On this page
What we do Section Compose your stack Section AI Solutions Section What we deliver Section How we work Section Industries we serve SectionServices
AI & Intelligent Automation Service Software Development Service Cloud Solutions & Migration Service Cybersecurity & Compliance Service Data Analytics & BI ServiceInsights
What custom software actually costs in India in 2026 IT Strategy India's DPDP Rules: what the 2026 and 2027 deadlines mean for your systems Security 12 questions to ask before hiring a software development company IT Strategy Why companies build software in Bengaluru - and when they should not IT Strategy 5 cloud migration mistakes that cost enterprises millions Cloud Zero Trust in 2026: a practical roadmap for mid-market teams Security How we cut a client's data pipeline costs by 62% with AI Data & AI How to choose a managed IT services partner: a founder's checklist IT Strategy Why most RAG chatbots hallucinate - and how we fix it Data & AI What an hour of downtime actually costs a mid-market company Cloud What "AI-first" actually means (and what it doesn't) AI Is your data actually ready for AI? A 6-point readiness audit AI Build vs. buy: when a custom AI system beats an off-the-shelf tool AI How to measure AI ROI without fooling yourself AI AI governance doesn't need to be a committee - a lightweight framework for mid-market teams AIGo to
Services Page Industries Page Case Studies Page Technologies Page About Page Blog Page Contact PageNothing matches that.
We choose stacks against your constraints, not our preferences. Here is what we run in production, why we reach for each one, and the architecture principles behind the decisions.
We choose stacks against your constraints, not our preferences: the skills your team already has, your existing platform commitments, and the operational maturity needed to run the result. Every choice is recorded in an architecture decision record so the reasoning outlives the people who made it.
Follow a request down the stack
Layer 01
Interfaces that stay fast on real devices and real networks.
React
Component model for complex, stateful enterprise interfaces.
Next.js
Server rendering, streaming and edge delivery for scale.
Astro
Content-first sites that ship almost no JavaScript by default.
TypeScript
Type safety that catches integration defects before runtime.
Tailwind CSS
Token-driven styling that keeps a design system consistent.
Vue / Nuxt
A pragmatic alternative where a team already has depth in it.
Layer 02
Service layers chosen for the workload, not for fashion.
Node.js
High-concurrency I/O services sharing types with the front end.
Python
Data, ML and automation workloads with the richest ecosystem.
Java / Spring Boot
Long-lived enterprise services with deep JVM tooling.
.NET 8
Performance and Microsoft-estate integration in one runtime.
Go
Low-latency, low-footprint services and platform tooling.
GraphQL
Federated APIs that let clients ask for exactly what they need.
Layer 03
Landing zones, guardrails and infrastructure that lives in version control.
AWS
Breadth of managed services and the deepest regional coverage.
Microsoft Azure
Natural fit for identity-led, Microsoft-centric estates.
Google Cloud
Strong data, analytics and ML-native platform services.
Kubernetes
Portable orchestration for containerised workloads at scale.
Terraform
Declarative, reviewable, reproducible infrastructure.
Cloudflare
Edge delivery, WAF and zero-trust network access.
Layer 04
Stores selected for access pattern, not for the logo on the box.
PostgreSQL
The default relational choice: reliable, extensible, well understood.
Snowflake
Elastic analytical warehouse with clean compute separation.
Databricks
Lakehouse for large-scale processing and ML workloads.
MongoDB
Document storage where schema genuinely varies by record.
Redis
Caching, queues and ephemeral state at microsecond latency.
Elasticsearch
Full-text search, relevance tuning and log analytics.
Layer 05
Model access, orchestration and the evaluation layer that makes it trustworthy.
Claude (Anthropic)
Long-context reasoning for document and agentic workloads.
Azure OpenAI
Enterprise model access inside an existing Azure boundary.
Amazon Bedrock
Multi-model access with data staying in your AWS account.
LangGraph
Deterministic orchestration of multi-step agentic workflows.
pgvector
Vector search alongside relational data, without a new datastore.
MLflow
Experiment tracking, model registry and deployment lineage.
Layer 06
The golden path from commit to production, with a rollback that works.
GitHub Actions
Pipelines that live beside the code they build.
Argo CD
GitOps reconciliation so the cluster matches the repository.
