Every agent is scoped, not templated
We build custom AI agents around your process, so pricing depends on what that process actually needs, not a fixed package. Quoted in USD.
What's included
What's included, every time
A discovery call to understand your actual process
A written scope before any build work starts
The agent built and tested against real cases from your business
Deployment and integration with the tools you already use
Support after launch as your process changes
What changes
What actually changes the price
Four things drive most of the difference between projects, we walk through all of them on the discovery call before writing a scope.
Complexity of the process
A single document workflow costs less to build than a multi-step process across several teams.
Integrations required
Connecting to your existing CRM, email, or internal systems adds scope beyond a standalone agent.
Data and document volume
Higher volume and more varied formats mean more testing before something goes live.
Ongoing support level
A one-time build differs in cost from an agent we monitor and maintain long-term.
Why it matters
Why AI agent pricing isn't one-size-fits-all
Navigating AI agent development pricing requires understanding that enterprise automation is rarely a one-size-fits-all commodity. Rather than relying on rigid, generic templates, custom AI agent development focuses on building background systems tailored precisely to existing operational workflows.
No fixed packages
We don't publish flat-rate tiers, they'd force your project into a shape that isn't yours. Every quote reflects the process you actually described to us, in USD.
Project categories
Three Broad Categories of AI Agent Projects
Every project we take on falls somewhere on this spectrum. It's a useful way to think about scope before we talk specifics, not a menu of packages to pick from.
Basic Prototypes & Single-Task Agents
Focused on automating a singular, well-defined task with minimal API integrations, straightforward prompt engineering, and basic cloud infrastructure.
Advanced Workflow & RAG Knowledge Agents
Incorporating Retrieval-Augmented Generation, proprietary database connections, and multi-step reasoning to handle complex document processing and customer interactions.
Enterprise Multi-Agent Systems
Large-scale deployments orchestrating multiple specialized agents across departments, with advanced API integration, custom LLM fine-tuning, and stringent security protocols.
Cost drivers
Key Cost Drivers in Custom AI Agent Development
Evaluating AI agent development cost involves analyzing several distinct technical and operational components that dictate the overall investment required for a successful production rollout.
Infrastructure, Model Selection, and API Costs
Choosing between proprietary frontier models and optimized open-source alternatives directly impacts both upfront setup costs and ongoing operational efficiency, alongside robust API integration with existing CRMs, ERPs, and document repositories.
Complex Workflows and RAG Knowledge Integration
Implementing RAG knowledge agents requires meticulous data collection, cleaning, and vector embedding. Complex automation rules, exception handling, and multi-turn reasoning add development hours that directly influence the final budget.
Beyond initial software development, maintaining production-grade autonomous agents involves recurring operational expenses, ongoing LLM token consumption, vector database hosting, continuous model evaluation, and regular system updates. Properly forecasting these ongoing expenses ensures long-term ROI without unexpected budgetary strain.
FAQ
Frequently Asked Questions About Pricing
Tell us the process, get a real number.
Describe what you want automated in the chat on our homepage, or reach out directly. We'll give you an honest quote, not a sales pitch.
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