Elastic and OpenAI collaborate to bring advanced intelligence to corporate unstructured data

The utility of AI agents is directly proportional to the business context on which they can reason. Much of the critical information that organizations own and that AI agents need is scattered in unstructured data, such as documents, tickets, logs, metrics, traces, and security alarms. These data are constantly changing and governed by different authorizations.

Today, Elastic and OpenAI have announced an expansion of their collaboration to bridge this gap, helping organizations build production-ready AI applications using OpenAI models with Elasticsearch. OpenAI provides advanced reasoning capabilities, while Elastic is specifically designed for unstructured data, offering a level of retrieval, governance, and operability that helps models find relevant and real-time contexts.

Three main goals of the collaboration

The collaboration will focus on three main outcomes for customers:

  • Context-aware AI agents: that retrieve accurate, permission-aware business knowledge at scale, with fewer tokens.
  • Agentic observability: that correlate telemetry and accelerate root cause investigation for SRE (Site Reliability Engineering) teams.
  • Agentic security operations: that accelerate detections, help triage alarms, enable faster responses, and proactively recommend detection rules to close security gaps.

Elasticsearch: the context layer for agentic AI

In the era of agentic AI, the value of a company lies in its context: data, history, decisions, and institutional knowledge scattered across unstructured data. OpenAI's internal data agent demonstrates that reliable results depend on combining multiple layers of context. Elasticsearch, as a context layer, provides agents with persistent and governed memory to retrieve what is relevant and maintain useful knowledge across interactions, grounding every response and action in the business context.

The practical challenge of context is a retrieval problem: selecting the right information at the right time with the correct permissions. Elasticsearch combines lexical and vector search, semantic re-ranking, filters, and access controls in a single platform. In Elastic's internal tests, using Elasticsearch for organizational memory, a recall of 0.89 was achieved with complete data protection among users.

Economics are as important as accuracy. In a controlled experiment using the BrowseComp-Plus dataset, Elastic's precomputed Knowledge Indicators reduced input token usage by up to 75% compared to the standard retrieval-augmented generation (RAG) baseline, while response accuracy increased from 60% to 92%. Results vary depending on the workload, but the direction is clear: better retrieval can improve both reliability and costs.

Elastic Agent Builder and integration with OpenAI

Elastic Agent Builder can be used with OpenAI models and agents to enhance context and provide greater relevance and efficiency. Agent Builder tools can be exposed to OpenAI agents via MCP, Skills are available for Codex, and Agent Builder supports GPT models for reasoning and tools.

As models advance, context is what enables companies to achieve real ROI and maintain a unique advantage in the world of agentic AI. Together, OpenAI and Elasticsearch provide developers with a practical foundation for agents that can create, manage, and retrieve context on governed business data.

Elastic Observability: agentic investigations powered by unstructured telemetry

The operational context lies in unstructured telemetry data. Agentic applications introduce additional data such as prompts, tool calls, model responses, token usage, traces, and retrieval steps. Elastic Observability helps correlate this telemetry and accelerate root cause analysis for SRE teams.

Elastic's integration with OpenAI collects API usage metrics and organizational audit logs. Teams can correlate token consumption, model activity, API calls, and administrative changes with application latency, errors, deployments, and infrastructure health, using a single data platform and a single query language from the application level to the model level.

The future of the collaboration

This collaboration represents a significant step towards integrating advanced AI capabilities into business workflows, improving efficiency, security, and decision-making across various industries.

The future of the collaboration

The collaboration between Elastic and OpenAI represents a fundamental step towards integrating advanced AI capabilities into business workflows. This synergy not only improves operational efficiency but also opens new possibilities for sectors such as cybersecurity, data analysis, and IT infrastructure management. As technologies evolve, the alliance between these two companies could redefine the standards for how organizations use artificial intelligence to solve complex problems.

Industry impact

The adoption of these technologies is already showing significant results in various sectors. For example, in the financial sector, the integration of AI agents with Elasticsearch allows the analysis of enormous amounts of transactional data in real time, improving fraud detection and optimizing decision-making processes. In telecommunications, advanced observability capabilities help monitor and resolve network problems with unprecedented speed.

In the healthcare field, the analysis of unstructured data such as medical records and laboratory results can lead to more precise and personalized diagnoses. Additionally, cybersecurity greatly benefits from these innovations, with the ability to identify and respond to threats proactively, reducing response times and improving the resilience of critical infrastructures.

Challenges and opportunities

Despite the advantages, the large-scale adoption of these technologies presents some challenges. Managing unstructured data requires robust infrastructure and specialized skills. Additionally, data security and privacy remain an absolute priority, especially in highly regulated sectors such as finance and healthcare.

However, the opportunities are immense. The ability to integrate advanced AI models with corporate unstructured data can lead to unprecedented innovation, transforming not only internal processes but also the customer experience. Companies that adopt these technologies will be able to gain a significant competitive advantage, positioning themselves as leaders in their respective sectors.

Conclusions

The collaboration between Elastic and OpenAI is an example of how technological innovation can revolutionize business workflows. With the integration of advanced AI capabilities, organizations can tackle complex challenges with greater efficiency and precision. As this partnership continues to evolve, it is clear that the future of artificial intelligence in the business realm is bright and full of potential.

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