AI prototype
$4,000-$10,000A constrained proof of concept with real examples, a useful interface and a clear validation decision.
Interactive AI development cost planner
How much does AI development cost?
Estimate a realistic budget for an AI assistant, document workflow, product feature or custom AI application based on its data, integrations, evaluation and risk.
About four minutes
No sales call required
Assumptions included
Step 1 of 8AI development estimate
Your planning range
This is not a binding quote. The data, evaluation target and smallest useful validation step still need a short review.
Useful starting points
Typical AI development investment ranges.
These are broad build ranges for focused custom solutions using modern models and software. Model usage, cloud services and ongoing operations are separate.
Knowledge assistant
$7,000-$20,000Grounded answers over business information with permissions, citations and quality evaluation.
AI workflow or product feature
$10,000-$32,000Document processing, generation or assistance integrated into real users and business systems.
Specialised AI system
$25,000-$65,000+Custom machine learning, computer vision or a higher-risk application with deeper data and governance work.
What changes the cost
A working demo is not a production system.
The expensive part is often not calling a model. It is preparing the right context, measuring quality and integrating uncertain outputs into dependable software.
Use case and success measure
A narrow valuable task creates a testable decision about whether the AI is good enough.
Data and system context
Knowledge quality, permissions and existing workflows determine what the model can use safely.
Model behaviour and evaluation
Structured outputs, retrieval, tools and specialised tasks need realistic test cases and monitoring.
Risk and human oversight
The cost of a wrong output determines the controls, auditability and review workflow required.
Before you estimate
AI development cost questions.
A useful range makes data, model usage, evaluation and production-risk assumptions explicit.
No. It is a planning range based on the use case, data, AI behaviour, integrations and risk level you select. A proposal requires validation of the available data and a measurable definition of useful output.
No. Many valuable systems use established models with good software, retrieval, evaluation and human oversight around them. Custom training is justified only when the problem and available examples support it.
Depending on the scope, it can include discovery, data preparation, prompts or model integration, retrieval, application development, evaluation, monitoring, permissions, deployment and a practical review workflow.
AI behaviour is probabilistic. Important outputs need realistic test cases, measurable quality checks, fallback behaviour and visibility when the system is uncertain or wrong.
No. Model tokens, vector databases, hosting, speech services and other external usage are recurring costs. I make those assumptions visible and design for cost control where volume matters.
Yes. Existing products are often the best starting point because the users, workflow and business data already exist. The AI feature can be introduced behind clear permissions, monitoring and gradual rollout.
Keep the range for planning with no obligation. If it fits, reply with the workflow, sample data or current product link and I will identify the smallest useful validation step.
Start with a realistic planning number
Estimate the likely cost of your AI solution.
Describe the use case, receive the assumptions and decide whether the range fits before starting a sales conversation.
Build my estimate