01
Product Engineering
Build reliable, scalable digital products from idea to production.
- Product architecture
- Web applications
- Mobile applications
- APIs
- Microservices
- +2 more
Build.Scale.Modernize.Automate with AI.
Engineering the future of digital products
From mobile and web applications to data platforms, cloud infrastructure, DevOps and AI — we help businesses build, modernize and scale technology that delivers real-world impact.
From product engineering to AI-powered transformation, we provide the expertise to take your technology forward.
ProductWeb · Mobile
APIsREST · gRPC
DataLakehouse
CloudAWS · Azure · GCP
DevOpsCI/CD · IaC
AILLMs · RAG · Agents
AnalyticsBI · Real-time
- Product Engineering
- Mobile Development
- Frontend Development
- Backend Development
- Data Engineering
- Data Analytics
- Cloud & DevOps
- AI & GenAI
What we do
Technology expertise across the entire product lifecycle.
One senior team for the product, the platform underneath it and the intelligence built into it — so nothing gets lost between vendors.
02
Mobile Engineering
Native and cross-platform mobile solutions.
- Android
- iOS
- React Native / Flutter
- Mobile architecture
- API integration
- +3 more
03
Frontend Engineering
Modern, fast and scalable user experiences.
- React
- Next.js
- TypeScript
- Design systems
- SSR/SSG
- +2 more
04
Backend Engineering
Build secure and scalable backend systems.
- Java / Spring Boot
- Node.js
- Python
- Go
- REST APIs
- +4 more
05
Data Engineering
Turn raw data into a reliable data platform.
- Data lakes
- Data warehouses
- ETL/ELT
- Streaming pipelines
- Kafka
- +6 more
06
Data Analytics & BI
Turn data into actionable business insights.
- Analytics platforms
- BI dashboards
- KPI frameworks
- Product analytics
- Customer analytics
- +3 more
07
Cloud & DevOps
Build reliable, automated and observable infrastructure.
- AWS / Azure / GCP
- Cloud architecture
- Infrastructure as Code
- Docker
- Kubernetes
- +6 more
08
AI & Generative AI
Help businesses adopt AI where it creates real value.
- Generative AI
- LLM integration
- AI agents
- RAG systems
- AI-powered search
- +7 more
Solutions by business need
Start with the problem. We'll map the path.
Pick the statement closest to where you are today.
AI-powered technology stack
AI isn't a separate layer. We help you embed intelligence across your technology ecosystem.
Product · with AI
AI-powered product features
Summaries, smart defaults, semantic search and generation built into the core product experience.
- Semantic search
- Content generation
- Smart onboarding
AI enablement
Don't just adopt AI. Build it into your business.
Most organizations already have access to capable AI tools. The hard part is everything around them: connecting models to the right data, fitting them into real workflows, proving they're accurate, and keeping them secure and affordable in production.
We close that gap — combining AI expertise with the product, data and platform engineering needed to make it work day to day.
The AI adoption path
- 1Experiment — Teams try AI tools individually
- 2Pilot — A promising prototype or demo
- 3Integrate — Connected to data, systems & workflows
- 4Operate — Evaluated, governed, measured
01
AI Readiness Assessment
Identify where AI can create measurable business value across your processes, data and systems.
02
AI Strategy
Define practical AI use cases aligned with business objectives, with owners, metrics and a roadmap.
03
AI Integration
Integrate LLMs and AI capabilities into your existing applications and APIs — no rebuild required.
04
RAG & Knowledge Systems
Build secure AI systems that work with company-specific knowledge, permissions and citations.
05
AI Agents
Automate multi-step workflows with tool-using agents and human approval where it matters.
06
AI Automation
Reduce repetitive manual processes like document handling, data entry, triage and reporting.
07
AI-powered Analytics
Let business users interact with governed data using natural language.
08
AI Engineering Enablement
Help engineering teams adopt AI-assisted development practices safely and productively.
Data + AI
Great AI starts with great data.
Models are only as useful as the data they can reach. We build the complete foundation required for AI adoption — from the first ingestion job to the application your users see.
