AI Product Development pricing.
AI product features vary a lot in cost depending on how much data needs to be processed, how much accuracy matters, and how deeply the feature needs to integrate with your existing product. Here's what actually drives the price.
What changes the number.
- Volume and format of source data (clean text versus scanned PDFs versus mixed formats)
- Accuracy requirements: a customer-facing feature needs more evaluation and guardrail work than an internal tool
- Depth of integration with your existing product, database, and authentication
- Whether real-time data sync is needed or a one-time ingestion is sufficient
- Number of distinct AI features versus a single focused capability
Included
- Data pipeline and ingestion setup
- Retrieval or integration architecture
- Full build of the feature, backend and frontend as needed
- Evaluation and testing before launch
- Documentation for your team
Not included
- Ongoing per-request API costs from the AI provider (billed directly to you at cost)
- Vector database or hosting infrastructure costs
- Large-scale data cleanup of source documents (scoped separately if needed)
- Ongoing feature expansion after launch (available as a follow-on engagement)
Example ranges, not fixed packages.
Single feature
Scoped after consultation
Adding one focused AI capability, like search or summarization, to an existing product
RAG knowledge system
Scoped after consultation
Building a full retrieval system over a document corpus with a chat or search interface
AI-native product build
Scoped after consultation
Multiple AI features architected together as the core of a new or rebuilt product
What affects how long it takes.
- How clean and well-organized your source data already is
- Whether this integrates into an existing codebase or is greenfield
- How many rounds of accuracy tuning the use case requires
- Whether a UI needs to be designed and built or already exists
What we need from you.
- Access to or samples of the source documents/data
- Access to your existing codebase and staging environment
- A clear description of what the feature should do and for whom
- Any existing brand or UX guidelines if a UI is involved
From conversation to fixed quote.
Initial call
We discuss the feature, the data involved, and your existing product architecture.
Data review
We look at samples of your actual data to gauge complexity and cleanup needs.
Scoped proposal
We send a fixed-scope proposal with price, timeline, and what's included.
Kickoff
Once approved, we start discovery and architecture work immediately.
Common pricing questions about AI Product Development.
Want a real number for your project?
Tell us what you need built. We will scope it and come back with a fixed quote based on your actual requirements.