Coworker.ai has launched OM2, an organizational memory layer designed to reduce the cost of enterprise AI by allowing systems to retain and reuse company context. The company says repeated context retrieval consumes a significant share of enterprise token budgets and forces AI agents to repeatedly reconstruct information they have already processed. Coworker.ai claims OM2 can cut context-related token spending by nine times while improving speed and answer quality.
Building a Persistent Enterprise Memory
OM2 continuously ingests information from more than 50 enterprise platforms, including systems such as Slack and Salesforce, and converts that information into what Coworker.ai describes as a permission-aware “neural graph” representing an organization’s knowledge. Rather than storing information solely as documents, the system extracts individual facts and connects them with relevant people, projects, customers, decisions, meetings, and other business relationships, creating a structured representation that updates as new data enters the organization. These facts are computed once and can then be reused across subsequent AI requests, reducing the need for different models or agents to repeatedly rediscover the same organizational context.
Lowering AI Infrastructure Costs
Coworker.ai says this approach allows OM2’s context layer alone to generate a ninefold reduction in token spending because previously processed organizational knowledge does not need to be reconstructed for every session. Customers can also combine OM2 with Coworker.ai’s Optimized Routing technology, which directs individual tasks toward models selected according to their requirements rather than relying on a single model for every workload. According to the company, combining organizational memory with model routing can produce cost savings of as much as 51 times compared with conventional enterprise AI architectures that depend heavily on repeated retrieval and tool calls.
Supporting Multiple AI Platforms
OM2 is designed to operate across existing enterprise AI environments rather than requiring customers to commit exclusively to Coworker.ai or a particular model provider, with integrations available for Claude, ChatGPT, Gemini, Perplexity, and custom agents. Coworker.ai says organizations can use the same underlying memory across different applications and AI systems through native products or headless integrations using MCP, helping prevent organizational knowledge from becoming fragmented between different tools. The company also says access controls are embedded into individual facts and relationships within isolated single-tenant infrastructure, with its security framework supporting SOC 2, GDPR, and CASA Tier 2 requirements.
Enterprise Adoption and Company Strategy
Coworker.ai says its technology is already used by more than 300 companies, including emergency response technology provider RapidSOS, where the platform is being used to bring together information previously distributed across Salesforce, Slack, and meeting records. Anna Waring, Director of Revenue Operations and Systems at RapidSOS, said Coworker.ai reduced the need for teams to manually assemble information from multiple systems before obtaining an up-to-date answer, illustrating the company’s broader effort to make enterprise knowledge immediately accessible to AI tools. Coworker.ai was founded by former Uber executives Alex Calder and Bradford Church and is backed by investors including former Google executive Jeff Huber and Ramtin Naimi.
Coworker.ai is positioning OM2 as infrastructure for enterprises seeking to reduce AI costs while preserving organizational context across different tools and models. The launch reflects growing demand for AI systems that can retain company knowledge without repeatedly rebuilding it from underlying data sources. Its broader adoption will depend on whether customers can reproduce Coworker.ai’s reported efficiency and performance gains at scale.