5YF Episode #34: Contextual AI CTO Aman Singh

Future Of Work: Context And Memory

subscribe and listen today

Today we dive into the future of AI in the enterprise.

I sat down with Aman Singh, CTO and Co-founder of Contextual AI, to explore how enterprise AI is evolving from demos to production-grade systems. Aman’s insights reveal a future where language models are orchestrators, enterprise systems are safeguarded against hallucination, and the enterprise AI ecosystem diverges from its consumer counterpart.

We’re building systems that don’t just automate tasks — they learn, adapt, and grow with your organization.

The conversation surfaced three transformative trends that will define enterprise AI over the next five years.

My 5 Year Outlook:

• LLMs Are The Universal Interface For Orchestrating Specialized Models And Agents: Humans use language to communicate making LLMs our natural first engagement layer for AI.‍
• Grounded Language Models (GLMs) and RAG Will Eliminate AI Hallucinations in the Enterprise: ‍The enterprise won’t accept misguided answers, luckily new technology is grounded AI replies in their data.‍
• Consumer AI Consolidates Vs Enterprise AI Proliferates: Enterprise AI is going down a very different path to consumer.

Curious? Read on as I unpack each below 👇🏼

LLMs Are The Universal Interface For Orchestrating Specialized Models And Agents

The future isn’t one monolithic model doing everything. It’s a constellation of specialized agents — each with a specific purpose, guided by a planner that knows your company’s context

Large Language Models (LLMs) are evolving into the universal interface through which humans interact with complex AI systems. Because natural language is the most intuitive, accessible medium for human interaction, LLMs are perfectly positioned to become the primary UX layer — enabling users to instruct, query, and coordinate a diverse array of specialized models and agents.

In this vision, LLMs are not just processing language; they are interpreting human intent and delegating tasks to other systems, including retrieval agents, world models, and domain-specific AIs. As Aman Singh explained, the power of LLMs lies in their ability to act as “the planner” — understanding user requests, breaking them down into sub-tasks, and routing those tasks to the right agents.

Companies like Adept are already building agent ecosystems that respond to natural language instructions. Meanwhile, Contextual AI is developing systems where LLMs act as orchestration layers, retrieving contextual data, coordinating agents, and optimizing performance through feedback loops. This paradigm is especially critical for enterprise environments where context, accuracy, and specialization are essential.

The future enterprise stack will increasingly rely on LLMs to serve as the connective tissue between human users and a diverse array of AI tools. Whether it’s accessing a specialized world model, deploying a retrieval system, or orchestrating a workflow across multiple agents, the LLM will be the central interface managing it all.

In short, LLMs are becoming the conductor of the AI orchestra — where agents and models are the instruments, and language is the baton.

Aman Singh, CTO & Co-Founder of Contextual AI

Contextual AI helps enterprises deploy AI and agents with accuracy, control, and safety. Founded by the team that pioneered Retrieval-Augmented Generation (RAG), Contextual is building next-generation AI that is deeply specialized to each business — answering with the context of your organization, not just the internet. With customers like Qualcomm and over $100 million raised from Greycroft, Lightspeed, Bain Capital Ventures, and Nvidia, Contextual is fast becoming one of the most important startups in enterprise AI.

Aman Singh is co-founder and CTO, he leads the company’s technology development and brings a deep background in research engineering. He previously worked at Hugging Face, a leading force in open-source AI, and at Meta’s FAIR lab, where he contributed to the development of RAG and cutting-edge multimodal models. He holds a master’s in computer science from NYU, where he focused on the intersection of language and vision.

Grounded Language Models (GLMs) and RAG Will Eliminate AI Hallucinations in the Enterprise

One of the biggest pain points for enterprises deploying AI is the problem of hallucinations. In consumer AI, minor inaccuracies are acceptable; a chatbot that suggests the wrong movie or provides a slightly incorrect fact can be forgiven. But for enterprises making financial decisions, legal evaluations, or critical diagnostics, mistakes are catastrophic.

Enterprises don’t want a chatbot that’s helpful — they want one that’s accurate. If it doesn’t know, it should say it doesn’t know.

Contextual AI’s approach is radically different from generic LLMs like GPT-4. By implementing Grounded Language Models (GLMs) — systems that only respond when the necessary context is available — Aman’s team is creating AI that understands its own limitations. The RAG 2.0 framework provides a retrieval system that is not only context-aware but continually optimized through feedback loops.

The value of these safeguards is already evident in Contextual’s work with Qualcomm, where their models are tuned to respect privacy rules, document attribution, and domain-specific expertise. Aman explained that this architecture doesn’t just prevent hallucinations; it offers a clear audit trail, allowing enterprises to trace an answer back to its source.

Going one step further is then prioritizing the top expertise in the organization so that their knowledge, often tribal in nature, is surfaced ahead of others. For Contextual AI this is their re-ranker system but it can be seen as a wider movement across the enterprise as they model their data to better represent their organizational structure.

‍

Consumer AI Consolidates Vs Enterprise AI Proliferates

Consumer LLMs will consolidate because they rely on massive distribution. Enterprise AI, meanwhile, will continue to fragment and specialize.

What is becoming clear is that AI in the enterprise can no longer be viewed via the same lens as consumer-facing AI like Chat-GPT. Over the coming years there is a divergent path that points to consumer AI being increasingly consolidated amongst the dominant players who have distribution, data access, compute, and brand: OpenAI, Google, LLaMa. As all of the internet data corpus is leveraged data doesn’t become the clear differentiator rather product and distribution is.

But in the enterprise, the opposite is happening. Each company has unique processes, proprietary data, and specialized needs. According to Aman, Contextual AI is building custom agents designed to work within these specific contexts, allowing enterprises to deploy AI models that fit their workflows, rather than forcing their workflows to fit a general-purpose model.

The proliferation of verticalized AI solutions is already visible. Companies like Harvey are building LLMs specifically for legal workflows, while others like Glean AI are developing accounting-specific agents. This specialization will continue to drive fragmentation in the enterprise AI landscape.

The consequence? While consumer AI consolidates under a few massive brands, enterprise AI will diversify into a rich ecosystem of tailored, purpose-built solutions designed for specific industries and companies.

Overall what surprises me most is how practical and near-term these changes are. We’re not talking about far-off AGI scenarios — we’re seeing concrete shifts in how enterprises design their AI systems today. Those that embrace specialization, context, and precision will emerge as the leaders of this new era.

Time to shine!

‍