Despite rapid progress in generative AI, companies face three major blockers when scaling beyond proof-of-concepts:
LLMs and quick hacks fail real-world performance and reliability targets.
Systems can't reason over enterprise data or handle domain constraints.
Internal teams lack the depth in ML, reasoning, and deployment disciplines.
Identify technical bottlenecks and root causes.
Redesign data and model stack for real-world demands.
Build hybrid systems and production pipelines.
Deploy, evaluate, and continuously evolve models.
We're a veteran AI/ML team with decades of hands-on experience in machine learning, Bayesian reasoning, knowledge systems, and large-scale production deployments.
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