Giving Enterprise AI the Right Business Context With Sphinx AI

In this episode of AI at Work, I speak with Rohan Kodialam, co-founder and CEO of Sphinx AI, about the difference between an AI system that sounds capable and one that understands how a business defines truth. Sphinx AI describes itself as an AI research company building human-interpretable context graphs that can support agents across different models and enterprise systems.

Rohan argues that consumer AI can feel impressive because many everyday questions are supported by information in the public domain. The situation changes inside a company. Questions about customer churn, product performance, acquisition costs, or operational efficiency depend on definitions, assumptions, and working practices that belong to that organization. A model may reach the data through an enterprise connector and still lack the knowledge needed to interpret it correctly.

The most revealing example comes from a retailer using a metric called revenue per store. One team divided revenue by every store in the estate. Another used only stores that were open on the relevant day. Both definitions were reasonable for their intended users, but an AI relying on retrieved documents could select one almost at random and return an answer that looked convincing. The error might remain hidden until somebody compared it with a manually prepared report.

Rohan says the problem can be resolved by making the missing business context explicit. In this case, the AI needed a short explanation describing both definitions and when each should be used. It could then consider whether the request came from a real estate or operations user and select the relevant metric. This is a small example of a much larger enterprise problem: the information that separates a useful answer from a costly mistake often lives in people’s experience rather than a formal system.

We discuss why retrieval augmented generation can struggle when company material is raw, contradictory, or unsynthesized. Rohan reports that Sphinx AI customers have seen accuracy increases of roughly 35 to 40 percent on existing enterprise benchmarks after curating context, with larger improvements in some cases. These are company observations rather than independent benchmark results, but they show the size of the gap Rohan believes a dedicated knowledge layer can address.

Context also changes. Rohan references an example in which an agent reportedly moved from 95 percent accuracy to 65 percent within a month because business processes and priorities had changed. His argument is that companies need context stewards who can review updates, approve definitions, reject unauthorized changes, and maintain an audit trail. He compares this with code review, where machine-generated work still requires accountable human oversight.

Security remains part of the design. Rohan says a context layer should explain what information exists while respecting role-based access controls. It should sit virtually above existing systems of record rather than copying sensitive data into a new repository. An agent might know that customer phone numbers exist while remaining unable to retrieve them. It can then tell the user that permission is required instead of inventing an answer or bypassing an established control.

Before allowing an agent to act on company data, Rohan recommends two forms of testing. Business experts should create a small gold-standard set of tasks they know well, while the company can build a broader benchmark from repeated work already visible in its operational history. Together, these tests can show whether the agent understands the decisions people actually make rather than performing well on a generic demonstration.

Does your organization have a shared, reviewable account of how it defines its most important metrics, rules, and working assumptions? Listen to the episode and share your thoughts.