Book a Call (opens in a new tab)

Our ServicesAutomation & CRM

AI automation for the operational work that eats your team’s week

Traditional automation moves data between systems. AI automation handles the step in the middle that used to need a person: reading a messy enquiry and deciding where it goes, summarising a long thread, extracting fields from a document, drafting the first version of a reply. We have shipped AI products — Tylo AI, Mocki and InkGenX — and we build the same discipline into operational workflows, because a model that is confidently wrong inside a business process is far more expensive than one inside a demo.

Book a Call (opens in a new tab)View Our Work

What You Get

Included as standard on every engagement — not an upsell list.

An honest assessment of what to automate

We separate the tasks a language model genuinely does better — classifying, summarising, extracting structure from unstructured text, drafting in a known tone — from the ones a rule, a filter or a form handles more reliably and far more cheaply. Sometimes the recommendation is to fix the process, not add a model.

Lead and enquiry triage

Incoming messages are read, categorised, scored against your qualification criteria, enriched with company or context data, and routed to the right person or pipeline stage with a short summary attached. Your team opens a triaged queue rather than an undifferentiated inbox.

Document and data extraction

Invoices, briefs, applications, CVs, transcripts and PDFs are parsed into structured fields against a defined schema, validated, and written into your database, CRM or spreadsheet. Anything the model is not confident about is flagged for review instead of quietly saved as a guess.

Content and communication drafting

First drafts produced in your voice from your own source material: product descriptions, follow-up emails, meeting summaries, support replies, multilingual adaptations. A human still approves anything customer-facing, which is exactly where the time saving is real and the risk is not.

Support and knowledge workflows

Retrieval over your own documentation and past conversations so answers cite your actual policies rather than a plausible invention, with automatic ticket summarising, tagging, priority detection and escalation when the request falls outside what the assistant should be answering.

Guardrails, validation and human checkpoints

Structured output schemas that reject malformed responses before anything is written, confidence thresholds, explicit fallback paths, and a human approval step wherever a wrong answer would cost money or credibility. Every run is logged so you can see what the model saw and what it decided.

Cost, latency and volume control

We model cost per run before building, then keep it down with caching, tighter context, and routing simple classification to smaller models while reserving larger ones for genuine reasoning. Hard usage caps mean an unexpected traffic spike cannot produce a surprise invoice.

How We Deliver It

Stage by stage, with the approval points marked. AI Automation follows the same rhythm on every project.

  1. Find the expensive task

    We shadow the real work and measure it: how many enquiries, documents or tickets per week, how long each takes, what it costs in salaried hours, and where errors currently creep in. Without that baseline there is no way to prove the automation is an improvement rather than a novelty.

  2. Prototype against your real data

    We build a narrow version using your genuine, awkward inputs rather than clean examples, and run it alongside the human process for a short period. Most concepts change materially here — usually because the real inputs are messier than anyone remembered.

  3. Engineer the pipeline

    Retrieval, prompt templates with versioning, structured output parsing, validation, retries, error handling and logging are built as a proper pipeline in n8n, Make or custom code, so each stage is inspectable and improvable on its own rather than being one opaque call.

  4. Evaluate before and after every change

    A fixed set of real inputs with known good outputs is scored whenever a prompt, model or retrieval strategy changes. Improvements get demonstrated rather than felt, and a regression is caught in the evaluation run instead of by a customer three weeks later.

  5. Ship, monitor and tune

    Launch includes run logging, failure alerting, cost and volume dashboards, and a feedback route on individual outputs. We review real runs after a few weeks and tune retrieval and prompts against what actually happened in production.

What You Receive

The concrete artefacts handed over at the end — files, access and documentation you keep.

  • Task assessment with an automate, simplify or leave-alone recommendation
  • Cost and time baseline for the process being automated
  • Working prototype tested against your real inputs
  • Production AI workflow deployed in n8n, Make or custom code
  • Retrieval pipeline over your documents and structured data
  • Versioned prompt library with structured output schemas
  • Evaluation set and scoring results for every prompt or model change
  • Run logging, failure alerting and cost monitoring
  • Documented guardrails, fallback behaviour and human-review checkpoints

Ideal for

If two or three of these sound like your situation, this is the right place to start.

