Pranjal Gupta
Building production AI systems that actually ship.
I specialize in taking AI from proof-of-concept to production at enterprise scale. Government, banking, healthcare, technology.
Production Systems
Enterprise Clients
Max Project Value
Largest Team Led
The Short Version
My Approach
80% of enterprise AI projects fail. Not because of the models—because of everything around them. I focus on the boring stuff that makes AI actually work: infrastructure, evaluation, change management.
"The difference between a $1M write-off and a system that transforms a business is rarely the AI itself."
What I Actually Do
- 1.Map the workflow before touching a model
- 2.Build boring infrastructure first (rate limiting, caching, fallbacks)
- 3.Target 85% automation with human review on the rest
- 4.Invest in evaluation before optimization
Track Record
Technical Depth
Currently
Founder at DataXLR8 — helping enterprises deploy AI that actually works.
Also writing about AI without the hype at BSKiller.
Selected Projects
Real production systems with measurable outcomes. Click any card for the full story.
Multi-Agent Document Intelligence System
The agency processed 50,000+ complex regulatory documents annually. Manual review took 45 minutes per document with 12% error rate. Backlog was growing 15% quarterly.
Real-Time Clinical Triage System
Emergency departments were overwhelmed with non-urgent cases. Nurses spent 40% of time on initial triage that could be automated. Wait times averaged 4.2 hours.
AI-Powered Vendor Discovery Platform
Enterprise procurement teams spent 6-8 weeks researching vendors for any new software purchase. Information was scattered across G2, Gartner, vendor websites, Reddit, and analyst reports.
Secure Multi-Tenant AI Platform
The bank wanted to deploy AI across 15 business units but couldn't risk data leakage between units. Existing solutions couldn't guarantee isolation while sharing infrastructure.
Educational Content Generation Factory
Creating educational video content cost $5,000-10,000 per video. Startup needed 1,000+ videos across subjects. Traditional production couldn't scale.
Mathematical Reasoning System
Advanced mathematical problem-solving requires multi-step reasoning that single LLM calls can't reliably perform. Competition-level math problems have <5% solve rates with standard approaches.
Enterprise AI Playbook
Patterns from 12+ production deployments. What actually works, what consistently fails, and how to implement enterprise AI successfully.
Engagement Models
Flexible engagement options based on your needs and timeline.
Advisory
2-4 weeksArchitecture review, technology selection, and strategic guidance.
- ✓Architecture review
- ✓Technology selection
- ✓Team capability assessment
- ✓Implementation roadmap
Implementation
3-12 monthsEnd-to-end system design, build, and production deployment.
- ✓System design & build
- ✓Team augmentation
- ✓Production deployment
- ✓Knowledge transfer
Rescue
1-3 monthsFailing AI project assessment, course correction, and recovery.
- ✓Project assessment
- ✓Root cause analysis
- ✓Re-architecture
- ✓Team coaching
Let's Build Something That Ships
Whether you're starting a new AI initiative or rescuing a struggling project, I'd love to hear about what you're building.
Also writing about AI without the hype at BSKiller
© 2026 Pranjal Gupta. Building production AI systems that actually ship.