AI for Startups (1-20 employees)
Move fast without burning cash. Get AI working in your product or operations within weeks, not months, with solutions sized for startup budgets and timelines.
Pain Points
Challenges you face
Limited Budget and Runway
Every pound matters when you are pre-revenue or early-revenue. AI investment needs to deliver measurable value quickly or it is money you cannot afford to waste.
No In-House AI Expertise
Hiring an ML engineer costs 80-120K per year — more than most startups can justify. Founders and early employees need to leverage AI without specialist skills.
Speed to Market Pressure
Competitors are integrating AI into their products. You need to match or exceed their capabilities to win customers, but you cannot afford a 6-month development cycle.
Build vs Buy Decisions
Should you build AI capabilities in-house, use APIs, or outsource? The wrong choice costs time and money you do not have.
Scaling from Prototype to Production
The demo works, but making it reliable, fast, and cost-effective at scale is a different problem that early-stage teams often underestimate.
Impact
Expected improvements
3-6 months in-house estimate
Time to AI-Powered Feature
Deliver in 2-4 weeks with expert help
80-120K annual ML hire
AI Development Cost
Achieve more for 10-30K project cost
Manual processes consuming founder time
Operational Efficiency
Automate 50-80% of routine operations
Use Cases
Top AI applications for you
Internal Buy-in
How to pitch AI to leadership
As a startup founder or early employee, you likely do not need to pitch AI to a board — but you do need to justify spend to co-founders or investors. Frame AI investment as buy vs build: 'We can hire an ML engineer for 100K or get a production-ready AI feature in 4 weeks for 15K.' Investors love AI in the product because it increases defensibility and valuation multiples. For operational AI, show time savings: 'I spend 10 hours per week on X — AI automation frees that time for sales and product work that grows revenue.'
Recommended for you
AI Sprint
Based on typical needs for this profile, we recommend starting with our AI Sprint engagement.
FAQ
Frequently asked questions
Yes. An AI Sprint starts from as little as a few thousand pounds and delivers working functionality in 2-4 weeks. You do not need to build a team, buy infrastructure, or commit to a large contract. Start with one high-impact use case and reinvest the savings into the next.
At 1-20 employees, building in-house rarely makes sense unless AI is your core product and you have ML expertise on the founding team. For everything else, an external partner delivers faster, cheaper, and with less risk. You can always bring capabilities in-house later as you scale.
Two categories: AI in your product (the feature that makes customers choose you over alternatives) and AI in your operations (automating the repetitive work that consumes founder time). Most startups benefit from starting with one of each — a product feature that differentiates you and an automation that gives you back 10+ hours per week.
Start with the simplest solution that works. Often that means an API call to an existing model rather than training a custom one. We help you find the minimum viable AI — the simplest, cheapest approach that solves the actual problem — and only add complexity when data proves it is needed.
We build with scale in mind from day one, using modular architecture and standard APIs. When you grow from 10 to 100 to 1,000 users, the AI system scales with infrastructure, not rewrites. We also plan for the transition from startup-friendly pricing tiers to enterprise-grade deployments.
Ready to get started?
Book a free strategy call and we'll map out an AI roadmap tailored to your needs.