Xenqube · Enterprise deep-tech · Est. 2023
Lots of teams have an AI pilot. Few have it running inside the real workflow. We build agents, RAG, and private LLM systems with human gates, audit trails, and a runbook your ops team can actually use.
Assessment: 2 minutes · No email to start · Instant score
What are you looking for?
The gap we work in
McKinsey State of AI 2025 found that 88% of organizations use AI in at least one function, while most remain in experiment or pilot stages. Closing that gap usually means redesigning the workflow and owning production, not buying another model demo.
Quick Summary
Xenqube: Xenqube (Xenqube Technologies Pvt. Ltd.) is an enterprise deep-tech studio founded in 2023 in New Delhi, India. The team builds production AI systems (agents, RAG, private/on-premise LLMs) plus cybersecurity and Web3 infrastructure for regulated enterprises that need workflow outcomes in production.
Results in production
Ranges from live engagements. Payer mix, volume, and data maturity change the outcome, so we publish bands instead of fake precision. Client names stay under NDA unless they approve public credit.
Buyer themes
Anonymized composites from engagement patterns until named, LinkedIn-verifiable quotes are approved. For numbers, see case studies.
“They mapped prior-auth, kept PHI in our VPC, and put humans on exceptions. Admin load on routine packets dropped meaningfully - and they sized it as a range because payer mix varies.”
Why Xenqube
How we work in practice. Claims you can diligence against case studies.
If you bolt a model onto a broken process, nothing moves. We map exceptions, approvals, and systems of record first, then put AI where it cuts cycle time.
Sometimes cleaner data pipelines or plain rules beat a model. We say that in discovery. Buyers who hear it once usually trust the roadmap that follows.
HIPAA/BAA, SOC 2-aligned controls, GDPR, and air-gap patterns when you need them. Your auditors own the certification. We own data flow, access, and audit trails.
Monitoring, evals, refresh process, and runbooks ship with the system. When quality drifts three months later, your team has a playbook instead of a dead prototype.
Results depend on data quality, volume, and how ready the team is to change the process. Case studies show the ranges we have seen.
Read case studiesFounder
Xenqube Technologies · New Delhi
Designs enterprise AI with security in the architecture from day one: private LLM paths, human approval gates, and systems that survive a compliance review. Background in AI systems, cybersecurity, and Web3. Started Xenqube in 2023 to ship working systems instead of decks.
Next step
Take the free assessment if you are still mapping. Request a brief if you already know the workflow.
Want to size savings first? ROI calculator · Book a demo
Prefer email? hello@xenqube.com. We reply within one business day.