Founded on a different kind of AI problem.
Most of the enterprise AI industry focused on making models smarter. We focused on something the industry was not asking yet.
The Founding Observation
For years, the enterprise AI conversation was dominated by a single question: how do we make the AI perform better? Accuracy, context windows, inference speed, prompting techniques, guardrails. These were the metrics that mattered. They were also, it turned out, the wrong question.
Working on enterprise AI initiatives through Lighthouse3, a different pattern kept appearing. The AI systems that created the most serious operational concerns were rarely the ones that failed outright. They were the ones that changed — slowly, quietly, without anyone noticing.
An AI assistant that gradually became overconfident in its responses. A recommendation workflow that subtly shifted its priorities over weeks. An underwriting agent that incrementally changed its approval behaviour after months of exposure to new data and workflows. An autonomous process that remained technically within policy while operational risk accumulated beneath the surface.
None of these were software failures in the conventional sense. They were something else: behavioural drift.
Enterprises were about to deploy an entirely new class of digital workforce — AI agents that could reason, adapt, coordinate, and operate semi-autonomously inside business processes. And yet organisations were governing them as if they were static software: monitoring for uptime, measuring outputs, checking error rates.
If AI agents increasingly behave like workers — reasoning, deciding, adapting over time — shouldn’t enterprises monitor and manage them more like a workforce?
Not with the tools of software operations. With the instruments of behavioural oversight. That question became the seed for Zypero Intellect.
What It Became
That question became Behaviour Intelligence for AI Agents — the discipline Zypero Intellect has built its practice around. Not AI automation. Not another monitoring dashboard. A structured approach to defining how agents should behave, building that standard into the architecture, and continuously measuring whether it holds once agents are in production.
AegentIQ grew from the same founding insight: once the engagement ends, someone needs to keep asking the question. AegentIQ asks it continuously — measuring whether every agent in production is still behaving the way the organisation approved it to behave.
Dilip Dand also writes The Quiet Drift — a newsletter on AI agent behaviour, risk, and what enterprise deployment really means. It is the ongoing conversation the company began.
Dilip Dand
Co-Founder & CEODilip brings 25 years of enterprise AI and machine learning experience, including senior roles at JP Morgan Chase, Visa, and LendingClub. He has built and led AI programmes at scale across financial services — environments where the consequences of how AI systems behave are directly consequential to customers, operations, and regulators. The founding insight behind Zypero came from patterns he observed across those engagements.
LinkedIn →Ajay Dand
Co-Founder & COOAjay founded and scaled an IT business to $3M+ in annual revenue over 25 years, building deep expertise across technology operations, client delivery, and finance. He joins Zypero Intellect full-time, leading operations, finance, and partnerships.
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