How long does an AI project take? From feasibility to production
From a proof of concept in 4–6 weeks to a production AI solution with data integration in 3–6 months.
AI Project Duration
The short answer
AI proof of concept: 4–6 weeks. Production chatbot / RAG knowledge base: 3–6 months. Custom ML models with dedicated training: 6+ months – data quality is the biggest time factor.
The short answer: an AI proof of concept – say, a chatbot prototype or a first RAG knowledge base on test data – stands in 4–6 weeks. A production solution with proper data integration, permissions and quality assurance takes 3–6 months. Custom machine-learning models with dedicated training take longer.
AI projects differ from classic software development: model integration is often the smallest part – data preparation, prompt and retrieval quality, and evaluation drive the schedule. For budgets, see the AI cost calculator and the AI knowledge base calculator; since 2025, EU AI Act compliance also belongs in the project plan.
The phases of an AI project on the timeline
| Phase | Duration | Scope |
|---|---|---|
| Use-case definition & data check | 1–3 weeks | Sharpen the use case, review data sources, assess feasibility and privacy (GDPR, AI Act). |
| Proof of concept | 3–6 weeks | Prototype on real data: model choice, first prompts or retrieval pipeline, evaluation of answer quality. |
| Data preparation & integration | 4–12 weeks | Structure documents, map permissions, connect existing systems (ERP, DMS, intranet). |
| Evaluation & refinement | 2–6 weeks | Systematic quality testing, hallucination control, feedback loops with domain users. |
| Rollout & operations | 2–4 weeks | Go-live, monitoring, user training and handover to operations with cost control. |
What really drives AI project duration
These factors make the difference between 6 weeks and 6 months:
- Data situation: structured, current documents accelerate; scattered legacy PDF scans slow massively.
- Quality bar: an internal assistant forgives more than a customer-facing chatbot with liability questions.
- System integration: connecting ERP, DMS or ticketing brings classic interface effort.
- Compliance: GDPR assessment and AI Act classification (transparency duties, risk class) belong in the schedule.
- Evaluation: without systematic answer testing, refinement never ends – build test datasets early.
Plan timeline and budget together
In a free initial consultation we clarify scope, milestones and realistic dates for your project – made in Germany, from Leer in East Frisia.
FAQ
FAQ: AI Project Duration
How long does developing an AI chatbot take?
A prototype on your content stands in 4–6 weeks. A production chatbot with data integration, escalation logic and quality assurance typically takes 3–5 months – depending on data situation and integration requirements.
How long does a RAG knowledge base take?
With well-structured documents: 6–10 weeks to a usable version. Scattered legacy content, permission concepts and multiple source systems extend this to 3–6 months.
Why do AI projects start with a proof of concept?
Because answer quality on your real data is hard to predict upfront. The PoC clarifies within weeks whether the use case holds – before budget flows into integration and rollout.
What delays AI projects most often?
Data quality and access: missing structure, outdated documents, unresolved permissions. Model integration itself is rarely the problem – preparing the knowledge base costs the most time.
Does the EU AI Act need to be part of the schedule?
Yes. Transparency duties for chatbots already apply, and AI literacy training has been mandatory since February 2025. Risk classification and documentation belong in the project plan from the start – retrofitting is more expensive.