- Data ingestion
- Data pipelines
- Data lakes
- Warehouses
- Data governance
- Feature engineering
- Vector databases
- Knowledge systems
- ML/AI pipelines
- Analytics
- AI applications
- 01Data SourcesApps, databases, SaaS, events, documents
- 02Data EngineeringIngestion, CDC, streaming, ELT
- 03Data PlatformLakehouse, warehouse, catalog
- 04Data QualityContracts, tests, lineage, SLAs
- 05AnalyticsSemantic layer, BI, product analytics
- 06AI / MLFeatures, embeddings, RAG, models
- 07Business DecisionsDashboards, copilots, automation
Cloud & DevOps
From code to production — reliably.
Automated, observable delivery on AWS, Azure or GCP. Shipping becomes routine; incidents become rare and well understood.
- 01
Code
Trunk-based, reviewed, linted
- 02
Build
Reproducible, cached artifacts
- 03
Test
Unit, contract, security scans
- 04
Deploy
GitOps, canary, instant rollback
- 05
Observe
Metrics, logs, traces, SLOs
- 06
Optimize
Performance, cost, reliability
Users
web · mobile
CDN / WAF
edge
Load balancer
TLS
Kubernetes · autoscaling
API gateway
auth · rate-limit
Core services
domain APIs
AI service
LLM · RAG
Workers
events · jobs
PostgreSQL
primary · replica
Redis
cache
Kafka
event stream
Object store
lakehouse
Platform
Metrics & traces
OpenTelemetry
Logs
centralized
SLO alerts
on-call
IaC
Terraform
GitOps
CI/CD · ArgoCD
What we set up
- Cloud architecture
- AWS / Azure / GCP
- CI/CD
- Infrastructure as Code
- Docker
- Kubernetes
- Monitoring
- Logging
- Security
- Reliability engineering
- Disaster recovery
- Cloud cost optimization
Engineering coaching
Build better engineering teams.
“We don't just build systems. We help teams become better at building them.”
Hands-on coaching and technical mentorship from practitioners — on your real codebase, architecture and delivery process, not generic workshops.
- 01Engineering leadership coaching
- 02Architecture reviews
- 03System design coaching
- 04Code quality improvement
- 05Engineering best practices
- 06Data engineering mentorship
- 07Cloud & DevOps coaching
- 08AI engineering enablement
- 09Technical interview preparation
- 10Engineering process improvement
- 11Team capability assessment
How we work
A clear path from first conversation to production at scale.
Five stages, each with concrete outputs. You always know what's happening, what's next and why.
- 01
Discover
Understand the business, users, technology and objectives.
- → Stakeholder interviews
- → System & data audit
- → Success metrics
- 02
Architect
Design the right technical architecture and roadmap.
- → Target architecture
- → Decision records
- → Phased roadmap
- 03
Build
Develop using modern engineering practices.
- → Thin vertical slices
- → Automated tests
- → Weekly demos
- 04
Launch
Automate deployment and establish production readiness.
- → CI/CD pipelines
- → Observability & SLOs
- → Runbooks
- 05
Scale
Optimize performance, reliability, data and infrastructure.
- → Load & cost tuning
- → Reliability reviews
- → Knowledge transfer
Engagement models
Flexible ways to work together.
Plug in a senior team, bring in leadership, or get targeted expertise — and change the model as your needs evolve.
Project-Based
End-to-end delivery of a defined technology project with clear scope, milestones and ownership.
Best for · New products, migrations, platform builds
Dedicated Engineering Team
A senior technical team working as an extension of your organization, in your rituals and tools.
Best for · Sustained roadmaps, scaling capacity
Technology Consulting
Architecture, technology strategy and transformation initiatives led by experienced practitioners.
Best for · Critical decisions, due diligence, roadmaps
Fractional Engineering Leadership
Experienced technical leadership — CTO, architect or head of data — without a full-time hire.
Best for · Early-stage companies, leadership gaps
AI Enablement
Help your teams identify, build and deploy practical AI capabilities with measurable outcomes.
Best for · AI roadmaps, pilots to production
Engineering Coaching
Upskill existing teams and improve engineering practices through hands-on mentorship.
Best for · Growing teams, quality and delivery
Technology stack
Proven, modern tools — chosen for the problem, not the trend.
We're pragmatic about technology. These are the ecosystems our engineers work in day to day.
