AI Automation Services
We build AI agents and automated workflows that plug into the CRM, helpdesk, ERP and spreadsheets you already run — not slide-deck concepts. Platform-agnostic, scoped narrow, measured against a baseline, and built with a human approval step wherever a mistake would cost you money.
The platforms we actually build on
We're not tied to one vendor's affiliate program. We pick whichever of these fits your stack, budget and data-residency needs — sometimes more than one, wired together.
Open-source and self-hostable, with native AI-agent nodes, persistent agent memory and no separate AI pricing tier. Billed per workflow execution, not per step — the cheapest option at high volume.
9,000+ app connectors and an AI Copilot that drafts workflows from plain-English prompts. Billed per task (each action), so cost climbs faster than n8n on long, high-frequency workflows.
A visual canvas that handles conditional branching more naturally than a linear builder, plus an AI assistant (Maia) that builds scenarios from a description. Watch credit burn on AI-heavy scenarios.
Direct integration against OpenAI, Anthropic or Gemini APIs, wired into your CRM/ERP database rather than a generic connector. The right call when your data model or compliance needs don't fit a no-code tool.
This page covers the practice: agents, copilots and LLM-driven workflows. If you already know which department needs the work, you may want to start with our narrower pages instead — customer support automation, marketing automation or general workflow automation — and come back here for the AI layer underneath.
Where AI automation actually pays off in 2026 — and where it doesn't
The findings below come from McKinsey's global State of AI survey (published November 2025) and Gartner's agentic AI research, not our sales deck.
Customer support and internal knowledge work move first
McKinsey's survey finds that IT and knowledge management lead reported agentic AI adoption — service-desk management and "deep research" style lookups are the most mature use cases — and contact-centre or customer-service automation is one of the most commonly reported AI applications company-wide.
That matches what we see in scoping calls: a support inbox or a WhatsApp/live-chat queue with repetitive, well-documented questions is usually the fastest place to get a working AI agent into production, because the "correct answer" is easy to verify. See our dedicated customer support automation service if this is your starting point.
Back-office workflow automation compounds quietly
Respondents most often report cost benefits from AI in software engineering, manufacturing and IT — the unglamorous, high-volume back office. This is the domain of tools like n8n, Zapier and Make: invoice data extraction, lead routing, CRM enrichment, report generation, inventory alerts — an AI step added to a workflow that was already running.
It rarely makes a good case study, but it's the most reliable ROI on this page, because the workflow existed before the AI step and the AI step is judged against a real baseline. See workflow automation and business automation for the non-AI foundation this usually sits on.
Marketing and sales see the biggest revenue upside — and the most noise
Revenue increases from AI use are most commonly reported in marketing and sales, strategy/corporate finance, and product development — ahead of every other function McKinsey tracks. That covers AI-assisted content drafting, lead scoring, CRM copilots and — increasingly — visibility in AI answer engines themselves. Related reading: marketing automation and AI SEO services.
The trap: marketing is also where "AI-powered" gets bolted onto products with the least underlying capability. Ask what model actually runs the feature before paying a premium for the label.
Most agentic AI pilots still don't reach production — and that's the honest baseline
Gartner predicts over 40% of agentic AI projects will be cancelled by the end of 2027 due to escalating costs, unclear business value or inadequate risk controls, and estimates only around 130 of the thousands of vendors marketing "agents" have real agentic capability — the rest is what Gartner calls "agent washing": rebranded chatbots and RPA bots. McKinsey's own numbers back this up: only 39% of organizations report any enterprise-level EBIT impact from AI at all, and most of that is under 5%.
Our response is to scope narrow on purpose. We start with one workflow, define what "an agent" is allowed to do without a human sign-off, and measure it against a baseline before recommending you scale it — see the transparency section below for exactly what that means in practice.
Three ways to engage us
Scroll horizontally on mobile. Every engagement starts from a scoping call; none of them starts by picking a tool for you before we've seen your workflows.
| AI Readiness Audit | Agent & Workflow Build | Managed AI Operations | |
|---|---|---|---|
| Best for | Knowing which use cases are worth building before spending | A validated use case ready to ship into production | Multiple live agents that need upkeep, month after month |
| Timeline | 2–3 weeks | 4–8 weeks | Monthly, 3-month minimum |
| Process & data mapping | Included | Included | Included |
| Impact vs. effort scoring | Included | Included | Included |
| Tool / platform selection | Recommendation only | Included | Included |
| Build & integration | Roadmap only | Included | Continuous |
| Human-approval guardrails | Recommended | Configured | Monitored |
| Monthly reporting | Final report | Weekly check-ins | Yes |
| Pricing | Fixed quote after scoping call | Fixed quote after audit or scoping call | Monthly retainer |
We quote after a scoping call, because a single WhatsApp support agent and a multi-department agent rollout are not the same job. The scoping call is free.
