Production software and
AI systems,
engineered to scale.
We help SaaS businesses and enterprise teams ship AI-native products, automate the work behind them, and modernize the platforms they run on. Eight years building systems that hold up in production.
Engineering teams in India. Client presence in North America.
We ship systems that hold up.
Eight years running production for SaaS and enterprise teams. Here is what that looks like in numbers, and what we run it on.
Live LLM and RAG analytics workload on infrastructure we migrated and now operate.
Exam-window traffic absorbed with zero degradation and no manual scaling.
Containerized, observable services deployed to run unattended across multi-tenant systems.
The seniors who scope the architecture are the ones who run it in year three.
- GPT-5
- Claude
- Gemini
- Llama
- LangChain
- Pinecone
- Qdrant
- FastAPI
- Node.js
- PostgreSQL
- Redis
- Kafka
- Kubernetes
- Docker
- AWS
- Vercel
- Cloudflare
Four practices. One engineering bar.
From AI products to the platforms they run on, the same senior team carries the work end to end.
AI Product Engineering
AI-native SaaS, copilots, RAG systems, and AI search, built into your product and shipped to production.
AI Automation & Workflows
Workflow and process automation, internal copilots, and operational AI that takes manual work off your team.
Embedded Engineering Teams
Senior engineers embedded in your team, in your workflow and your standups, for the long term.
Platform Modernization
Legacy modernization, cloud migration, and technical-debt reduction, shipped in slices instead of a risky rewrite.
Engagement models.
Choose the model that fits your team structure, timeline, and budget. Same engineering bar across every engagement.
AI Product Engineering
Build AI-powered features into your product. Fixed-scope projects with defined milestones and deliverables. Best for a defined AI feature with a clear finish line.
- →LLM integration & orchestration
- →RAG pipeline development
- →AI workflow automation
- →Production deployment
- →Technical documentation
Embedded Engineering Teams
Augment your engineering team with senior engineers who integrate with your workflow and tools. Best for adding senior capacity to an existing roadmap.
- →Full-stack capabilities
- →Your management, our execution
- →Attends your standups & rituals
- →Long-term partnerships
- →Scale up or down monthly
White-Label Products
Deploy production-grade booking, e-signature, and project management systems under your brand. Best for launching a proven system under your brand.
- →Production-ready systems
- →Full source code access
- →Customization available
- →White-label deployment
- →Optional support retainer
Systems we've built.
Real infrastructure in production. Not prototypes, not MVPs. Systems that scale under load and handle enterprise traffic.
Ailyze
Converted a Streamlit research prototype into a production Django SaaS with proper user management, third-party integrations, and a dynamic interface.
Setera
Built a custom portal for a global communications business: optimized architecture, faster load times, and a revamped interface built for scale.
ResInnov
Rebuilt a global market-research platform on auto-scaling, caching, and a CDN for resilience under load, then stayed on for performance and security.
Hear it from a client.
Jason Layle of deskvana on what it is like to build with us. Two minutes, unscripted, in his own words.
“Shubham has been great to work with. He is responsive and was an asset to the team.”
The engineers who scope your system are the ones who run it.
I started Shubpy in 2018 on a single bet: the same senior engineers stay on the work for its lifetime. No swap-out after the kickoff call, no delivery team replacing the people who scoped it. The engineers your CTO meets on day one are still running the system in year three.
The AI work we do now sits on the same foundation as the scaling work we did in 2019: understand the system, respect the constraints, ship something that holds up under real load. The acronyms changed. The discipline did not.
The objections we hear most.
How are you different from a typical software agency?+
Agencies win the work with senior engineers and ship it with junior ones. We do the opposite. The senior engineers who scope your system are the ones who build it and run it. We stay small on purpose and turn down work we cannot staff with senior people on day one.
How do we know an AI feature will work in production, not just in a demo?+
We build AI on the same discipline as the rest of our systems: evaluation sets, guardrails, observability, and graceful fallbacks. We have run LLM and RAG workloads at 40M+ requests a month. A demo is not a deliverable. Production behaviour under real data is what we design for from the start.
How is an embedded team different from staff augmentation or hiring freelancers?+
Our engineers integrate with your workflow, attend your standups, and stay on the work for the long term under your direction. You get senior, dedicated capacity and continuity, not a rotating bench of contractors or a staffing agency reselling resumes.
Can you modernize our legacy platform without a risky rewrite?+
Usually, yes. We modernize incrementally: map the system, isolate the risk, and migrate in slices that each ship to production. Big-bang rewrites are the exact failure mode we are most often hired to avoid.
What happens after launch? Do you support what you build?+
Yes. The team that builds the system runs it. We offer ongoing support and embedded retainers, and because the same engineers stay on, support is never a handoff to people who have never seen the code.
How do we get started?+
A short, paid discovery. We map your system and hand you an architecture and a plan you own, whether or not you build the rest with us. It is the fastest way to find out if we are the right team for the work.
Let's scope your next system.
Tell us what you are building and where it needs to go. We will map the architecture, the risks, and the path to production before you commit to a build.