Practical AI builds for growing companies: scoped to the budget and team size you actually have, without the enterprise overhead you don't need yet.
Most AI advice is scaled for a company you're not
Growing companies get sold enterprise-shaped AI advice platforms, governance frameworks, dedicated ML teams when what actually moves the business is one automated workflow shipped this quarter. Overbuilding for a scale you haven't reached yet burns budget that should go toward the product.
What we build
- One clearly-scoped AI workflow, chosen for impact-to-effort ratio, not novelty
- Systems built on managed infrastructure so there's no platform team required to run them
- Clear documentation so a small team can maintain what we hand over
- A build sized to ship in weeks, not quarters
- A prioritized backlog of what to build next, once the first system proves out
How we work
Identify the single workflow costing the most hours or losing the most revenue today
Build the narrowest version that solves it, on managed infrastructure
Ship, measure, and only then decide what earns the next investment
Hand over documentation your team can actually maintain without us
Typical stack
Frequently asked questions
Mainly in scope and infrastructure choices: SMB builds favor managed services over self-hosted infrastructure, target one high-impact workflow instead of a platform, and are sized so a small team can maintain them without a dedicated AI engineer.
Whichever single workflow currently costs the most hours or loses the most revenue to manual work not the most impressive-sounding AI feature. We help identify that workflow honestly, even when the answer is less exciting than what a vendor might pitch.
A well-scoped first system typically ships in 4–8 weeks. Cost depends on integration complexity more than the AI itself connecting to your existing tools is usually the bulk of the work, not the model.