Applied AI & Automation
AI and automation that ship intoyour real workflows
One team for everything AI and automation — answering calls, handling enquiries across every channel, building AI into your product, and removing the repetitive steps between the tools you already pay for.
Most AI projects stall as demos and most automation projects never get mapped. Ours go live and get measured. A voice agent picks up calls after hours, a chatbot covers your website, WhatsApp and Instagram, custom AI works inside your product — and behind it, lead routing, quoting, invoicing and reporting run automatically across your existing stack, all logged so you can see what it actually produced.
- AI
- LLM
- Voice
- Chatbot
- Workflows
- CRM Integration
Is this you?
If any of these sound familiar, this is the service you are looking for.
- Calls and messages arrive after hours and turn into lost business
- Enquiries land in four different inboxes with no single queue
- Leads are copied by hand between ads, forms and your CRM
- Quotes, invoices or reports are rebuilt manually every week
- You tried an AI prototype and it never survived real data
What you get
Deliverables
Where AI is worth it
A shortlist of use cases ranked by value and how ready your data is — we will say when AI is the wrong answer.
Answers from your own content
Your documents, product data and past tickets made searchable so replies are accurate, not invented.
Voice agent
Answers inbound calls, books meetings, hands over to a human, and logs the transcript to your CRM.
Website & social chatbot
One assistant across your site, WhatsApp, Instagram and Messenger that captures leads.
AI inside your product
Streaming responses, safety limits, fallbacks and cost caps built into your own app.
Workflows that run themselves
Lead routing, reminders, follow-ups, quotes and document generation, automatically.
Your tools, connected
Ads, forms, CRM, accounting, email and messaging joined end to end.
Proof it works
Accuracy testing plus a live dashboard showing cost, speed, resolution rate and where deals drop off.
Typical stack
- OpenAI / Anthropic
- Realtime speech models
- LangChain
- pgvector
- Twilio
- WhatsApp Business API
- n8n
- Make
- HubSpot
- CRM APIs
Outcome — Every call answered, every inbox covered, hours of repetitive work removed each week, and a dashboard that shows exactly what was handled.
How we do it
How we do it,step by step
Typical durations for a project of this type. You get a working demo at the end of every sprint, not a surprise at the end.
- 01
Discover
We shadow the process, pick one high-value use case and check your data can support it.
1 week - 02
Design
Conversation flows, escalation rules, tone and each automation drawn out and approved — including what happens on failure.
1 week - 03
Build
Content indexed, agent built, channels connected and workflows tested against real data.
2–4 weeks - 04
Launch
Pilot on a share of traffic, tuned on real transcripts, then full rollout with monitoring and a manual fallback.
1–2 weeks - 05
Support
Monthly accuracy review, prompt tuning, fixes when a tool changes its API, and new use cases.
Ongoing
Who works on it
- AI lead
- ML / integration engineer
- Automation engineer
- Full-stack engineer
- Conversation designer
A small senior team, named at kick-off. The people who scope your project are the people who build it.
Common questions
- Will our data train public models?
- No. We use enterprise endpoints with training disabled, or self-hosted models where required.
- Can it run on our own infrastructure?
- Yes — open-weight models can be deployed inside your cloud or private network.
- Will callers know it is AI?
- Yes, we disclose it. It is required in many regions and, in practice, better received than pretending.
- What happens with a complicated call?
- The agent recognises what it cannot handle and passes it to a person with the full context attached.
- Do we need to change tools?
- Rarely. We automate around your existing stack unless one tool is genuinely the bottleneck.
- What if a workflow breaks?
- Every flow has retries, an alert to your team and a documented manual fallback.