AI applied where it actually earns its cost
AI strategy, agents, chatbots, AI-powered marketing automation, business automation, and AI analytics - scoped narrowly, not deployed for its own sake.
"Add AI" is not a use case, and most AI initiatives that underdeliver were scoped exactly that vaguely from the start. Every AI or automation project here starts from a specific, measurable problem - hours saved, a bottleneck removed, a response time cut - not a broad mandate to use AI more.
What usually goes wrong here
- AI projects started without a specific, measurable outcome in mind
- Chatbots and agents scoped too broadly, producing something nobody trusts in production
- Automation built around fixed rules that never adapt as patterns shift
- The same manual handoffs repeated in finance and operations that marketing automated years ago
- Reporting that shows what happened without ever explaining why or what to do next
What we cover end to end
AI Solutions & Strategy
- AI strategy and consulting
- Build-vs-buy guidance
- Generative AI solutions
AI Agents
- Narrowly scoped agents for marketing, sales, research, and support
- Custom AI agents
AI Chatbots
- Chatbots grounded in real documentation
- Lead generation and support chatbots
AI Marketing Automation
- Lead scoring, enrichment, and personalisation
- Content automation
Business Automation
- Workflow and process automation
- Data and document automation
AI Analytics
- AI-powered reporting
- Predictive analytics and customer insights
How we structure the work
Assess
An honest read on where AI genuinely fits - and where it doesn't yet.
Scope
Narrow the use case to something specific and measurable before building anything.
Build
Guardrails and escalation paths designed in from the start, not added after an incident.
Monitor
Accuracy and outcomes tracked after launch, with a human still in the loop where it matters.
The disciplines inside AI & Automation
AI applied only where it earns its cost
Strategy, consulting, and generative AI applications scoped to specific, measurable outcomes - not AI for its own sake.
Agents scoped narrow enough to trust
Marketing, sales, research, and support agents scoped narrowly enough to actually trust in production.
Chatbots that know what they don't know
Website, support, lead-gen, and internal chatbots built on your documentation - not general model knowledge.
Automation that improves as it learns your data
Content automation, AI-enhanced lead scoring, enrichment, and personalisation layered on top of your existing marketing automation.
The manual work outside marketing gets automated too
Workflow, process, data, reporting, and document automation across departments - not just the marketing function.
Reporting that explains the number, not just shows it
AI-powered reporting, marketing intelligence, predictive analytics, and automated insights that explain the number, not just show it.
How to engage us for this
Project-based
A defined outcome with a start and end date - an audit, a migration, a campaign build, a tracking overhaul. Fixed scope, fixed price, agreed upfront.
Ongoing retainer
Continuous management and optimization once the initial build is live - campaigns, SEO, reporting, and iteration run every month under one accountable team.
Advisory
Strategy and oversight without full delivery - we review what's already running, unblock decisions, and point an in-house or existing team in the right direction.
Common questions
By grounding responses in your actual documentation rather than general model knowledge, and building clear escalation to a human whenever confidence is low - not by promising it never happens.
No - the aim is removing repetitive work around decisions, not the decisions themselves. Every build we've done keeps a human in the loop where judgment actually matters.
That's the first conversation, before any build - an honest assessment of where it fits. We'll tell you plainly if the answer is 'not yet.'
No - and treat any vendor who claims that with suspicion. What we build in are confidence thresholds and escalation paths, so a wrong or uncertain answer gets routed to a human instead of shipped to the customer.
Some is usually needed - even a narrow use case needs a reliable source of truth to ground it in. Part of scoping is identifying what data actually exists versus what needs to be organised first.
Considering AI for a specific problem?
We'll help you scope it narrow enough to actually trust in production.
Ready to get started?
We usually reply within 24 hours.
AI & Automation, in detail
Where this applies
- A team fielding the same handful of support questions repeatedly with no automation in place
- A business unsure whether an AI tool being pitched to them is genuinely useful or overstated
- Manual processes in finance or operations that marketing automated years ago
- Dashboards that show data but never explain what changed or why