Platform & Deployment Engineering
Deploy and scale production AI systems with reliability from day one.
What it is
We design and build the infrastructure layer that makes AI systems production-ready — scalable, observable, secure, and maintainable. From initial deployment to multi-region scale, we build platforms that operations teams can trust.
Systems we design and deliver
AI System Deployment Pipelines
CI/CD pipelines for AI systems — automated testing, model validation, staged rollouts, and rollback capabilities that match the reliability needs of production.
Cloud-Native AI Infrastructure
Scalable, cost-optimised cloud infrastructure for AI workloads — on AWS, GCP, or Azure — with auto-scaling, resource management, and cost controls.
Internal Developer Platforms
Self-serve platforms that let your engineering teams deploy AI components without manual DevOps intervention — accelerating delivery velocity.
Security & Compliance Architecture
Infrastructure designed with security, data residency, and compliance requirements built in — not bolted on — for regulated industries.
From intake to production
Assess
We review your current infrastructure, team capabilities, and system requirements — identifying gaps between what you have and what production AI needs.
Design
We design the target architecture: deployment pipeline, compute layer, networking, storage, secrets management, and observability stack.
Build & Deploy
We implement the infrastructure as code, set up CI/CD pipelines, and deploy your AI systems into the production environment.
Handover & Monitor
We document everything, train your team, and set up monitoring dashboards — ensuring your team can own and operate the system confidently.
Who we build this for
Common questions
What cloud platforms do you work with?
We work with AWS, Google Cloud Platform, and Azure. We recommend the platform based on your team's existing expertise, cost profile, and compliance requirements — not by preference.
What is the difference between MLOps and platform engineering for AI?
MLOps focuses on the model lifecycle — training, versioning, deployment, and monitoring of ML models. Platform engineering is broader — it covers the full infrastructure stack that AI systems run on, including CI/CD, compute, networking, security, and developer tooling.
Can you work with our existing infrastructure team?
Yes. Most engagements involve working alongside your existing team — we bring AI system expertise while respecting your existing infrastructure patterns and constraints.
How do you handle data security and compliance requirements?
We design security into the architecture from the start — data encryption at rest and in transit, access controls, audit logging, and data residency compliance. For regulated industries (BFSI, healthcare, GovTech), we align with relevant frameworks (RBI guidelines, DPDP Act, GDPR).
Do you help with cost optimisation for AI infrastructure?
Yes. AI workloads can be expensive if not architected well. We design for cost efficiency from the start — right-sizing compute, using spot/preemptible instances where appropriate, and setting up cost monitoring and alerting.
Let’s identify the highest-leverage
system for your business.
We’ll review your workflows, prioritize the right opportunity, and define a practical path from concept to production.
Start a system reviewResponse within 1 business day.