An AI agent trying to answer "what did we invoice this customer" runs into a problem most demos never show: the honest answer isn't one number. There's what you invoiced in U.S. dollars, what that converts to in local currency, and the tax implication layered on top. There's a purchase order that got canceled and a separate one that got executed for the same order. To a system built to give a clean answer, that looks like corrupted data. To Naveen Neelakantam, product and technology strategy lead in Everpure's Office of the CTO, it's just what business data actually looks like — and building AI that tries to erase it is where a lot of agentic pilots start quietly failing.
"Contradiction is part of the data landscape," Neelakantam said in an interview about Everpure's approach to AI-ready data. "Those contradictions are meaningful. You might see a canceled purchase order plus the executed purchase order — we want to link all of those in the knowledge graph and say, if you're asking about POs, some of them are canceled, some are executed."
That's a deliberately different posture from a lot of "clean your data for AI" pitches, which treat conflicting records as noise to filter out before a model ever sees them. Everpure's approach, built on its Everpure Data Intelligence platform — acquired via 1touch.io, and already running in production for financial and insurance customers on-premises, not a fourth-quarter beta, per Neelakantam — keeps the mess and surfaces it instead.
The mechanics are worth understanding if you're the one building the agent. Data Intelligence scans structured sources like Snowflake, Salesforce, and SAP alongside unstructured ones — invoices, scanned purchase orders, object stores — looking for entities: names, addresses, PO numbers. "In these invoices or this OCR document, I think this is a name, I think this is a PO number, I think this is an address," Neelakantam said. It then checks those candidate entities against a canonical source a business has designated as reliable, such as a Salesforce record, and links matches into a knowledge graph. Where two systems disagree, both versions stay in the graph, tagged as related, rather than one getting silently discarded.
That matters more as agents start reasoning across systems instead of staying inside one application's walls. Neelakantam framed the shift as less about tearing down applications than about what sits above them: an agentic pricing engine, for example, isn't really an application in the traditional sense. It pulls from several systems, gets trained on success criteria, and plugs back in through APIs. For that to work reliably, the data underneath has to carry more context than a database column ever did on its own. Vocabulary is part of that problem. "All words are not created equal," he said. The word "inventory" means something different to a finance team than to a shipping team, and grounding that word in the actual documents and systems where it shows up, rather than treating it as plain English, is what lets a model tell the difference.
None of this means foundational models get it right by default once the data's connected. Neelakantam pointed to case-deflection systems as a concrete failure mode: an agent auto-closing a support ticket as resolved when the customer is still angry. "Nobody likes having a case auto-closed on them," he said. Fixing that kind of error isn't a matter of a new rule or a line of code, in his view. It takes the same discipline as traditional machine learning: logging inputs and outputs, running feedback loops, tracking precision and recall, and in some cases fine-tuning on the right training data. Prompt tuning alone doesn't get a system to production-grade accuracy.
The audit trail follows the same logic. As agents take on more autonomous action, Neelakantam said the industry is only beginning to build out what detailed logging of agentic workflows actually looks like — capturing intent, reasoning, and outcome at every step, not just the final answer. For regulated industries already running Data Intelligence in production, that's not optional. It's the same instinct driving the rest of this approach: don't hide the parts of the system that are hard to explain. Log them.
Neelakantam was candid that this transition carries real risk. Coding has gotten more accessible faster than the engineering discipline needed to deploy AI systems safely has caught up, in his view — a "wild west" period where people can build and ship agentic systems without necessarily understanding the failure modes underneath them. His read isn't that this slows down. It's that developers need to get comfortable fast with logging, testing, and documentation as the basic hygiene of handing real responsibility to a system that reasons on its own.
For developers building on top of any AI-readiness platform, not just Everpure's, the takeaway is less about the specific product and more about the posture: treat contradictory data as a signal, not a bug to be squashed before a model ever sees it. The version of your data with all its disagreements intact is more honest than the version an engineer smoothed over, and it's the version that will keep an agent from being confidently wrong.
