By Grace Foley
Saad Bin Shafiq was just 12 years old when he began learning to code. Not on a computer, but on paper. Growing up in a village in northern Pakistan without reliable electricity, he studied programming syntax from a C# textbook, memorising functions and logic flows by candlelight.
More than a decade later, Bin Shafiq leads NODES, a talent intelligence platform built for enterprises seeking to deploy artificial intelligence while keeping sensitive data entirely within their own infrastructure.
The company says its platform has processed hundreds of thousands of candidate profiles, and that multiple Fortune 500 organisations are currently evaluating its approach as demand grows for privacy-preserving AI solutions.
The Enterprise AI Problem Few Could Ignore
Enterprise adoption of AI has accelerated rapidly, but regulated industries face a persistent challenge: data privacy.
Many organisations, particularly in banking, insurance, and financial services, cannot risk sending confidential information to third-party servers or external AI platforms. Legal teams remain cautious, as sensitive data shared outside internal systems may create compliance exposure.
This concern became widely visible when major corporations across sectors restricted the use of consumer-facing generative AI tools due to uncertainty around confidentiality and governance.
As OpenAI CEO Sam Altman has publicly acknowledged, conversations with large language models do not carry the same legal protections as privileged communications.
For enterprises, the message is clear: AI may be transformative, but it must also be deployable within strict regulatory boundaries.
Building AI That Stays Inside the Firewall
Bin Shafiq founded NODES in October 2023 with the goal of addressing this specific enterprise constraint.
Unlike many AI-enabled hiring platforms that rely on external cloud APIs, NODES is designed to run entirely inside a customer’s own infrastructure. According to the company, this ensures that sensitive hiring and workforce data never leaves the organisation’s environment.
The platform uses a network of specialised AI agents that operate across core enterprise systems, including:
Each agent performs distinct functions such as skills inference, performance pattern recognition, and bias monitoring, while an orchestration layer combines outputs into structured recommendations.
By using open-source models that organisations can host internally, the platform aims to reduce the legal and compliance barriers that often slow enterprise AI deployment.
Early Adoption in Regulated Industries
One early adopter was CNO Financial Group, a Fortune 500 insurance provider operating across more than 200 locations.
The company had reportedly spent significant time evaluating AI hiring vendors, but approval processes were delayed due to concerns about external data processing.
NODES offered an alternative model: an AI system designed to operate fully within the organisation’s infrastructure. The company says this approach enabled faster internal approval compared with solutions dependent on third-party servers.
CNO integrated the platform into its existing recruiting workflows through Avature, allowing deployment without major disruption.
Reported Outcomes and Continuous Learning
According to performance data shared by the company, NODES has supported large-scale candidate screening while improving hiring efficiency.
Reported outcomes include:
The system is designed to improve continuously through feedback loops. Several months after hiring decisions are made, performance outcomes can be incorporated into model refinement, helping organisations align hiring patterns with real-world success metrics.
Bin Shafiq argues that this outcome-based learning is essential for building enterprise-grade AI infrastructure rather than surface-level automation.
A Founder Shaped by the Hiring System
Before founding NODES, Bin Shafiq experienced the hiring process personally. He applied to nearly 700 roles before receiving one acceptance.
“I am the use case,” he says. “I am the person algorithms might filter out, not because I couldn’t do the work, but because I didn’t match the patterns they were trained to recognise.”
That experience became central to his mission: building systems that identify talent based on actual success outcomes rather than narrow résumé templates.
The Next Phase of Talent Intelligence
NODES is now expanding beyond hiring into broader workforce intelligence.
The same infrastructure used to identify candidates can also support:
Bin Shafiq envisions AI platforms becoming long-term organisational intelligence systems — capturing what success looks like within a specific company and allowing those patterns to evolve over time.
“Somewhere right now,” he says, “there’s a kid writing code on paper in a village with no electricity. The system is going to tell them no. I wanted to build infrastructure that finds them anyway.”
Artificial intelligence (AI) refers to the simulation of human intelligence in machines programmed to think and learn like humans, enabling them to perform tasks such as problem-solving and decision-making.
Data privacy is the aspect of information technology that deals with the proper handling of data, including its collection, storage, and sharing, ensuring that personal information is protected from unauthorized access.
Compliance in financial services refers to the process of adhering to laws, regulations, and guidelines that govern financial institutions, ensuring they operate within legal frameworks and maintain ethical standards.
A talent intelligence platform is a software solution that uses data analytics and AI to help organizations manage their workforce, streamline hiring processes, and improve talent acquisition strategies.
Enterprise systems are integrated software platforms used by organizations to manage and automate core business processes, including finance, HR, and supply chain management, enhancing efficiency and data accuracy.
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