AI Automation Readiness Audit
A clear-eyed map of where AI agents and workflow automation would actually help your business — and where they wouldn't.
We review your current workflows across operations, support, sales and marketing, interview the people doing the work, and score candidate use cases on expected impact against implementation effort and risk — the same "where decisions are needed vs. where routine automation is enough" framing Gartner recommends. You get a prioritised roadmap, not a vendor pitch.
What you get
Process and data mapping across your key functions, a scored shortlist of automation candidates, a build-vs-buy recommendation for each (existing CRM/helpdesk AI feature vs. custom build), a data-privacy and risk review, and a prioritised roadmap your team or ours can execute from.
What it's not
It's not a build — the audit tells you what's worth automating and roughly how, not working code. It's also not a vendor comparison written to justify a tool we already prefer; if your existing CRM's built-in AI feature already covers a use case, we'll say so instead of proposing a custom agent.
Best for: businesses that keep hearing about AI agents and want an honest map before committing budget, and teams that have tried one AI tool and aren't sure if the disappointment was the tool or the use case.
AI Agent & Workflow Automation Build
We design, build and wire up the agent or workflow into the tools you already run — with a human checkpoint on anything that matters.
Typical scope: an AI agent or automated workflow built on n8n, Zapier, Make, or a custom API integration, connected to your CRM, helpdesk, WhatsApp Business, email or spreadsheets, with defined boundaries on what it can do autonomously versus what needs a human to approve.
What you get
Everything in the audit if you haven't already run one, plus design and build of the agreed workflow(s), integration with your existing systems, human-approval steps on high-stakes actions, a monitoring view for accuracy and cost, staff training, and a before/after report against the baseline agreed at kickoff.
What it's not
Not a general software development project or a platform migration — if the audit shows your bottleneck is a missing CRM or ERP, we'll say so and point you to CRM software or cloud ERP instead. And not unlimited scope: the build ships the agreed use case, deliberately, rather than expanding mid-project.
Best for: businesses with a validated use case — from the audit or from their own experience — ready to move from idea to something running in production.
Managed AI Operations
Ongoing monitoring, maintenance and expansion of the agents and automations you already have live.
Models, vendor APIs and pricing change faster than most internal teams can track without a dedicated hire. A managed retainer keeps your live agents accurate and cost-controlled, and adds new use cases on a steady cadence instead of in occasional, expensive bursts.
What you get
Ongoing monitoring of agent accuracy, failure rate and token/execution cost, prompt and workflow maintenance as underlying models and APIs change, a new use case scoped and shipped roughly each quarter, and monthly reporting on hours saved and cost against your baseline.
What it's not
Not worth it for a single, simple automation — the value is in managing a portfolio of live workflows. If you have one agent and no plans to add more, the build engagement's warranty period and our forum-style support are usually enough.
Best for: teams running three or more live automations that don't want to hire an internal AI operations person yet, and businesses that got burned by a pilot going stale after launch.
How an engagement runs
The same sequence every time, because it's designed to keep your project out of Gartner's 40% cancellation statistic.
- Scoping call (free). We map your current workflows, tools and data, and tell you honestly whether AI automation is your bottleneck — sometimes the answer is a missing CRM or a process problem, not a missing agent.
- Opportunity scoring. Candidate use cases scored on impact versus effort and risk, favouring the areas research shows pay off fastest — support, back-office workflows, and well-defined marketing/sales tasks.
- Platform selection. We pick n8n, Zapier, Make or a custom API build based on your stack, data-residency needs and budget — not whichever tool we're most used to selling.
- Build with guardrails. We define exactly what the agent can do without approval, add a human checkpoint for anything higher-stakes, and test against real (or sandboxed) data before go-live.
- Launch and monitor. We track task success rate, cost per execution and time saved against the baseline agreed at kickoff — not vibes.
- Review, expand or stop. Monthly report on what's working. Anything underperforming gets rebuilt or retired rather than left running quietly at a loss.
How we work — and what we won't claim
AI automation attracts inflated promises right now. Here's where we stand.
