Crystil
Crystil is an AI visibility platform that connects AI usage to teams, apps, and customers with a single line of code, then reconciles that usage with provider invoices. The platform measures cost, waste, prompt quality, value, and security compliance, with SOC 2 compliance verified by an independent third party. It is free until it identifies real cost savings for the customer.
- Data & Analytics
- Artificial Intelligence
- Enterprise Software
- Software Only
Funding
Funding not disclosed
Founders
Founder details are not available yet.
Product
Problem
Organizations deploying large language models often face opaque, aggregated AI spending that is difficult to attribute to specific teams, applications, or customers. Without granular cost visibility, companies struggle to identify waste, separate experimentation from production usage, and reconcile internal usage with provider invoices.
Solution
Crystil connects AI usage to teams, apps, and customers with a single line of code, then reconciles that usage with provider invoices. The platform shows cost breakdowns by team, application, and customer, making it possible to separate experimentation from shipped work. It also measures prompt quality by surfacing patterns of repeated and failed requests, helping teams improve results. Crystil requires no pilot, procurement, or upfront risk, and is free until it identifies real cost savings. Users can control what data they share and what remains internal. The platform is SOC 2 compliant and audited by an independent third party.
Target Audience
Primary customers are engineering, finance, and operations leaders at companies running LLM-based products who need to understand and control AI infrastructure spending, as well as compliance and legal teams preparing for AI-related state regulations.
Features
- One-line code integration for connecting AI usage without complex setup
- Cost attribution by team, application, model, customer, and other dimensions
- Provider invoice reconciliation to match internal usage with billed amounts
- Separation of experimentation spend from production workload costs
- Prompt quality monitoring that identifies repeated and failed request patterns
- Value and ROI tracking that ties token spend to outcomes across customer usage and coding sessions
- Waste detection for idle seats and runaway customer usage of agents
- Security and compliance features that surface unapproved AI use and maintain an audit trail for HR, legal, and the board
- Granular data sharing controls so users decide what information is shared
- SOC 2 compliance verified by an independent third party