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Topics for submission are listed here: https://hasgeek.com/fifthelephant/enterprise-ai-in-production/
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Accepting submissions till 01 Jun 2026, 11:59 PM
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Zahle Making Generative UI work in productionFormat Lightning Talk Speaker Zahle Khan , Founding Engineer, Thesys more
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Aviral Tuteja Streaming AI Dashboards in Production: From Enterprise Data to Live Agent-Generated WidgetsDescription Most enterprise AI demos stop at a polished chat response. This demo goes one step further: an AI assistant receives a natural-language business question, gathers relevant enterprise CRM context, and turns the answer into a live dashboard-style widget inside the conversation. The widget is not a static mockup or a pre-built report; it is generated from retrieved business data, streame… more
Submission type: Live demos (15 mins)
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Asif Mansoor Why Data Quality Matters When Working with Data at ScaleMost data quality problems aren’t bugs in the data. They’re broken contracts between producers and consumers. The contract gets implicitly defined when the first staging pipeline runs, then quietly violated in production when an upstream service ships a “harmless” schema change, a field gets nullified, or volume changes by 10x without warning. By the time the dashboards look wrong, the bad data h… more
Submission type: Lightning talks (10 mins)
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Ramanuj Vidyanta AI Governance in Indian Banks Isn't a Policy Problem — It's an Engineering Problem{Describe your session in 2 paragraphs} Every major Indian bank now has an AI governance policy document. Most are useless in production. I’ve spent the last several years operationalizing AI governance frameworks — RBI’s FREE AI guidelines, the EU AI Act (for global banking clients), ISO 42001, NIST AI RMF, and the DPDP Act — across production ML systems at some of India’s largest financial inst… more
Submission type: Lightning talks (10 mins)
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KANIKA SINGHAL When AI Agents Access Your Data: Controlling Data Flow in Modern Pipelines{Describe your session in 2 paragraphs} Modern AI systems increasingly rely on agents and automated workflows that interact with databases, APIs, and external services. While databases like PostgreSQL provide strong controls for who can access data, they do not govern how that data us used once retrieved. In practice, agent-driven pipelines can query sensitive data and then propagate it across sy… more
Submission type: Lightning talks (10 mins)
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Vivek Kalyanarangan ₹11 Lakh/Month: How We Took the GPU Out of Face MatchFace matching is one of the highest-volume workloads in identity verification. At IDfy, a single GPU pod handling 1 RPS cost us ₹3,500/day. After moving the model to BF16 inference on Intel CPUs via OpenVINO, the same 1 RPS pod cost ₹350/day. Same TAT, same throughput, same accuracy envelope. At our traffic shape (50 RPS sustained for the peak hour, 10 RPS for the remaining 23), that translates t… more
Submission type: Anchor talk (30 mins)
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Anirud Incident to Root Cause: Oodle's AI-Native Debugging in ActionA live demonstration of AI-native debugging using Oodle. Starting from real alerts in a Kubernetes cluster running AI features, I will show how we use Oodle to instrument and debug: all without leaving Cursor / Claude Code. more
Submission type: Live demos (15 mins)
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sooraj shankar The Memory Layer Production Agents Need: Inspectable, Governed, DurableProduction AI agents need more than retrieval and a larger context window. Once an agent starts returning to the same user, acting on company data, and carrying context across sessions, memory becomes production state. That state needs boundaries: what is personal to a user, what is shared across the organization, who can change it, and how teams can inspect what happened when the agent behaves u… more
Submission type: Anchor talk (30 mins)
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Akash Sathish When the agent workflow survives production but the MCP server splits an RCEEvery enterprise AI workflow that matters in 2026 routes through MCP servers starting from the tools that give your AI agents access to files, databases, APIs, till shell commands. But the security posture of these servers is systematically poor: 43% have command injection vulnerabilities, 36% have SSRF exposure, and the real CVEs (CVE-2025-6514, CVSS 9.6) are execSync(args.cmd). These are bugs t… more
Submission type: Anchor talk (30 mins)
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rajesh 
Sachin Chaurasiya Debugging Agents in ProductionDebugging Agents in Production Every distributed system is impossible to debug without custom built observability and tracing tools. Multi-agent systems are no different. more
Submission type: Anchor talk (30 mins)
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