AI applied where it actually earns its cost
Strategy, consulting, and generative AI applications scoped to specific, measurable outcomes - not AI for its own sake.
"Add AI" is not a use case, and most AI initiatives that fail were scoped exactly that vaguely from the start. The businesses getting real value from AI right now picked two or three specific, measurable problems - hours saved, output increased, a specific bottleneck removed - rather than a broad mandate to "use AI more."
We start with strategy: an honest assessment of where AI genuinely fits your business before recommending any specific tool or build, including telling you plainly where it doesn't fit yet. Consulting work covers vendor evaluation and build-vs-buy decisions once a use case is identified. Generative AI and AI-powered application work only starts once there's a specific, scoped outcome to build toward - a measurable one, not a vague aspiration.
If the honest answer to "why do you want AI here" is "everyone else is doing it," that's usually a sign to slow down before spending on it.
Every AI Solutions service, broken down
Where AI actually fits your business, before any build
AI strategy work identifying realistic, high-value use cases before recommending any specific tool or build.
A second opinion before you commit budget
AI consulting and advisory for teams evaluating vendors, tools, or in-house AI builds.
Generative AI applied to a specific, measurable outcome
Generative AI implementation for content, code, or creative workflows, scoped to a specific business outcome.
Applications with AI doing real work, not a chatbot bolted on
AI-powered application development where AI is core to the product's function, not a superficial add-on.
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.
Talk to us →