Stop shuffling data between AI tools — AI-native orchestration layer

Large enterprises and mid-market organizations are increasingly forced to "shuffle" data between dozens of point-AI apps, custom scripts, and generic integration platforms, creating brittle pipelines, duplication, and uncontrolled costs that fall on data engineering and security teams. This problem is especially acute at the roughly 600,000 mid+large global organizations for which a $60K annual contract value implies a $36.0B addressable market, and it manifests as slow model experimentation, inconsistent results, and auditability gaps across business functions. You could build an AI-native orchestration layer that treats embeddings and semantic search as the canonical data abstraction, automates schema-free mappings between tools, and performs policy-driven multi-model routing to optimize for cost, latency, and accuracy. The product would include managed connectors, observability and lineage, automated prompt selection, and a runtime that can broker between multiple LLMs and specialized AI services while enforcing enterprise access controls. This market is attractive now because API commoditization of models lowers the barrier to multi-model strategies, embeddings enable cross-tool interoperability, and the rapid proliferation of point-AI apps creates immediate integration pain that organizations are willing to pay to resolve; independent assessments score the market highly (Market Score 92/100, Revenue Potential 85/100). The timing aligns with customers who can realize $60K+ ACV if you demonstrate measurable savings and governance improvements in the first 6–12 months. To stand out you should focus on embedding-first data mapping, deterministic lineage and compliance features, and a lightweight but extensible runtime for model routing and prompt optimization, while acknowledging real challenges: integration complexity, a medium-competitive landscape, long enterprise sales cycles, and the need to earn trust from IT/security teams.