EnterpriseLab: A Full-Stack Platform for developing and deploying agents in Enterprises
Ankush Agarwal, Harsh Vishwakarma, Suraj Nagaje, Chaitanya Devaguptapu

TL;DR
EnterpriseLab is a comprehensive platform that streamlines the development and deployment of AI agents in enterprise settings, integrating tool use, data generation, and training into a unified, efficient system.
Contribution
It introduces a full-stack, modular platform that unifies enterprise AI development stages, enabling privacy-preserving agents with high performance and cost efficiency.
Findings
8B-parameter models trained in EnterpriseLab match GPT-4o performance.
Inference costs reduced by 8-10x.
Models remain robust across enterprise benchmarks (+10%).
Abstract
Deploying AI agents in enterprise environments requires balancing capability with data sovereignty and cost constraints. While small language models offer privacy-preserving alternatives to frontier models, their specialization is hindered by fragmented development pipelines that separate tool integration, data generation, and training. We introduce EnterpriseLab, a full-stack platform that unifies these stages into a closed-loop framework. EnterpriseLab provides (1) a modular environment exposing enterprise applications via Model Context Protocol, enabling seamless integration of proprietary and open-source tools; (2) automated trajectory synthesis that programmatically generates training data from environment schemas; and (3) integrated training pipelines with continuous evaluation. We validate the platform through EnterpriseArena, an instantiation with 15 applications and 140+ tools…
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Taxonomy
TopicsScientific Computing and Data Management · Artificial Intelligence in Healthcare and Education · Machine Learning and Data Classification
