From first call to first wire in under 21 days.
Prism handles provenance, legal, and fulfillment end-to-end. Here’s exactly what happens at each stage.
Four steps. Full transparency at every stage.
Scope
~10 minFill the 5-minute intake or schedule a call. Our data strategists map your systems, estimate yield, and produce a written quote within 48 hours.
Identify source systems (CRM, ticketing, support, code repos, etc.)
Estimate data volume, freshness, and modality mix
Produce a preliminary valuation and licensing quote
No commitment required — scoping is free and non-binding
Connects to the systems you already use
Read-only connectors. No pipeline to maintain. No code to deploy.
What changes when you work with Prism
Dormant operational data generating $0 in value
Custom pipelines, months of integration work
Unclear IP rights, no standard licensing framework
Data leaving your environment without controls
6-12 months of negotiation and custom work
Recurring licensing revenue from frontier AI labs
Zero engineering — read-only connectors, we handle everything
Structured terms, retained IP, revocation with 90-day notice
CMK encryption, regional residency, SOC 2 / HIPAA compliance
Under 21 days from first call to first wire
The goal is not to replicate your business. The value comes from teaching AI systems how real work happens across industries, workflows, and edge cases, and not from exposing proprietary strategy or customer relationships. Data is processed with controls around privacy, attribution, and permitted use. Participation can be scoped narrowly: specific workflows, metadata layers, or historical datasets. Most frontier AI labs need broad, generalized real-world context. A single company’s dataset contributes as part of a much larger training ecosystem. You maintain control over what is shared, how it is used, and what is excluded.
Proprietary operational data is becoming a strategic asset in the AI era, and thoughtfully monetizing it is often seen as a sign of sophistication and market relevance. Leading companies already monetize APIs, infrastructure, analytics, and operational insights. Investors and acquirers increasingly ask what proprietary data advantage a company has in an AI-driven market. A structured data licensing initiative reinforces that your company has uniquely valuable operational systems. The positioning matters: this is governed AI collaboration, not selling customer data.
Yes. When data is exported, we first remove all personally identifiable information (PII) and then transfer it into our secure infrastructure for processing and use. Data is encrypted in transit and at rest using customer-managed keys. Regional residency guarantees ensure data stays in your designated geography.
Compensation varies by project and is shaped by factors like data type, scale, exclusivity requirements, and buyer needs. Smaller, more targeted datasets typically start around $50K, while large-scale enterprise data partnerships can reach $1M+. Highly specialized or high-demand data streams can exceed that range depending on ongoing usage and long-term value. You receive an upfront licensing fee at signing plus a revenue share on downstream use.
The most valuable datasets reflect how real work happens inside modern organizations. Typically sourced from Slack, Jira, Salesforce, email platforms, CRMs, data warehouses, and internal tools. Highest-value data includes: operational workflows across teams and functions, human decisions and escalation paths, expert reviews and corrections, multimodal business processes, internal tool usage and interaction logs, edge cases and real-world failure modes, and structured enterprise knowledge in context. Value is driven less by volume and more by how authentically it captures complex, real-world work.
Because the next bottleneck in AI is no longer internet-scale information — it is authentic, real-world operational data. Labs increasingly need examples of how work actually gets done across industries, teams, and systems. The most valuable training data now comes from expert workflows, decision-making patterns, edge cases, and operational context. Synthetic data still depends on real-world grounding to remain useful and accurate. High-quality real-world business data helps models become more capable, reliable, and commercially useful.