Agentic AI: Governed Autonomy at Enterprise Scale

We bring deep expertise in crafting Vertical Multi-Agent AI Systems, intelligent agents designed to automate specific roles within distinct industries, such as an Investment Analyst Assistant and Clinical Decision Support Agent. These systems are deployed as ready-made solutions or custom-built from the ground up.

We deliver Horizontal Multi-Agent AI Systems that tackle common challenges faced across sectors. Examples include Data Product Builder Agents, Coding Agents like PR Review Agents, and our proprietary application modernization solution - LIFTR.ai. By centralizing best practices across industries, these systems enable organizations to build, translate, and optimize applications rapidly. Enterprises benefit by accelerating innovation, reducing operational silos, and enabling teams to pivot to new initiatives with less friction.

Indium advances enterprise search capabilities through cutting-edge Retrieval-Augmented Generation (RAG) technologies, including GraphRAG, Multimodal LLMs, LLMs as a Judge, Pydantic validations, and agile feedback loops. These enhancements ensure highly accurate, contextually relevant search results aligned with business needs.

Organizations can leverage these advanced techniques to surface actionable insights swiftly, improving decision-making and reducing dependence on manual research.

Make your APIs accessible by LLMs and natural language by converting them to MCP Servers. These servers uphold security best practices authentication, RBAC, rate limiting ensuring enterprise-grade regulation. They automate routine choices and actions while centralizing logic and contextual prompts, reducing code duplication.

With Indium’s enhanced governance, enterprises experience smoother integration, and real-time intelligence embedded throughout business processes.

We create custom utilities for agentic AI including tools for prompt management, optimization, and memory (both short- and long-term) to supercharge agents’ capabilities.

These utilities enable tailored information retrieval and learning. Enterprises gain systems that not only execute tasks but also evolve dynamically, adapting to new requirements and operational nuances.

Indium refines Large Language Models (LLMs) using sophisticated techniques like Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). This process imparts niche skills and aligns agents’ behaviors to enterprise objectives.

By enabling ongoing feedback loops, agents consistently learn from real-world outcomes. Businesses benefit from AI that continuously improves, reducing error rates and amplifying impact over time.

We curate comprehensive instruction, demonstration, and comparison datasets to support post-training of LLMs during SFT and RLHF phases. Beyond model training, the team configures high-performance inference servers (e.g., vLLM), optimizes key-value caching, and selects the right GPU infrastructure to host open-source models on-prem.

Our security operations, including audits, red teaming, and prompt injection safeguards, ensure robust and compliant deployment. Enterprises enjoy seamless, secure rollouts of advanced AI capabilities, future-proofing operations and unlocking strategic value.