How Much Does Custom AI Development Cost in 2026? A Realistic Guide by Project Type
Detailed cost guide by project type — chatbots ($15K–$40K), RAG systems ($30K–$80K), AI platforms ($100K–$500K), AI agents ($20K–$60K). What drives cost and how to optimise.
Honest numbers from real projects
Most AI development cost guides give ranges so wide they’re useless (“$10K to $1M+”). Here’s what things actually cost based on the 100+ products we’ve delivered across legal, fintech, edtech, and other verticals.
By project type
A customer-facing AI chatbot grounded in your content (RAG-based FAQ, product assistant, intake bot): $15K–$40K for MVP, 3–6 weeks. Add $10K–$20K for CRM integration, multilingual support, or advanced analytics. Ongoing: $500–$2,000/month (LLM API + hosting).
A knowledge search system (internal documents, legal research, content archive): $40K–$80K for MVP, 6–10 weeks. This is our bread and butter — we’ve built these for legal publishers (AAA), law firms, and educational platforms. The cost range depends on corpus size, format complexity, and accuracy requirements. Ongoing: $1,500–$5,000/month.
A document processing pipeline (contract analysis, loan applications, compliance documents): $40K–$100K for MVP, 6–10 weeks. Costs scale with the number of document types and the complexity of extraction requirements. Ongoing: $1,000–$4,000/month.
An AI agent system (multi-step workflow automation — contract review, compliance monitoring, research): $25K–$60K for a single workflow, 4–8 weeks. Multi-workflow platforms: $80K–$200K, 3–6 months. Ongoing: $2,000–$8,000/month.
A full AI-powered platform (wealth management, educational, SaaS product with AI at its core): $100K–$500K+, 4–12 months. AdvisorEngine was at the high end and took years, but it started as a small project and grew. Most platforms don’t need to be that ambitious at launch. Ongoing: $5,000–$20,000/month.
What drives costs up
Compliance requirements (FCA, SEC, FERPA, HIPAA, EU AI Act) add 20–50% depending on the regulatory environment. Scanned document processing (OCR, layout analysis) adds $10K–$30K to any project that involves non-native PDFs. Multi-language support adds $5K–$15K per language for content processing, more for multilingual generation. Complex integrations (multiple APIs, legacy systems, custom protocols) can consume 30–40% of total development time. High accuracy requirements (95%+ precision, citation verification, anti-hallucination) add 30–50% compared to standard accuracy.
What saves money
Starting with an MVP that covers one use case, not five. We’ve seen the difference this makes hundreds of times — a focused $40K project that validates the approach beats a sprawling $200K project that tries to do everything.
Using managed services where possible. LLM APIs (Claude, GPT-4) instead of self-hosted models. Managed vector databases instead of self-hosted Qdrant. Cloud infrastructure instead of on-premise (unless compliance requires it).
Having a team that knows your domain. We charge the same hourly rate regardless of industry, but projects in legal, fintech, and edtech — where we have deep domain expertise — take 30–40% less time because we’re not learning the domain on the client’s budget.
The discovery phase is the best investment
Before committing to a full build, invest 5–10% of your expected budget in a discovery phase: scope definition, architecture design, data audit, and risk assessment. This typically costs $5K–$15K and takes 2–3 weeks. It dramatically reduces the risk of building the wrong thing.
“The most expensive AI project isn’t the one with the biggest budget — it’s the one that builds the wrong thing. A $5K discovery phase that reveals the right approach saves more money than any technical optimisation.”
Need a realistic estimate for your AI project? Contact us — we’ll scope it properly in a discovery phase before you commit to building.