The Quiet Revolution in Enterprise AI: Why Context is the Real Bottleneck

As enterprise AI initiatives stall in pilot purgatory, a startup argues the real problem isn't the models - it's the invisible infrastructure no one wants to build. Contextual AI CEO Douwe Kiela claims the bottleneck in enterprise AI adoption is 'context' - access to proprietary data - not the models themselves.

'The model is almost commoditized at this point. The bottleneck is context,' Kiela said. The company's Agent Composer platform offers three workflows: pre-built agents, natural language workflow description, and drag-and-drop interface. It supports OpenAI, Anthropic, Google models plus Contextual AI's 'Grounded Language Model.'

Customer claims include reducing 8-hour root-cause analysis to 20 minutes and 60x faster issue resolution. The platform's hybrid deterministic/dynamic agent architecture competes with DIY solutions by addressing both structured and unstructured tasks.

Performance benchmarks show top results on Google's FACTS benchmark for hallucination-resistant outputs.

For technical teams evaluating Agent Composer, the 'unified context layer' can be audited by mapping their engineering documentation against the platform's context management architecture.

This involves verifying how proprietary data flows through the system and comparing self-reported metrics with independent validation methods like A/B testing against existing workflows.