Coworker.ai introduces OM2, an organizational memory layer that aims to reduce enterprise AI token usage by nine times.
**Summary**: Coworker.ai is launching OM2, a comprehensive organizational memory layer that assimilates data from over 50 enterprise platforms and condenses it into precomputed, permission-based facts. Rather than recreating context for each AI inquiry, OM2 leverages structured insights across interactions, resulting in a 9x decrease in token expenses and 64% faster response times. With the addition of an optimized model routing system, total savings can reach up to 51x.
When traditional enterprise AI agents start up to respond to internal queries, they often lack essential context. This forces companies into an inefficient cycle of gathering data from numerous documents, Slack conversations, and CRM entries, which consumes over 50% of AI token budgets.
To address this issue, Coworker AI is introducing OM2, which improves how AI interacts with corporate information. Instead of reconstructing company context for each session, OM2 continuously gathers data from more than 50 platforms, creating a dynamic, permission-aware neural graph.
**Reducing Token Expenses with Precomputed Intelligence**: Traditional enterprise AI typically retrieves extensive text segments whenever a question is posed. In contrast, Coworker.ai’s OM2 utilizes a more refined method. As information circulates within the organization—such as completed transactions in Salesforce, product changes in Slack, or strategic decisions in meeting notes—OM2 extracts this data and converts it into specific, precomputed facts. These facts clearly outline who was involved, what decisions were made, and which accounts are affected. By computing this structured intelligence once and reusing it across queries, the AI avoids having to reconstruct context for every request. This transformation reportedly leads to a 9x reduction in token expenses, increases response times by 64%, and provides answers that users rate as higher quality 84.5% of the time.
**Reducing Costs and Vendor Dependencies**: Coworker also addresses the inflated costs associated with a uniform usage model. By incorporating its Optimized Routing engine, OM2 intelligently categorizes tasks. It assigns straightforward data retrieval to less expensive, faster models while reserving complex reasoning for advanced models. Coupled with the precomputed context, Coworker suggests that overall cost savings could be as high as 51x. This architecture allows companies greater data sovereignty by preventing them from confining their organizational knowledge within a single vendor’s system. OM2 operates as an independent intelligence layer, integrating seamlessly with existing tools like Claude, ChatGPT, Gemini, and custom internal agents through its Model Context Protocol (MCP).
“Token costs are skyrocketing, and much of today's AI fails to function effectively within a corporate environment,” remarked Alex Calder, Co-Founder and CEO of Coworker.ai. “OM2 learns and adapts to your business continuously while enforcing strict access permissions at each level. This is how we achieve a 9x cost reduction before even considering routing. Unlike many other AI providers, our goal is to help you use fewer tokens, not more.”
**Security and Practical Applications**: In the enterprise sector, the value of intelligence is directly linked to its security measures; for instance, a junior engineer querying an agent shouldn’t access sensitive information about the CEO's M&A discussions. OM2 addresses this concern by integrating access policies within every fact and connection in the neural graph. Hosted on isolated, single-tenant infrastructure compliant with SOC 2, GDPR, and CASA Tier 2 standards, the platform ensures users and agents can only access nodes they are explicitly permitted to view.
Initial users are employing the platform to connect disparate data sources. For example, at RapidSOS, the Revenue Operations team previously dedicated hours to manually cross-referencing Salesforce data and fragmented communication logs. “Now, the answer is instantly available and accurate as soon as we inquire,” stated Anna Waring, RapidSOS’s Director of Revenue Operations and Systems.
Supported by prominent Silicon Valley investors like Ramtin Naimi and former Google SVP Jeff Huber, Coworker.ai is focusing on a transformative change in the AI sector. While foundational model creators strive to expand context windows, Coworker is demonstrating that the key to unlocking enterprise potential lies not in larger models, but in enhanced memory capabilities.
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Coworker.ai introduces OM2, an organizational memory layer that claims to reduce enterprise AI token consumption by nine times.
Coworker.ai's OM2 transforms corporate knowledge into a permission-aware neural graph, reducing token expenditure by nine times and enhancing AI response times by 64%. The system directs queries through different models, achieving total savings of up to 51 times.
Coworker.ai introduces OM2, an organizational memory layer that aims to reduce enterprise AI token usage by nine times.
Coworker.ai’s OM2 precomputes organizational knowledge into a permission-aware neural graph, reducing token expenditures by 9 times and accelerating AI response times by 64%. The system directs queries through various models to maximize total savings up to 51 times.