Make your data productive and profitable.
Prism acquires real-world operational data from companies and contributors to power frontier AI while you retain full control of your data and rights.
Two ways to license your data.
Work documents
Coding sessions, scientific workflows, financial modeling, research traces, creative production, field operations, and day-to-day knowledge work. Structured into high-signal training data for frontier models.
- ›Upload your data to see how much it's worth
Operational data
Transforming operational systems like Salesforce, Jira, Slack, SAP, CAD, and financial workbooks into environments for training enterprise AI systems.
Data categories we're buying right now.
All categories below have confirmed buyer interest. Urgent means a funded, time-sensitive contract awaiting supply.
Key verticals that we focus on
Discover the industries where we have buyer commitments and active acquisition mandates.
Your operational data is dormant equity.
Your business systems continuously capture how your business runs, from knowledge work execution across Salesforce, Jira, SAP, and ServiceNow. It is real operational signal, but it usually stays locked inside tools built for running the business, not monetizing it.
Licensing that signal turns it into recurring value without changing day to day operations. With clear governance, contracts, and security controls, you keep full ownership and oversight while enabling limited, consented use that generates ongoing returns from data you already produce.
We license based on data type, scale, exclusivity, and buyer demand, with smaller targeted datasets starting around $50K and large enterprise partnerships reaching $1M+ depending on long-term value and ongoing usage.
Governance and control are central to our model. Data is filtered, anonymized, aggregated, or permissioned before use.
Most teams can export the needed operational data with existing tools. We handle ingestion, normalization, and PII scrubbing so your team avoids building pipelines or handling sensitive processing.
Licenses are structured as exclusive by default and tailored to the scope, sensitivity, and strategic value of the data.
Read-only connectors to your existing stack
No pipeline to build, no code to deploy, no engineering burden on your team.
From first call to first wire in under 21 days.
Scope
~10 minFill the intake form and 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
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.