  • Your team retypes, re-reads or re-summarises the same information daily
  • Enquiries arrive as free text and someone has to sort them by hand
  • You process documents whose data ends up keyed into another system
  • Support answers the same questions with slightly different wording each time
  • A first AI experiment produced confident nonsense and lost internal trust
  • You want AI inside your operations, not a chat widget bolted to a page

Tools we use

Standard, portable tooling. The licences, accounts and source stay in your name, so nothing here is a reason you cannot leave.

  • n8n
  • Make
  • OpenAI API
  • Anthropic API
  • Next.js
  • Node.js
  • Supabase
  • PostgreSQL
  • Bubble.io
  • Vector search
  • JSON Schema
  • Webhooks
  • Intercom
  • GoHighLevel

Work We Have Shipped

Live projects where this work did the heavy lifting:

The Boomerang

Bubble.io to Custom MERN & Next.js Platform

AI & SaaS ProductsNext.jsReactNode.jsExpress

A product that started on Bubble.io and outgrew it. We ran the Bubble.io build with the team for roughly six to eight months, then rebuilt the whole thing as a custom application on the MERN stack and Next.js with Supabase as the database — including a custom Data Team Panel that replaced their paid Airtable dependency, a set of complex n8n automations, and a round of work resolving long-standing issues in their Intercom setup.

View Case StudyVisit Site

Related Services

Work that usually runs alongside this one:

n8n Workflow Automation

Build the workflows your business runs on in n8n — self-hosted or cloud — with proper error handling instead of per-task billing.

Make.com Automation

Connect the apps your business already pays for into scenarios that run reliably, with error handling and a cost you can predict.

AI & LLM Integration

Put AI to work inside your website or product where it genuinely saves time, not as a novelty chat bubble.

API Development & Integrations

Connect your website to the CRM, payment, booking and internal systems your business already runs on.

More on the Subject

What we have written about this, in more depth than a service page allows:

Automation & CRM12 August 2026

GoHighLevel for Agencies: A Practical Setup Guide

GoHighLevel replaces a stack of separate tools with one system your whole agency can resell — but only if you structure it properly on day one. Here is how we set it up, and where a purpose-built site still beats a GHL funnel.

Read More
AI & Automation8 August 2026

AI Automation for Business Operations: A Practical Guide

AI automation earns its keep inside the business, not on the marketing site. Here is where LLM-driven workflows genuinely pay off, how to scope the first one, and how to keep it accurate, reviewable and affordable.

Read More
Automation & CRM5 August 2026

n8n Workflow Automation: A Practical Guide

n8n is the automation tool we reach for when a workflow has to survive contact with real production data. Here is how it is built, how to run it properly, and the operational details that decide whether your automations stay reliable.

Read More

Frequently Asked Questions

The questions we get asked most about AI Automation.

Three layers, used together. Retrieval grounds the model in your own documents and records so it summarises rather than recalls. Structured output schemas and validation reject anything malformed or out of range before it is written anywhere. And confidence thresholds route uncertain cases to a human queue instead of guessing. The failure mode we design for is "escalate to a person", never "write something plausible to the database".
Not when it is configured correctly. The major providers offer API terms and account settings that exclude your data from training, and we enable them explicitly rather than trusting a default. We also agree upfront what leaves your systems at all — personal data can be redacted or swapped for internal references before a request is sent — and we document the flow so your privacy policy describes what genuinely happens.
It depends on run volume, how much context each run carries and which model handles it, which is why we estimate cost per run during the prototype rather than after launch. Typical operational workflows sit in the low tens of dollars a month; heavy document processing costs more. We reduce it with caching, tighter context and model routing, and set hard caps so spend cannot run away.
Very often plain automation does. If the rule can be written down — this field equals that value, so route it there — a deterministic workflow in n8n or Make is cheaper, faster and completely predictable, and we will build that instead. A language model earns its place only where the input is unstructured or the judgement genuinely varies. We say so plainly rather than adding a model because it is fashionable.
Tylo AI is a research platform with several AI and LLM tools and graph-based data display. Mocki runs adaptive LLM mock interviews that respond to the candidate in real time. InkGenX generates artwork through multiple image models behind one workflow. On the operations side we built complex n8n automations in production for The Boomerang alongside their Next.js and MERN rebuild. That is product-grade experience, not a course certificate.

Ready to start on AI Automation?

Send us the brief — or just the problem. You will get a written scope, a timeline and a fixed price, usually within one working day.

Book a Call (opens in a new tab)Contact us