Short answer: a focused, well-scoped AI system goes from kickoff to production in 3 to 8 weeks. That is not marketing — it is the range we actually deliver across 200+ deployments. The more useful answer is why some projects hit three weeks and others drag on for six months — because that gap is almost never about the AI. It is about the process around it.
The three phases (and how long each really takes)
Discovery — days, not weeks. Before a line of code you need one thing: a single, high-value use case with data that exists and a clear definition of done. We run this as a short scoping sprint, not an open-ended AI strategy engagement that bills for a quarter and produces a slide deck. If discovery is taking weeks, usually no one has picked the first use case yet.
Build — short sprints with working software at the end of each. The teams that move fastest demo working software every sprint instead of disappearing for two months. Senior engineers matter hugely here: a senior AI/ML engineer who has shipped the pattern before moves 3-5x faster than a junior figuring it out live.
Deploy — the phase most teams underestimate. A model in a notebook is not a model in production with monitoring, access controls, an audit trail and a handover your team can maintain. We treat deployment as part of the build from day one, shipping into a real environment (your cloud, private cloud, or on-premises) early.
Timeline by project type — with real examples
- A retrieval assistant / chatbot (RAG over your documents): ~3-5 weeks — the fastest, because the pattern is established. See our generative AI consulting and LLM development.
- A custom predictive model (demand forecasting, fraud, anomaly detection): ~4-8 weeks, most of it data engineering, not modelling — e.g. our manufacturing demand-forecasting build.
- Document / process automation: fast once documents are understood — our insurance statement automation and payment automation for 1,000+ delivery partners.
- A regulated or clinical system: genuinely longer, and it should be — validation is the long pole. Our cardiac-risk prediction platform needed the rigor healthcare demands. If a vendor promises a clinical model in two weeks, be worried.
Why AI projects actually take long (it is rarely the AI)
- No single, decided use case — trying to do AI instead of solving one specific problem. The #1 time sink.
- Data that is messy, siloed or does not exist yet — which is why we lead with data engineering; it is usually the real critical path.
- Scope creep — every mid-build addition resets the clock.
- No empowered decision-maker — sprints stall waiting for sign-off.
- Over-engineering — chasing 99% on v1 when 90% already beats the manual process and ships this month.
- Junior teams learning on your time.
How to make it fast
- Pick one narrow, high-value use case and define done before building.
- Staff it with senior engineers, or hire senior AI developers to embed with your team.
- Ship working software every sprint and deploy to a real environment early.
- Reuse proven patterns — 200+ deployments means most of the path is known.
- Resist the second use case until the first is live.
That is how we take most projects from scoping to production in 3-8 weeks — roughly 60-80% faster than a traditional engagement.
The honest caveats
Some things legitimately take longer: heavy regulation, strict data-residency requiring on-prem, genuinely novel research, or an organisation not ready to decide at the pace of weekly sprints. The extra time should go to validation and governance, not a discovery phase that never ends.
Want a straight answer for your use case?
Tell us the problem and the data you have and we will give you an honest timeline, usually on the first call. Book a call, or see how we deliver production AI in 3-8 weeks.
Ready to put a real timeline on your AI project?
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