Docker
Reproducible builds and true environment parity.
Helm
Templated, versioned Kubernetes releases with rollback.
Ansible
Configuration management for what has not been containerised.
Jenkins
Established pipelines we extend rather than force-migrate.
Layer 07
Knowing something is wrong before a customer tells you.
Datadog
Unified metrics, traces and logs with mature alerting.
Prometheus
Dimensional metrics and alerting, open and portable.
Grafana
Service dashboards and SLO tracking across data sources.
OpenTelemetry
Vendor-neutral instrumentation you are not locked into.
Sentry
Error tracking with release attribution and user impact.
PagerDuty
On-call rotation, escalation and incident coordination.
Layer 08
Controls that hold under test and produce evidence on demand.
HashiCorp Vault
Central secret management with dynamic, short-lived credentials.
Okta / Entra ID
Identity, SSO and lifecycle management across the estate.
Snyk
Dependency, container and IaC scanning inside the pipeline.
Trivy
Fast image and filesystem vulnerability scanning in CI.
Burp Suite
Manual and automated application penetration testing.
Vanta / Drata
Continuous control monitoring and audit evidence collection.
Architecture is a series of trade-offs made under uncertainty. What separates a good decision from a lucky one is whether the reasoning was written down and can be revisited. Every principle below comes with the condition under which we would argue against it.
Service boundaries drawn around business capability and team ownership, not around technology layers. We are equally willing to recommend a well-structured modular monolith when distribution would add cost without adding value.
Asynchronous, event-carried state transfer so services stay decoupled and available. Outbox pattern for atomicity, schema registry for compatibility, and replay for recovery and backfill.
Contracts written and reviewed before implementation, published as OpenAPI or AsyncAPI, versioned with a deprecation policy, and verified by contract tests in both consumer and provider pipelines.
Tenancy model chosen deliberately — pooled, bridged or siloed — with isolation enforced at the database layer, per-tenant configuration, and a documented path to promote a large customer to dedicated infrastructure.
Multi-AZ by default and multi-region where the business case supports it. Health-checked, self-healing services with graceful degradation, so a dependency failure narrows functionality instead of ending it.
Explicit RPO and RTO targets agreed with the business, automated backups with restore rehearsals, and infrastructure as code that can rebuild an environment from an empty account.
Structured logs, dimensional metrics and distributed traces correlated by request. SLOs with error budgets that inform release decisions, and alerts tied to user-visible symptoms rather than machine noise.
Performance budgets enforced in CI, load and soak testing before launch, database access reviewed against real query plans, and caching applied at the layer where it actually removes work.
Security work is only half technical. The other half is producing evidence — for auditors, insurers and the enterprise buyer whose security review is standing between you and a signature. We build for both from day one.
Threat modelling at design, secure code review at merge, and SAST, DAST, dependency and IaC scanning as blocking pipeline gates.
TLS 1.3 in transit, AES-256 at rest, envelope encryption for sensitive fields, and keys managed in a dedicated KMS or Vault with rotation policy.
Least privilege by default, just-in-time elevation with expiry, quarterly access recertification, and no shared or standing administrative credentials.
Immutable, tamper-evident logs on every privileged action and state change, retained to policy and queryable for audit within minutes.
Continuous scanning with severity-based remediation SLAs: critical in 24 hours, high in 7 days, tracked to closure with evidence.
Independent testing before launch and at least annually thereafter, with mandatory retest on every finding before it is marked closed.
Automated, encrypted, geographically separated backups with documented RPO/RTO and restore rehearsals on a scheduled cadence.
Control mapping and evidence packs for SOC 2, ISO 27001, HIPAA, PCI-DSS, GDPR and India's DPDP Act, maintained continuously rather than assembled at audit.
Data minimisation, purpose limitation, consent capture, retention enforcement and subject-access request tooling built into the system, not bolted on.
Our security evidence library — control mappings, penetration test summaries, DPAs and sub-processor lists — is maintained continuously and shared under NDA within one business day of request.
Request the evidence packEvery quarter a critical process stays manual, a platform stays unmodernised or a security gap stays open, the cost compounds quietly. A 30-minute conversation is enough to know whether it is worth acting on now — and we will tell you if it is not.
Or reach us directly