Generative AI applied to a specific, measurable outcome
Generative AI implementation for content, code, or creative workflows, scoped to a specific business outcome.
A generative AI solution is a purpose-built tool that uses a large language or generative model to produce content, code, or creative work inside a specific, already-defined workflow, replacing a repetitive manual step rather than acting as a general-purpose assistant.
What is Generative AI Solutions?
We build generative AI applications that produce content, code, or creative output as part of your defined workflow - not a standalone chatbot experiment, but a tool wired into an actual process with a specific job: drafting a first pass of a recurring document, generating creative variations, or automating a repetitive step that previously took a person's time by hand.
The gap between a demo and a solution that holds up in production is almost entirely about scope. Scoping against a measurable outcome is what turns a proof of concept into something worth maintaining, and it protects you from the ongoing per-call cost that scales with volume once the tool goes live.
It's different from a general-purpose AI assistant or chatbot - a generative AI solution is wired into your actual workflow and data, with defined evaluation criteria and review checkpoints, not a general interface someone has to manually feed and check.
Common problems we solve
Demos that don't survive production
Prototypes work on curated examples but fall apart against messy inputs and the edge cases a demo never has to handle.
Output quality tracks input quality
Workflows with messy or inconsistent inputs need more scaffolding than a demo suggests.
Model behaviour drifts
An underlying provider update can shift model behaviour, which needs ongoing monitoring rather than a one-time build.
Review bolted on after the fact
Human review for anything customer-facing needs to be designed into the workflow, not added after a mistake ships.
Cost discovered too late
Without volume modelling upfront, the per-call cost that scales with usage often surfaces only after the tool is live.
Everything covered in this service
Use Case Scoping
A specific, measurable outcome and a clear boundary around what the tool is not meant to do, defined before any build starts.
Model & Architecture Selection
Model choice matched to the task, with retrieval-augmented generation where grounding in your own content matters.
Prompt Engineering & Evaluation
Prompts built and tested against real, messy inputs, with structured evaluation criteria instead of eyeballing outputs.
Pipeline Integration
Wired into your existing content or workflow tools, with fallback handling for output that is not usable.
Human-in-the-Loop Review
Review checkpoints and escalation paths placed where output risk actually matters.
Our optimization framework
Scope Outcome
We define the specific, measurable outcome the tool needs to hit before any model or prompt work begins.
Select Model
A model is chosen against the task and cost profile, not defaulted to whichever is newest or most talked about.
Test Prompts
Prompts get built and stress-tested against real, messy inputs rather than a handful of clean examples.
Integrate Pipeline
The tool is wired into the workflow it needs to live in, with fallback handling for outputs that don't clear quality thresholds.
Design Review
Review checkpoints are placed where output risk is highest, not applied uniformly or skipped entirely.
Monitor & Iterate
Output quality and cost get monitored after launch, since model behaviour and usage patterns both drift over time.
What actually changes
| Before | After |
|---|---|
| Team told to "add AI" with no defined outcome | Tool scoped against one specific, measurable outcome |
| Model chosen because it is newest or most talked about | Model chosen to match the task and cost profile |
| Output quality checked by eyeballing a few examples | Output evaluated against defined, tested criteria |
| Review added after a bad output ships | Review checkpoints designed in where risk is highest |
What we report on
Outcome movement
Tracked against the specific goal set at scoping - hours saved, output volume, or cost reduced.
Output quality
Measured against the evaluation criteria defined during scoping, not eyeballed after the fact.
Operating cost
Per-call and total cost tracked against the volume the tool was scoped for.
Tools & platforms
Industries we optimize for
Built for teams like yours
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.
The generative AI projects that hold up in production are rarely the most technically ambitious ones - they're the ones with the narrowest, most boring scope. A tool that drafts one specific document type reliably beats a general-purpose assistant that does ten things adequately, because the narrow tool can actually be evaluated against a clear bar.
How Generative AI Solutions connects with AI search
Generative AI Solutions ├── AI Strategy ├── AI Consulting ├── AI-Powered Applications └── Custom AI Agents
AI Strategy
Sets the broader direction and priorities that a generative AI build should fit into, before implementation starts.
AI Consulting
Advises on where generative AI fits against other approaches, including when it is not the right tool for the job.
AI-Powered Applications
Extends the same scoped-build approach to a full application rather than a single generative workflow.
Custom AI Agents
Adds autonomous, multi-step decision-making on top of generative output, for tasks that need more than a single generation step.
Related AI Solutions services
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.
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.
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
No - generative models produce probabilistic output, and no honest vendor can promise zero errors. What we build in is human review at the points where an error would actually matter, and evaluation criteria that catch quality drops before they reach a customer.
It depends on model choice and call volume, and that ongoing cost is modelled during scoping specifically so there are no surprises once the tool is live. High-volume use cases sometimes justify a smaller, cheaper model over the most capable one available.
It depends on the task, the cost profile, and any data constraints - we don't default to one provider regardless of fit. Model choice is one of the decisions made during scoping, not assumed beforehand.
Model updates can change output behaviour, sometimes subtly. We build in monitoring so a quality shift gets caught and addressed rather than discovered after it's already affected output for a while.
For nearly all real use cases, human review stays somewhere in the loop, at least initially - the question is where the review checkpoint sits, not whether one exists. Fully unattended generative output is a higher-risk setup we only recommend for genuinely low-stakes tasks.
A generative AI solution is wired into your actual workflow and data, with defined evaluation criteria and review checkpoints - not a general chat interface someone has to manually feed and check. That integration and quality control is most of the engineering effort.
Have a specific, repetitive task in mind for generative AI?
We'll scope it against a measurable outcome before writing a single prompt.
Ready to get started?
We usually reply within 24 hours.
Generative AI Solutions, in detail
Scope, area by area
| Area | What we deliver |
|---|---|
| Use Case Scope | A documented, measurable outcome the solution is being built to hit |
| Working Tool | The generative AI application, integrated into the relevant workflow or pipeline |
| Evaluation Framework | Defined criteria for judging output quality, used during build and after launch |
| Review Workflow | Human-in-the-loop checkpoints designed into the process where they matter |
How this compares
| Scoped Generative AI Solution | Open-Ended "Add AI" Initiative |
|---|---|
| Built against a specific, measurable outcome | Built against a general mandate to use AI more |
| Cost modelled against expected usage volume | Cost discovered after the fact from the API bill |
| Human review designed in where risk is highest | Review added reactively after a bad output ships |
A scoped solution can still fail - the difference is that failure is measurable and fixable, instead of an open-ended experiment nobody can evaluate.
What we measure this against
- Whether the defined outcome (hours saved, output volume, cost reduced) actually moved
- Output quality against the evaluation criteria set during scoping
- Per-call and total operating cost against the volume the tool was scoped for
Where this applies
- A content team needs a first-draft generator for a recurring, structured document type, with a human editing pass before publish
- A support team wants draft responses generated from existing documentation, reviewed before sending
- A creative team needs rapid variation generation on a defined asset type, reviewed for brand fit before use