MLOps & Deployment
Get AI models from Jupyter notebook to production — reliably.
Most AI projects die between prototype and production. We set up MLOps infrastructure — CI/CD for models, monitoring, automated retraining, and scalable serving — so your AI systems run reliably at scale, not just in demos.
Sound familiar?
These are the pain points we hear from businesses every week — and exactly what we build to fix.
Models work in notebooks but break in production.
No monitoring — model accuracy silently degrades over time.
Data scientists and engineers work in silos with no deployment pipeline.
Scaling inference is an afterthought that causes outages.
Everything you need, nothing you don't
A complete, production-ready delivery — not a half-finished prototype.
From first call to live deployment
A clear, collaborative process — so you always know what's happening and what's next.
Discovery Call
Discovery Call
We understand your business, customer questions, pain points, and success metrics.
Design & Scripting
We map conversation flows, integrations, and qualification logic before writing code.
Build & Train
We build the solution and train it on your real content, FAQs, and business rules.
Test
We run real-world scenarios and edge cases with your team before anything goes live.
Deploy & Support
We launch, monitor performance, and tune based on real usage — with ongoing support available.
Real project, real results
Production ML Pipeline
Took a client's fraud detection model from manual deployments to fully automated CI/CD — reducing deployment time from 2 weeks to 2 hours with continuous monitoring.
Starting from ₹1,00,000 — depends on existing infrastructure and model complexity.
Common questions
Straight answers — no sales fluff.
Infrastructure setup typically 4–8 weeks.