Five areas of practice. One engineering team that operates what it builds.
We do not subcontract. The engineers who design your system also write, review, and operate it, from architecture review through production.
AI Engineering
AI built for production.
End-to-end AI engineering: LLM applications, AI agents and copilots, RAG-based knowledge systems, computer vision pipelines, and AI integration into existing products. Eval-first delivery from the same senior team that ships our cloud and software work.
- LLM applications: Domain-tuned assistants and copilots, grounded in your data.
- Agents: Multi-step agents with scoped tools and audited actions.
- RAG systems: Ingestion, hybrid search, re-ranking, and retrieval evals.
- AI integration: Add AI to existing products without rewriting them.
Cloud Architecture
Infrastructure that holds.
Cloud foundations that adapt to load and to budget, multi-cloud, hybrid, and instrumented from day one. We design for the production curve, not the launch screenshot.
- Scalable infrastructure: Architectures that grow with traffic, not against it.
- Cloud migration: Sequenced moves with rollback paths, never freeze the business.
- Performance & cost: Right-sized from day one. Continuously instrumented.
- Security & compliance: Threat-modeled at design time, evidence-ready at audit time.
Custom Software
Products built to survive contact with users.
From discovery to deployment under one roof. The same engineers stay on the system through production; the bar for quality is the same one applied to our own platforms.
- End-to-end delivery: Discovery, design, build, deploy, one accountable team.
- Modern stacks: Typed, observable, deployable by Friday afternoon.
- Agile cadence: Two-week ship rhythm, not two-week status meetings.
- Long-term sustain: Documented, instrumented, handed over or operated by us.
App Modernization
Legacy made operable again.
A pragmatic path from monolith to managed services, assessed, sequenced, delivered without freezing the business. We have done this in production five times; the playbook is real.
- Legacy assessment: A clear-eyed read of what to keep, what to retire, what to refactor.
- Cloud-native refactor: Move the workload to managed services without rewriting it twice.
- Microservices & containers: Decomposed where it helps; left alone where it does not.
- API-first integration: Contract-driven boundaries with backward compatibility by default.
Architecture Consulting
An independent read on your systems.
Senior engineers, brought in to evaluate, recommend, and, where it helps, to build alongside your team. Outputs are decisions, not decks.
- Architecture review: Independent assessment with prioritized recommendations.
- Scalability strategy: A roadmap from today's load to next year's.
- Secure by design: Threat models, control mappings, evidence pipelines.
- Stack modernization: A sequenced plan to retire technical debt without a rewrite.
How we work
A senior team, end to end.
Engagements run on a small senior pod, no junior layer between you and the engineers shipping the code. Four checkpoints structure the work; the rest is delivery.
Discovery
Workshops, code & infra walkthrough, prioritized risk register.
Architecture
Target-state diagrams, sequencing, success metrics agreed in writing.
Delivery
Two-week ship cadence with demoable output and instrumentation.
Operate
SLOs, on-call, cost & posture reviews, for as long as the engagement runs.
Senior by default
The people in your standup are the ones writing the production code.
Documented exits
If we leave, your team owns the system. Runbooks, ADRs, dashboards included.
Questions
How is Xpanso different from a typical IT services firm?
We do not subcontract. The senior engineers who design your system also write, review, and operate it in production. Our practice is narrow on purpose: AI engineering, cloud architecture, custom software, modernization, and consulting.
Do you work with clients outside India?
Yes. We have shipped systems for clients in the US, UK, Japan, and across India and the EU. Our engagement models support time-zone overlap windows for distributed teams.
What AI work do you actually take on?
LLM application development, AI agents and copilots, RAG-based knowledge systems, computer vision and OCR pipelines, and AI integration into existing products. We approach AI with the same engineering discipline we apply to cloud and software work: evals before demos, observability before scale.
How are engagements structured?
Most engagements run on a small senior pod with a two-week ship cadence. We agree on success metrics in writing during discovery, demo working software every checkpoint, and operate the system through production when that is part of the scope.
Who owns the code you write?
You do. Source code, models, evaluation suites, and infrastructure-as-code are yours from the first commit. We hand over with runbooks, ADRs, and dashboards so your team can operate the system if we step out.
What technologies do you typically work with?
AWS, Azure, GCP, Kubernetes, Terraform on the infrastructure side. Python, TypeScript, React, Next.js, Node, Go on the application side. OpenAI, Anthropic, Vertex AI, and open-weight models for AI work. Selection is driven by the workload, not by what's trending.
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