution history
Inspect every run, replay failures and route errors to a dedicated workflow.
Predictable costs
Priced on workflow runs rather than individual steps, so complex flows don't get penalised.
Our impact in numbers
What changes when repetitive work runs on AI workflows.
Typical shifts we target on workflow automation projects, measured against a baseline we agree with you before we build.
<1%Error ratedown from 3–5% on manual handling
60–100%Of steps run by AIwith people kept on the decisions
24/7Availabilityinstead of shift-limited cover
MinutesTurnaroundinstead of days to weeks
What we build
Workflows, agents and the operating layer around them.
Workflow automation
End-to-end automations that remove manual effort without brittle handoffs.
Agentic AI systems
Agents that reason, act through tools and know when to stop and ask.
Voice AI
Inbound reception, qualification calls and missed-call recovery that sound human.
Document intelligence
Invoices, forms and PDFs turned into validated, structured data.
Internal copilots
Knowledge assistants grounded in your own documents and processes.
Decision systems
Scoring, triage and routing built around decisions your team already makes.
How we deliver
A practical rollout model for busy teams.
- 01 · Audit
Map the workflow
We document where time is lost, where effort is duplicated, and which step AI can actually improve.
- 02 · Design
Design the system
Triggers, prompts, tools, routing rules, fallbacks and approval points, drawn as a workflow you can read.
- 03 · Build
Ship fast, safely
Tested against real examples and edge cases, then launched with guardrails and monitoring.
- 04 · Improve
Improve with evidence
We track accuracy, cost and adoption, and tune the workflow as your process evolves.
From the journal
Notes from the work.
AEO & GEO: Why Ranking in AI Answers Is the New Page One
Search behaviour has split in two. Half of your buyers still type into Google; the other half ask ChatGPT, Claude or Perplexity. Here is how Answer Engine Optimization and Generative Engine Optimization decide who gets recommended.
ReadHealthcare Marketing in the UK: Growing Patient Volume Without Crossing Compliance Lines
Healthcare is one of the most rewarding — and most regulated — verticals in digital marketing. A practical framework for clinics and hospitals that want predictable patient acquisition without policy violations.
ReadReal Estate Lead Generation: Why Quality Beats Volume Every Time
A thousand leads that never pick up the phone cost more than a hundred who book site visits. How we structure real estate funnels around qualification, not just form fills.
ReadCommon questions
Clarity before we build anything.
The right projects start with strong process context, sensible expectations and a shared definition of success.
What is an AI workflow?
An automated process that combines ordinary automation steps (triggers, rules, integrations) with AI steps that read messy input, make judgements and act through tools. A trigger starts it, an agent reasons over the context, rules route the result, and the workflow writes back to your systems, with a person approving anything high-stakes.
Why do you build on n8n?
It can be self-hosted so data stays in your infrastructure, connects to hundreds of apps plus any API, supports custom code when visual nodes aren't enough, has native AI agent nodes that work with any model, and logs every execution so you can see exactly what happened.
Is our data safe in an AI workflow?
We design for least-privilege access, keep sensitive data out of prompts where possible, can self-host in UK or EU regions, log every execution and put human approval in front of high-risk actions.
Can we start with one workflow?
Yes. Most projects start with a single high-friction process so we can prove value before expanding to a broader system.
Can you work with our existing tools?
Yes. We connect the systems your team already uses (CRM, inbox, helpdesk, accounting, telephony and spreadsheets) so adoption stays light.
Ready when you are
Bring one workflow. We'll show you what it looks like automated.
A short call to map the process, the tools involved and the first win, based in Manchester and working across the UK.