AI development costs are opaque. Vendors quote ranges so wide they are useless. This guide gives you the actual numbers by system type, what drives cost in each category, and what a realistic budget looks like for a mid-market business starting its first AI project.
The proof of concept: $5K to $25K
A proof of concept is a working prototype on your real data, scoped to one use case, delivered in 2 to 4 weeks. Cost ranges from $5K (simple, single-system integration, one AI step) to $25K (multi-system integration, complex workflow logic, compliance requirements). This is the fixed-scope engagement that answers the question 'will this work for us' before committing to a full build.
What drives PoC cost up: multiple system integrations, compliance review requirements, complex data cleaning, proprietary or difficult-to-access data sources.
AI voice agent development: $15K to $80K
An AI voice agent that handles inbound calls, books appointments, and integrates with a CRM or practice management system runs $15K to $40K for a standard deployment. HIPAA-compliant voice agents for healthcare add $10K to $25K in compliance architecture and BAA structuring. High-volume enterprise deployments with custom telephony (SIP trunking, contact center integration) start at $50K.
Ongoing costs: platform fees ($0.10 to $0.30 per minute of call volume), LLM inference costs (typically $0.05 to $0.15 per conversation), infrastructure, and maintenance. At 1,000 minutes per month, running cost is roughly $200 to $450/month. At 10,000 minutes, $1,500 to $4,000/month.
See also: How Much Does an AI Receptionist Cost and How Much Does an AI Voice Agent Cost for detailed per-use-case breakdowns.
Workflow automation: $15K to $100K
A single-workflow AI automation — invoice processing, support ticket routing, lead qualification — runs $15K to $40K to build. Multi-workflow deployments covering an entire department's operational stack run $50K to $100K. Enterprise multi-system deployments with compliance controls, SOC 2 requirements, and complex orchestration start at $150K.
Ongoing costs: platform fees ($29 to $800/month for n8n, Make, or Zapier depending on volume), AI API costs ($0.01 to $0.10 per automated task), and engineering time for maintenance (typically 4 to 8 hours per month for a stable automation).
RAG system development: $15K to $200K+
An entry-level RAG system — single knowledge base, standard retrieval, one use case (customer support or internal Q&A) — runs $15K to $30K. Mid-scale enterprise RAG with hybrid retrieval, multiple data sources, and access controls runs $35K to $80K. Full enterprise knowledge base with compliance controls, multi-department deployment, and live data sync starts at $100K.
What drives RAG cost up: number of data sources and ingestion complexity, compliance requirements (HIPAA, SOC 2), retrieval quality tuning for specialised domains, real-time data sync (vs. batch update), and access control granularity.
MCP server and AI agent development: $20K to $150K
Model Context Protocol (MCP) servers that expose enterprise tools to AI agents, and multi-agent systems built on frameworks like LangGraph, cost $20K to $50K for a standard deployment. Complex agentic workflows with multiple specialised sub-agents, tool orchestration, and enterprise integrations run $75K to $150K.
What drives total AI development cost
Integration complexity is the single largest cost driver. A workflow that connects to two well-documented REST APIs is dramatically cheaper to build than one that requires a custom integration with a legacy system, an undocumented internal API, or a vendor that requires a partnership agreement (like the Dentrix or Salesforce integration process). Budget 30 to 50% of total project cost for integration work.
Compliance requirements are the second largest driver. HIPAA adds $15K to $30K in architecture, BAA structuring, audit logging, and PHI isolation. SOC 2 alignment adds $20K to $50K. Regulated industries should budget these explicitly rather than treating them as scope creep.
Data quality is the most underestimated driver. AI systems trained on inconsistent, poorly structured, or incomplete data produce inconsistent outputs. Data cleaning, normalisation, and preparation often add 20 to 40% to project cost when not scoped upfront.
How to get a fixed quote
The only way to get a reliable quote for AI development is a discovery session followed by a fixed-scope PoC. Discovery reveals the actual integration complexity, data quality, and compliance requirements. The PoC validates the technical approach. After both, you can price the full build with precision rather than with assumptions.