We build the systems your business actually runs on.
Arvanto Labs delivers cloud, data, AI, and platform engineering that hold up in production — for teams in tech, healthcare, and banking who can't afford a system that fails quietly.
Advisory that gets its hands dirty in the system, not just the slide deck.
We work alongside your engineering team across the stack — infrastructure, data, software, and security — from first architecture sketch to production hardening.
Cloud & Infrastructure
Architecture, migration, and cost/capacity planning across AWS, GCP, and Azure — built for reliability, not just a lower monthly bill.
Data & AI/ML Systems
Pipelines, platforms, and ML systems — from warehouse design to getting a model from notebook to production without surprises.
DevOps & Platform Engineering
CI/CD, observability, and internal platform tooling that let your team ship without babysitting the pipeline.
Technology & IT Strategy
Vendor selection, build-vs-buy calls, and a technical roadmap grounded in your team's actual constraints.
Healthcare Systems
EMR integrations and clinical data pipelines built around HIPAA, data residency, and audit requirements from day one.
Banking & Financial Systems
Transaction pipelines, fraud/risk models, and core-banking integrations designed for uptime and regulatory scrutiny.
Small by design.
We stay boutique on purpose — every engagement gets senior attention, not a rotating bench of junior consultants.
Fostering Collaboration
Transparent process, shared outcomes — you work directly with the person who scoped the engagement.
Vendor-Neutral Thinking
No reseller incentives. Recommendations are based on your constraints, not a partner kickback.
Built to Hand Off
We measure success by how well your team runs the system without us — not by contract renewal.
A structured, hypothesis-driven approach.
Borrowed from classic management-consulting method — structured problem-solving applied to real technical systems, not just decks.
Problem Definition
Frame the issue as a sharp, falsifiable question — not a vague task list — before any analysis begins.
MECE Structuring
Break the problem into workstreams that are mutually exclusive and collectively exhaustive, so nothing is double-counted or missed.
Hypothesis-Driven Analysis
Form a hypothesis for the likely answer early, then run the analysis that would prove or disprove it fastest.
Fact-Based Synthesis
Every recommendation is grounded in evidence from your systems and data — not opinion or industry template.
80/20 Prioritization
Identify the small set of changes that drive most of the impact, and sequence effort around those first.
Client Ownership
Build and document for your team to run independently — implementation success is measured by your adoption, not our tenure.
Modern stack for production systems.
Every tool below is something we've deployed, debugged, and handed off — not a logo we're comfortable name-dropping.
Real engagements, real outcomes.
A look at how we approach a production system end-to-end — starting with healthcare, where the compliance stakes are highest.
Raising OT utilization 31% for a multi-hospital network
A 400-bed hospital group was scheduling operating theatres through a spreadsheet-based manual process — leading to frequent double-bookings, idle OT time between cases, and surgeons routinely waiting on room readiness.
Manual, spreadsheet-driven OT scheduling led to idle theatre time, last-minute conflicts, and no visibility into which surgeons or departments were driving under-utilization.
A real-time OT scheduling and utilization system pulling from EMR and staff rosters, with predictive case-duration modeling and a live dashboard flagging idle or conflicting slots before they happened.
OT utilization rose from the low 60s to over 90%, with scheduling conflicts and last-minute case cancellations dropping sharply within the first quarter post-launch.
Real-time fraud detection for a Mumbai-based NBFC's UPI transactions
A fast-growing NBFC processing over 2 million UPI transactions daily was relying on rule-based fraud checks that ran post-settlement — catching fraud after money had already moved, and generating high false-positive rates that frustrated legitimate customers.
Fraud detection ran as a nightly batch job, well after settlement, with rule-based logic producing false-positive rates high enough to trigger RBI-reportable customer complaints.
A real-time scoring pipeline (Kafka + a lightweight ML model) scoring transactions in-flight, with a feedback loop feeding confirmed fraud cases back into retraining — built around RBI data-localization requirements.
Fraud caught pre-settlement instead of after, with false positives cut by more than half — reducing manual review load on the risk team.
More case studies — including cloud migration work — coming soon.
Frequently asked questions.
If you don't see your question here, reach out — we respond quickly.
What industries does Arvanto Labs serve?
Do you build custom AI/ML systems, or only advise?
What's the typical engagement model?
Can you modernize an existing system instead of building new?
How do you handle compliance-sensitive data?
A small, senior team — not a bench.
Every engagement is led by someone who's actually run these systems in production.
Founder
Technology & Strategy Lead
Principal Engineer
Data & ML Systems
Principal Engineer
Cloud & Platform
Advisor
Healthcare & Compliance
Tell us what's breaking, or what you're about to build.
Most first conversations are a 30-minute call — no deck, no pitch, just a look at your actual problem.
Let's look at your technology stack together.
- hello@arvantolabs.com
- Phone
- +91 81496 93036
- Response time
- Within 1 business day
- Based in
- Mumbai, India · working with teams globally