Enterprise Software

Complex systems, reliably delivered.

Microservices. Legacy integration. Long-term ownership.

24mo

Average engagement

Active projects

1.4M+

Users on our systems

80%+

Test coverage minimum

The lit floors of an office building at night, seen as a grid of windows

Overview

Enterprise software projects fail because vendors treat them like standard projects. We treat them like the mission-critical, politically complex, technically constrained environments they are. We map your entire existing landscape before writing code, design for your integration constraints, and stay engaged for the life of the system.

The Problem

Enterprise software failures are almost never technical. They are scope misalignment, underestimated integration complexity, or vendors who disappear after go-live. Large organisations end up with expensive systems that do not connect to anything and a vendor pointing fingers at the brief.

Our approach

We begin every engagement with a Technical Alignment phase where lead engineers audit your existing systems, APIs, data models, and integration points. We produce Architecture Decision Records that document every design choice. Sprint delivery starts only when the architecture is understood and agreed. We stay for 12–24 months post-launch on average, becoming a genuine extension of your engineering team.

Deliverables

Technical landscape audit
Architecture Decision Record
Full-stack system delivery
Legacy system integration
CI/CD and test automation
Long-term SLA support

Tech stack

Next.js 15FastAPIDjangoNode.jsPostgreSQLRedisDockerKubernetesGraphQLEvent-driven Architecture

In practice

The estimate is a document you can argue with

Effort is stated in person-days against a named work breakdown, not a range with contingency hidden inside it. The custody platform resolved to 1,256 person-days across 26 weeks, published line by line, because a number a procurement committee can audit is the only kind that survives one. The architecture decision record — every integration point, data flow and security boundary — is settled before production code begins, and change control is written down. That is why the estimate tends to hold.

Audit is a schema decision, taken early

Regulatory posture is designed in rather than retrofitted. The custody system of record runs on an immutable hash-chained event ledger, architected against SEBI CSCRF 2024, DPDP Rules 2025 and the evidentiary standard of BSA 2023 — a history that is independently verifiable, not merely logged. Residency works the same way: a tenant knows which jurisdiction it belongs to and where it may be processed. Building that on day one is an afternoon. Retrofitting it into live data is a rewrite.

Measured in production, over years

Reliability is a record, not an intention. Krida AI has held 99.97% uptime across three years while serving over 1.4 million registered athletes, coaches and federation administrators in 28 disciplines, with selection cut from six weeks to four days. Underneath it: an 80% unit coverage floor, a live demo every fortnight instead of a status deck, blockers escalated inside 24 hours, and monthly health reporting once live. On a ₹325 crore programme, the same discipline forecast a 96-day slip while intervention was still possible.

What you own when it is finished

All of it — source code, schema, infrastructure definitions and documentation, on an open-source base with no per-seat licence at the platform layer. The stack is deliberately conventional, because an unconventional one is a recruitment problem and a handover problem: the platform has to be maintainable by an engineer who did not write it, including one who does not work for us. Exportability is the test. If you could not reconstruct your records and history from an export inside one working day, it is a silo with an API page.

Where this has run

Krida AI

National-scale sports and athlete management across 28 disciplines — registration, performance analytics and selection, with mobile apps for athletes and coaches.

1.4 million+ registered athletes, coaches and administrators at 99.97% uptime over three years. Selection cut from six weeks to four days. Next.js, FastAPI, PostgreSQL, React Native.

EMSPL

AI fleet management with predictive maintenance and live GPS telemetry, extended into a full enterprise resource platform.

2,400 vehicles: 41% fewer breakdowns and 12% fuel saving. A 24-month partnership still running on a standing monthly retainer with joint IP; ERP beta August 2026.

Custody system of record

A document custody platform for a SEBI-regulated trusteeship, built on an immutable hash-chained event ledger.

1,256 person-days across 26 weeks, estimated line by line. Architected against CSCRF 2024, DPDP Rules 2025 and the evidentiary standard of BSA 2023.

Questions

What buyers ask about this specifically.

Yes. The runtime is Red Hat Enterprise Linux, OpenShift, Kubernetes and Docker, provisioned as code, and we deploy that same stack into air-gapped estates — your data centre is the easier case. The AI layer follows the record: if it cannot leave the country, neither can the prompt containing it, so the intelligence layer runs on a model you control as readily as a hosted one. Our engineers hold RHCE, RHCSA, RHCA and OpenShift Administration, so we support that base ourselves.

Senior engineers, and the people who scope the work stay accountable for it — you are not passed to a junior team after signature. Engineers assigned to your platform work only on your platform for its duration. A bench protocol keeps additional engineers current on the codebase, so two can become six inside a sprint. Most engagements run 12–24 months past launch; the EMSPL partnership has run 24 months and is still running.

Yes — stabilise it, document it, then run it against agreed response windows, with infrastructure ownership included and a quarterly service review. Migration is part of that work: a fintech platform moved onto Red Hat cloud improved uptime by 40% and cut infrastructure cost by 25%. Delivery engineering is not a separate discipline either — telecom rollout automation cut rollout time by 60% through CI/CD pipelines, which is where most of a schedule actually lives.

Related services

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LLMs, RAG, computer vision, agentic AI.

MLOps & DevOps

Infrastructure that scales with you.

CI/CD, GPU clusters, air-gapped Kubernetes.

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