Kanishk Mehta
Product Leader at Quantiphi
The agentic AI gold rush has left many enterprises with an uncomfortable reality: the platforms they rushed to adopt may undermine their long-term competitiveness.
This concern drives warnings from Kanishk Mehta, product leader at AI consulting firm Quantiphi Analytics, who has watched companies grapple with unintended consequences of their AI adoption strategies.
Quantiphi, which has provided AI and data science consulting services since 2013, developed baioniq, an agentic AI platform designed to operate within customer infrastructure rather than external cloud environments.
Kanishk’s warning comes from an unfolding data security crisis where nearly 40% of employees share company secrets with public AI tools without permission, rising to 46% among younger workers.
“This isn’t theoretical, it’s happening right now in organisations across every industry,” he says.
“When employees use public AI tools with company data, they’re essentially broadcasting your competitive intelligence to the world.”
How data sovereignty drives baioniq development
The question has gained urgency as AI companies face business continuity challenges while customers worry about data locked into external platforms.
Traditional cloud-based AI services require organisations to transmit sensitive information to third-party providers, creating vulnerabilities many enterprises are only now understanding.
Recent surveys suggest employees routinely share sensitive company data with consumer AI tools, exposing organisations to data leaks.
When companies use external AI services, their prompts, training data and model improvements often become shared resources rather than proprietary assets.
How Baioniq addresses enterprise control requirements
Quantiphi’s baioniq operates within existing virtual private cloud infrastructure – the secure computing environments companies use to run applications and store data.
This architecture differs from cloud-based AI services that process customer data on external systems.
“The fundamental differentiator is deployment architecture,” Kanishk explains.
“The platform deploys within your existing cloud infrastructure – your VPC, behind your firewall – ensuring complete data sovereignty.”
The platform connects to enterprise data through 37 connectors, creating intelligent retrieval systems that understand context and intent rather than matching keywords.
With baioniq, these assets “remain exclusively yours, creating intellectual property that appreciates over time,” Kanishk argues.
The platform also has pre-built agents for specific industries, including pharmacovigilance systems for life sciences companies monitoring adverse drug events and underwriting agents for insurance risk assessment.
“These aren't generic chatbots but specialized AI systems designed to solve complex industry challenges,” Kanishk says.
The three phases shaping the AI adoption evolution
Kanishk, who has spent over six years at Quantiphi developing enterprise AI solutions, sees adoption following three phases.
Initial democratisation makes AI accessible beyond IT departments.
The second phase develops AI that understands business contexts.
The final phase envisions autonomous AI systems handling complex processes.
Quantiphi acknowledges what Kanishk describes as the reality that “most enterprises will operate in a multi-vendor AI environment” rather than committing to single providers.
The company also reports measurable improvements from baioniq implementation: 50% gains in knowledge worker efficiency, 60% acceleration in task automation and 80% reduction in content summarisation time.
Quantiphi uses baioniq internally, providing validation while informing development based on actual usage patterns.
“Every day you delay is a day your competitors are building advantages that will define the next decade,” Kanishk concludes.
“This is a long-term investment that positions your enterprise to compete in an AI-native economy.
“The question isn’t whether you can afford to own your AI stack – it’s whether you can afford not to.”
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