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.
AI solutions is finding where AI genuinely fits a business, then scoping only the strategy, consulting, generative AI, or application work that follows from a specific, measurable outcome, not AI adopted for its own sake.
'Add AI' is not a use case, and most AI initiatives that fail were scoped exactly that vaguely. The businesses getting real value picked two or three specific, measurable problems - hours saved, a bottleneck removed - instead of a broad mandate to 'use AI more.'
We start with an honest assessment of where AI genuinely fits your business, including telling you plainly where it doesn't. Consulting covers vendor evaluation and build-vs-buy calls, and build work only starts once there's a specific, scoped outcome to aim at - not a vague aspiration.
Problems we solve in AI Solutions
An open-ended mandate to use AI more
No specific problem attached, so activity happens with no clear answer to what actually improved.
A tool purchased before a use case exists
A use case gets found for the tool afterward, instead of the other way round.
Vendor claims taken at face value
A demo built to impress in thirty minutes is not the same as a tool that holds up against real data at real volume.
Demos that do not survive production
Prototypes work on curated examples but fall apart against messy inputs and real edge cases.
AI bolted onto a product instead of built into it
A chat icon disconnected from the interface signals AI was involved without changing what the product does.
Cost and risk discovered too late
Data privacy, integration, and per-call cost surface only after budget is already committed.
Everything covered in AI Solutions
AI Strategy
Identifying which specific problems AI can realistically solve, in what order, before recommending any tool.
AI Consulting
An independent second opinion on a specific vendor, tool, or build decision already in motion.
Generative AI Solutions
Purpose-built tools that produce content, code, or creative output inside a defined workflow.
AI-Powered Applications
AI built as a core product function, with fallback handling and phased rollout, not a bolted-on widget.
What actually changes
| Before | After |
|---|---|
| An open-ended mandate to use AI more | A short, ranked list of prioritised, scoped use cases |
| A tool gets purchased first, and a use case is found for it afterward | Use cases identified and scored before any tool is chosen |
| Vendor claims taken at face value | Claims checked independently against your actual data and requirements |
| AI sits in a separate widget disconnected from the core product | AI performs a core function inside the existing workflow |
| Cost and risk discovered after budget is already committed | Cost, data privacy, and integration risk modelled before commitment |
What we report on
Use case prioritisation
Each candidate scored against feasibility and value, reported as a ranked list.
Readiness gaps identified
Data, system, and workflow gaps documented before any build commitment.
Outcome movement
Tracked against the specific goal set at scoping - hours saved, output volume, or cost reduced.
Cost and performance against modelled bounds
Tracked in production against the figures modelled before launch.
Tools we work with
Every AI Solutions service, broken down
Find Where AI Actually Pays Off, Before You Build Anything
AI strategy that pinpoints the highest-value use cases for your business before recommending a single tool.
AI SolutionsGet a Second Opinion Before You Commit Budget
Independent AI advisory for teams evaluating vendors, tools, or an in-house build.
Generative AI applied to a specific, measurable outcome
Generative AI implementation for content, code, or creative workflows, scoped to a specific business outcome.
AI That Does Real Work Inside Your Product, Not Beside It
AI application development where the model is core to the product, not a chatbot bolted onto the side.
Industries we work with
Built for teams like yoursPairs well with AI Solutions
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.
The manual work outside marketing gets automated too
Workflow, process, data, reporting, and document automation across departments - not just the marketing function.
Why choose EASI7
We fix tracking before we touch results
Every engagement starts with the same audit we'd run on our own accounts, so the numbers you're optimizing against are actually true.
One accountable team, not siloed vendors
The same team that does this also owns the rest of your stack - no handoff, no blame-shifting between vendors.
Built for how AI search actually works
Everything we build is structured to be extractable by AI answer engines, not just ranked in a list of blue links.
Reporting tied to your own dashboard
Every number we report ties back to GA4/GTM events you can see directly - not a screenshot from a tool you can't independently verify.
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
Strategy identifies where AI genuinely fits your business before anything gets built - the use cases, the priority order, the roadmap. Consulting is a narrower, second-opinion engagement, usually vendor evaluation or a build-vs-buy call on something already in motion. Generative AI solutions and AI-powered applications are the build phase, once a specific, scoped outcome exists to build toward.
No - if you already know the specific use case and it's genuinely scoped, we can start building directly. Strategy work exists for the more common situation, where 'we should probably do something with AI' hasn't yet been narrowed down to a use case worth spending money on.
No, and any agency that promises a specific ROI number before scoping the work is guessing. What we can commit to is scoping against a measurable outcome up front - hours saved, a specific bottleneck removed - so you know what you're actually measuring against once it ships, rather than judging success on a vague feeling.
It depends heavily on which models and vendors are involved and what data actually needs to touch them - that gets assessed case by case as part of the strategy or consulting phase, not treated as a generic checkbox. Where sensitive data is involved, that constraint shapes the build-vs-buy and vendor decisions from the start, not after something is already live.
Strategy and consulting engagements usually run a few weeks - they're bounded by how much discovery and evaluation is needed, not by build time. Generative AI and AI-powered application builds vary far more, from a few weeks for a narrow tool to several months for something integrated deep into a product, depending entirely on scope.
Then that's what we'll tell you, and it happens more often than people expect. AI adds cost, maintenance, and often a dependency on a third-party model provider - if a simpler rule-based tool or a straightforward process fix solves the actual problem, that's the cheaper and more reliable answer.
Not sure where AI actually fits your business?
We start with an honest assessment of where it helps - and tell you where it doesn't.
Ready to get started?
We usually reply within 24 hours.
AI Solutions, in detail
Scope, area by area
| Area | What we deliver |
|---|---|
| Readiness & Strategy | A written assessment of data, systems, and workflow fit, with prioritised, scoped use cases |
| Independent Evaluation | Vendor, tool, and build-vs-buy assessment measured against your actual requirements |
| Working Solutions | Generative AI tools and AI-powered application features built against a measurable outcome |
| Monitoring & Review | Evaluation criteria, fallback handling, and production cost and performance tracking |
How this compares
| Scoped AI Adoption | Open-Ended "Add AI" Initiative |
|---|---|
| Built against a specific, measurable outcome | Built against a general mandate to use AI more |
| Data, system, and vendor readiness checked up front | Readiness gaps surface mid-build, when they are expensive to fix |
| AI performs a core function inside the existing workflow | AI sits in a separate widget disconnected from the core product |
None of this guarantees a positive return - it guarantees that if it fails, the failure is measurable and fixable, not an open-ended experiment nobody can evaluate.
What we measure this against
- Whether the defined outcome (hours saved, output volume, cost reduced) actually moved
- Use case prioritisation and readiness gaps documented before any build commitment
- Cost, latency, and output quality tracked in production against what was modelled before launch
Where this applies
- Leadership has a mandate to do something with AI with no specific use case identified yet
- A team is evaluating an AI vendor or an in-house build and wants an independent second opinion before committing budget
- A content or support workflow needs a generative AI tool scoped to a specific, measurable outcome
- A product team wants an AI feature built as a core function, not a bolted-on chat widget