From proof-of-concept to production
AI work moves in stages, and each stage answers a different question. A proof-of-concept answers 'can it even do this on our data?' and is often quick. Turning that into something real users depend on — with error handling, access control, testing, and monitoring — is where most of the calendar goes.
Timelines vary widely by scope, so treat any estimate as conditional. A single AI feature added to an app you already run is faster than a new AI product built from scratch.
- Proof-of-concept: often a couple of weeks to prove feasibility on your real data
- Production build: typically several weeks to a few months depending on scope
- Full AI product from scratch: longer, because you are also building the app around it
- A single feature added to an existing product is the fastest path to launch
- Evaluation and tuning add time but are what make the result trustworthy
What makes it faster or slower
The single biggest variable is your data. If it is clean, accessible, and well-organized, we move quickly. If it is scattered across PDFs, spreadsheets, and legacy systems, a chunk of the timeline goes to preparing it before any AI work starts.
Decision speed on your side matters just as much. Fast feedback on demos and clear ownership keep momentum; slow approvals and shifting requirements stretch any estimate.
- Speeds it up: clean, accessible data and a narrow, well-defined use case
- Speeds it up: existing app and infrastructure to build into
- Slows it down: messy or locked-away data that needs cleaning first
- Slows it down: high accuracy or compliance bars that demand heavy testing
- Slows it down: many integrations, or scope that keeps expanding mid-build
- Slows it down: slow feedback loops and unclear decision ownership
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Frequently asked questions
Can you build an AI feature in a week?
Sometimes a demo, rarely a production feature. A quick prototype can prove the idea, but the testing, guardrails, and integration that make it safe for real users take longer. We would rather set an honest timeline than rush something you can't rely on.
What's the fastest path to launch?
Pick one narrow use case, build it into a product you already run, and start with a proof-of-concept on your real data. Narrow scope and clean data are the two biggest accelerators.
How do we get a real timeline for our project?
Book a free consultation. Once we understand your use case, your data, and where it needs to plug in, we can give you a stage-by-stage estimate instead of a generic one.