AI and machine learning for your app
We integrate artificial intelligence into mobile and web products: cloud models, language APIs, automation, and on-device inference when you need lower latency and stronger privacy.
Written and reviewed by Wilson Daniel Ospina, Founder & Lead EngineerUpdated
What it costs
Typical timeline: 4–8 weeks
What the starting price covers
An AI or machine-learning capability added to a product you already have in production — chat, vision, recommendations, or an assistant — including model selection, evaluation, and the guardrails around it. It does not include building the product itself.
Fixed scope and a fixed price, agreed in writing before work starts. If discovery shows the build is larger than the floor, you get the revised number before you commit — not an invoice afterwards.
Prices are in US dollars and exclude Colombian VAT (IVA) and any withholding that applies to your jurisdiction.
Get a fixed quoteKey Features
- •Language model and agent integrations
- •Computer vision and image/video classification
- •Lightweight models and TensorFlow Lite–style edge deployment
- •Data pipelines and pragmatic MLOps for your product
- •Conversational UX and operational assistants
Benefits
- ✓Differentiation with measurable intelligent features
- ✓Less manual work for field and support teams
- ✓Architecture aligned with cost and privacy goals
Frequently asked questions
Straight answers about this service.
- How much does it cost to add AI to an app?
- Adding one AI capability to a product that is already in production starts at USD 4,000: model selection, integration, evaluation and the guardrails around it. It does not include building the product itself.
- How long does it take?
- Typically 4–8 weeks. We usually start with one small, measurable feature so you can see the impact before committing to a wider AI roadmap.
- Cloud model or on-device?
- Cloud language and vision APIs are the fastest route for most features. On-device inference makes sense when latency, cost per request or privacy matter more, for example when data should never leave the phone.
- What happens to our users' data?
- Which data a model may see is decided in the written scope, before any integration work. When privacy matters most, on-device inference keeps the data on the phone.
Ready to start?
Tell us what you want to build. You already know where the number starts; after a short discovery call we send a written estimate with fixed scope, a fixed price, and a timeline.
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