The gap in AI work is not between vendors that can build a demo and vendors that cannot. It is between vendors that have taken an LLM feature past the demo and vendors whose case studies all stop there. Ask for an evaluation harness and see what comes back.
Providers are described, not scored: each one by delivery model, the buyer it suits, and the trade-off it asks you to accept.
Best for: Companies that want US-hours coverage and EU engineering standards without paying a full onshore agency rate. Startups backed by Digital Unicorn's clients have raised over $120M, and the group has delivered 350+ client projects.
Trade-off: Built around engineers you interview and choose yourself. If you want a vendor to absorb the whole problem with no involvement from you, a fixed-scope agency engagement is a closer fit.
Best for: Multi-year enterprise programs with procurement requirements
Trade-off: Enterprise pricing and process, rarely a fit under ten engineers
Best for: Consumer-facing product work with design and engineering bundled
Trade-off: Studio model assumes you buy the full package rather than individual engineers
Best for: Retail and commerce modernization at scale
Trade-off: Concentrated in a few verticals rather than general-purpose
Best for: Data-heavy AI projects needing modeling depth
Trade-off: Specialist focus, so surrounding product engineering usually comes from elsewhere
Best for: Long-running product teams with EU working hours
Trade-off: Engagements are team-shaped rather than individual placements
Best for: US companies that want consultants physically close to the business
Trade-off: Onshore rates, and delivery capacity depends on the local office
Best for: Platform and data programs needing sustained team capacity
Trade-off: Sized for programs rather than for one or two engineers
Best for: Complex modernization where method matters as much as code
Trade-off: Consultancy rates, and engagements are scoped rather than staffed by the hour
Best for: Short senior engagements where speed matters more than rate
Trade-off: Among the more expensive marketplace options, and minimum commitments apply
Best for: Scaling several remote engineers at once
Trade-off: Matching is heavily automated, so screening depth varies by role
The question that filters AI vendors fastest is how they know a change is an improvement. Teams that have shipped answer with an evaluation set, a scoring method, and a regression run before release. Teams that have not answer with a demo. The difference costs you months, because a feature that cannot be measured cannot be improved safely.
Ask about cost before architecture. Token spend at real usage decides whether an AI feature is a product or a science project, and the model choice, context strategy, and caching are all cost decisions. A partner that models this in the proposal is doing the work; one that says it depends is deferring your budget risk.
Red flags that should end the conversation
A production feature with retrieval and evaluation typically runs $25,000 to $80,000. Demos cost a fraction of that, which is exactly why they mislead teams on timeline.
By delivery model and buyer fit, not by ratings. Every provider is assessed against the criteria listed on the page, and nobody is given an invented score.
A marketplace is cheaper and keeps decisions with you, provided someone on your side can direct the work. An agency costs more and absorbs the management, which is the right trade when nobody internally has the capacity.
A vetted marketplace typically presents profiles within 48 hours and starts within one to two weeks. Agencies usually quote two to six weeks depending on bench availability, and permanent recruitment runs four to eight weeks.
How we do this work
Generative AI Development Company →Hire directly instead
By city
Vetted engineers matched to your stack in 48 hours. No recruitment fees, $0 until you hire.
🇺🇸 Trusted by companies across the United States