- We don't rebrand a chatbot as an "agent." Gartner's research identifies "agent washing" — vendors relabelling existing chatbots, RPA bots and AI assistants as agentic — as widespread. If a use case only needs a scripted workflow or a simple assistant, we'll build that, not something fancier and less reliable.
- We won't recommend an autonomous agent where a simple workflow does the job. Gartner's own guidance is to reserve agents for cases where a decision is genuinely needed, use plain automation for routine steps, and use an assistant for simple retrieval — we follow the same triage.
- We don't promise a specific productivity or ROI percentage. McKinsey's latest survey found only 39% of organizations report any enterprise-level EBIT impact from AI, and most of that is under 5%. Your baseline, data quality and process maturity determine your available upside, and the audit is how we find out what's realistic for you specifically.
- Data and privacy are scoped before we build, not discovered after. We document what data reaches a third-party model provider, what stays inside your own systems, and what retention or redaction rules apply, before any workflow goes live — not as an afterthought.
- We'll tell you when off-the-shelf already covers it. Many CRMs and helpdesks now ship a built-in AI copilot. If yours already does what you're asking us to build, we'll say so rather than sell you a custom agent you don't need.
Frequently asked questions
What is AI automation, exactly?
It's the use of AI models — mostly large language models — inside an automated workflow, either as a smarter step in a traditional automation (drafting a reply, classifying a request, extracting data from a document) or as an "agent" that plans and executes several steps toward a goal with limited human input. It sits on top of traditional workflow automation: the workflow does the plumbing, and the AI does the judgment calls that used to require a person.
Is an "AI agent" different from a chatbot or RPA bot?
In principle, yes — a true agent can plan multi-step actions and adapt based on what it finds, where a chatbot answers one question at a time and an RPA bot follows a fixed script. In practice, Gartner estimates only around 130 of the thousands of vendors marketing "agentic AI" products have real agentic capability; the rest are relabelled chatbots or RPA. We'll tell you plainly which category a proposed solution actually falls into before you pay for it.
Is my business too small for AI automation?
No, but expectations should scale with reality. McKinsey found companies under $100 million in revenue are less likely to have reached the "scaling" phase of AI adoption than large enterprises (29% versus nearly half of companies over $5 billion) — mainly because smaller teams have fewer resources to throw at the problem, not because the technology doesn't work at small scale. A single well-scoped agent — one support inbox, one lead-routing workflow — is often a better starting point for a smaller business than an ambitious multi-department rollout.
Which AI tools and platforms do you actually build with?
Mainly n8n, Zapier and Make for workflow orchestration, connected to OpenAI, Anthropic or Google's models depending on the task and your data requirements, plus direct API integrations when a no-code tool can't reach your data model. We're not tied to one vendor — see the platform comparison above for how we choose.
Will our customer data be sent to a third-party AI model?
For most cloud-hosted agents, some data has to reach a model provider's API to generate a response — that's how large language models work. What we control is which data, how much, and whether it's retained. We map this explicitly during scoping: which fields are sent, which are redacted or kept local, and what each vendor's data-retention policy says, so you're deciding with full information rather than finding out afterward.
What does AI automation cost?
We quote after a free scoping call, because a single support-inbox agent and a multi-department rollout are entirely different jobs — and because platform costs (n8n, Zapier, Make, or model API usage) scale with your usage, not with a flat fee we can quote blind. The audit is the smallest fixed-scope engagement and the usual starting point if you're not yet sure what to build.
Do you guarantee a return on investment?
No — and we'd be cautious of anyone who quotes a guaranteed ROI figure before seeing your workflows and data. What we commit to: recommendations grounded in your actual process data rather than a generic pitch, guardrails that stop a misbehaving agent from causing real damage, and honest monthly reporting against the baseline we agree at kickoff — including telling you when something isn't working so it can be fixed or retired rather than quietly left running at a loss.
How is this different from your Customer Support, Marketing or Workflow Automation services?
This page covers the AI and agent layer itself — the models, the platforms, and how we scope and govern them. Customer support automation, marketing automation and workflow automation are the department-specific applications of that same layer. If you already know which team needs help, start on the relevant page; if you're not sure yet, the readiness audit above is designed to answer that question first.
Find out what's actually worth automating
Book a free scoping call. We'll look at your workflows and tools and tell you honestly which use cases are worth an AI agent, which just need a simple workflow, and which aren't worth automating yet.
Book a free consultation