.1TechnologyNovember 10, 2026 Karina Less Context, Better Answers: How Minimum Complete Context Cuts Enterprise AI CostsMost enterprise AI sends the model every connected data source, and the team pays in token costs, slow answers and wrong answers that no one can trace. Minimum complete context sends only what each task needs: 100 to 1,000 times less context. Here is how Iris.ai builds it in four steps, and what changes when the model reads less.
.2Iris.ai JourneyOctober 7, 2026 Karina What Iris.ai Does: From AI Researcher to AI HarnessIn 2016, Iris.ai announced "an AI Researcher" from a TEDx stage. Ten years later, it runs the same research on retrieval and evaluation in production across steel manufacturing, telecom, and automotive. The customers changed. The standard did not: AI that enterprises in expert domains can control, trace, and verify.
.3AnnouncementsOctober 7, 2026 Karina Welcome Iris.ai's CMO - Liana HakobyanEvery new chapter at Iris.ai starts with people who embody how we build. Today, that chapter continues with the appointment of our Chief Marketing Officer.
.4TechnologySeptember 29, 2026 Karina What Is AI Harnessing? A Definition for Enterprise AIMost enterprise AI pilots stall at expert review, when a specialist asks where an answer came from and nobody can say. AI harnessing is the discipline of controlling what an AI system is given and verifying what it produces. Learn what it is, how it differs from RAG, and the five questions every harnessed system can answer.
.5AI Across IndustriesSeptember 21, 2026 Karina Governance and Traceability for Agentic AI: Closing the Accountability GapEnterprises run AI agents in production faster than they build controls for them. Agents decide in the moment, based on what they retrieved and in what order, so an API log shows that an agent acted but never why. In regulated sectors, an answer you cannot trace is an answer you cannot use. Here is what it takes to reconstruct every agent decision.