Enterprise consultancy for complex transformations
Build the operating backbone for your next decade.
We modernize mission-critical enterprise systems and workflows, and improve software delivery with AI where it creates measurable leverage.
Engineering enterprise systems that become more intelligent, resilient, and decision-ready with AI.
Senior-only
Hands-on founders and senior operators stay inside the work.
Research to production
AI depth is applied where it survives operational reality.
Selective by design
Selective engagements, scoped for accountability.
Selected experience
Our offerings
Our offerings. Clear scope. Senior execution.
Focused transformation work for teams dealing with brittle systems, operational drag, and AI adoption inside real delivery environments.
Core systems
Platform Modernization
Modernize brittle platforms, incumbent business systems, and enterprise products without reckless rewrites.
Where it creates leverage
- Untangle integrations, data flows, and workflow architecture
- Reshape systems around maintainability and operating leverage
Operational workflows
AI-led Process Automation
Design audit-ready workflow automation for operations, compliance, and back-office execution.
Where it creates leverage
- Reduce manual drag without losing human oversight
- Build traceable systems that survive production reality
AI-led delivery
AI-DLC
AI-led Developer Lifecycle (AI-DLC) applies AI across migration, engineering, testing, and release without lowering quality bars.
Where it creates leverage
- Increase engineering leverage without delivery theatre
- Embed review discipline, quality gates, and enablement
Track Record
Evidence of change, not a stack inventory.
The proof should read like operating outcomes: maintainability, traceability, throughput, and reliability.
Transformation lens
Systems + operations + delivery
Engagements are scoped around the operating model, not around isolated features.
Operating bar
Practical, auditable execution
The work is designed to survive governance, handoff, and production use.
Enterprise platforms modernized into maintainable, production-ready systems.
Workflow automation that reduces manual operations and improves traceability.
Production AI deployed in identity, media, and high-pressure operating settings.
Delivery systems that increase engineering leverage without lowering quality.
Case Studies
Selected transformation work
Proof from operating environments where reliability, workflow discipline, and system change all mattered.
Aadhaar (UIDAI): National-scale biometric identity systems
Built and evaluated biometric identity systems for Aadhaar, the world's largest digital identity program, where scale, auditability, fairness, and operational reliability mattered as much as matching accuracy. The work combined research-grade biometric methods, product-aware system design, and strong engineering to support de-duplication, age-robust face recognition, and repeatable benchmarking at national scale.
Reported impact
~6%
Uplift at FMR@10,000
Reported improvement on large-scale biometric benchmarks.
Allocation & replenishment platform for global retail
Built and launched a multi-tenant allocation platform from MVP to production in about two months, then rolled it out across North America, EMEA, Japan, and China. The business value was straightforward: replace manual, hard-to-audit allocation workflows with a standardized process that could support regional operations and create a clear decision trail.
Reported impact
~2 months
MVP to production
Reported delivery timeline from build start to production launch.
Compliance-ready workflow platform integrations
Enterprise customers needed integrations delivered faster, but they also needed the resulting workflows to survive compliance reviews and security scrutiny. We built a reusable integration and auditability layer that combined enterprise domain understanding, product pragmatism, and strong engineering to make speed and compliance reinforce each other.
Reported impact
Weeks to days
Integration lead-time reduction
Reported shift enabled by reusable integration and auditability patterns.
Why Vitartha
Tight positioning. High-trust execution.
The redesign should make the firm feel focused, not broad. Small surface area. Clear operating taste.
Research to production
AI/ML depth is paired with production engineering discipline, so the work survives operating constraints.
Senior-only delivery
No staff augmentation theatre. Partners and senior engineers stay hands-on throughout the engagement.
Enterprise comfort
Incumbent systems, integrations, compliance, and handover are part of the work, not awkward edge cases.
Selective by design
A limited number of engagements lets the team optimize for quality, accountability, and fit.
Approach
How the work gets de-risked
Discovery before automation theatre, explicit constraints, and production readiness from the start.
Define the operating problem
We map constraints, system context, stakeholders, and where the business is losing leverage before proposing automation or platform change.
Ship with senior ownership
Partners and senior engineers stay close to the architecture, implementation, reviews, and demos.
Leave behind something durable
Runbooks, handover, instrumentation, and delivery discipline are part of done, not post-launch clean-up.
Team
Hands-on founders, not a layered delivery machine
The relationship starts with senior operators. Specialist depth sits behind them, but the ownership stays visible.
Himanshu Shankar
Partner - Delivery Systems, Execution & Operational Reliability
Engineer-founder focused on execution discipline for high-stakes transformation work. Leads senior teams, sharpens delivery systems, and helps organizations turn strategy into reliable operating change.
Mahen Gandhi
Partner - Research-to-Production AI Systems & Enterprise Transformation
Research-to-production AI systems lead working across enterprise AI, computer vision, biometrics, large-scale platforms, and AI-led software delivery. Brings applied AI/ML depth, software engineering systems thinking, and production engineering discipline to transformation work that has to survive real constraints.
FAQs
Questions serious buyers usually ask first
The goal is fast qualification, not marketing sprawl.
Start here
Planning a modernization, automation, or AI-DLC initiative?
Start with the business problem, the system constraints, and the delivery pressure. We can decide quickly whether the work is a fit.