Languages
6- Java
- Python
- Scala
- JavaScript
- TypeScript
- Go
Frontend
2- React
- Next.js
Backend
4- Spring Boot
- Node.js
- Python
- Go
Mobile
4- Android
- iOS
- React Native
- Flutter
Data
7- Spark
- Kafka
- Flink
- Databricks
- Iceberg
- Hudi
- Trino
Databases
5- PostgreSQL
- MySQL
- MongoDB
- Redis
- ClickHouse
Cloud
3- AWS
- Azure
- GCP
DevOps
4- Docker
- Kubernetes
- Terraform
- CI/CD
Analytics
4- Superset
- Power BI
- Tableau
- Looker
AI
6- OpenAI
- LLMs
- RAG
- Vector Databases
- AI Agents
- ML pipelines
Industries
Cross-industry engineering, grounded in domain context.
The engineering fundamentals are universal. What changes is the domain — its data, regulations, traffic patterns and users.
FinTech
Secure, auditable systems where correctness is non-negotiable.
- ›Ledger & payments services
- ›Real-time fraud signals
- ›Regulatory reporting pipelines
E-commerce
Fast storefronts and data that turns browsing into buying.
- ›Headless commerce frontends
- ›Recommendation engines
- ›Inventory event streams
Gaming
Backends and analytics built for spiky, global traffic.
- ›Player telemetry at scale
- ›Live-ops dashboards
- ›Matchmaking & leaderboards
SaaS
Multi-tenant platforms that scale with every new customer.
- ›Tenant isolation & billing
- ›Usage-based metering
- ›In-product AI copilots
Healthcare
Privacy-first systems for clinical and operational data.
- ›Consent-aware data platforms
- ›Document intelligence
- ›Care operations analytics
EdTech
Learning platforms that personalize and scale.
- ›Adaptive learning paths
- ›AI tutors with RAG
- ›Learner engagement analytics
Logistics
Real-time visibility across fleets, hubs and partners.
- ›Live shipment tracking
- ›Route & ETA prediction
- ›Partner API integrations
Media
Content platforms built for reach, speed and engagement.
- ›Content recommendation
- ›Streaming analytics
- ›Automated metadata tagging
Retail
Unified customer and inventory data across every channel.
- ›Omnichannel data platforms
- ›Demand forecasting
- ›Store operations apps
Startups
Senior engineering from day zero, without a senior headcount bill.
- ›MVP to v1 builds
- ›Fractional CTO
- ›Investor-ready architecture
Engagement patterns
The kind of problems we solve.
Representative engagement patterns showing how we approach common challenges — the problem, the solution shape and the outcomes we design for.
Data & Analytics01
Data Platform Transformation
- Challenge
- Fragmented data across operational databases and SaaS tools, with slow, inconsistent analytics that leadership had stopped trusting.
- Solution
- A modern lakehouse on open table formats, CDC and streaming ingestion, a governed semantic layer and role-based dashboards.
- Impact
- Faster analytics, improved pipeline reliability and lower infrastructure cost — with one agreed definition for every core metric.
- Kafka
- Spark
- Iceberg
- Trino
- Superset
Cloud & DevOps02
Cloud & DevOps Modernization
- Challenge
- Manual deployments, snowflake servers and operational complexity that made every release a late-night event.
- Solution
- Cloud landing zone, infrastructure as code, containerized workloads, CI/CD with progressive delivery and end-to-end observability.
- Impact
- Faster, lower-risk releases, improved reliability and clear visibility into system health and cost.
- AWS
- Terraform
- Kubernetes
- GitHub Actions
- OpenTelemetry
AI Enablement03
AI Enablement for Knowledge Workflows
- Challenge
- Teams spent hours searching documents and re-keying information across manual knowledge and approval workflows.
- Solution
- A permission-aware RAG system, LLM-powered extraction and workflow automation with human review and evaluation dashboards.
- Impact
- Reduced manual effort, faster decision-making and answers grounded in cited company sources.
- LLMs
- RAG
- pgvector
- Python
- Workflow automation
Why teams choose us
A senior team that takes responsibility for outcomes.
End-to-End Expertise
Product → Engineering → Data → Cloud → AI. One team that understands how the layers affect each other.
Senior Engineering Approach
Architecture and engineering decisions designed for long-term scalability, not just the next demo.
Business-First Technology
Every technical decision is connected to a measurable business outcome that you agree with upfront.
AI-Ready Architecture
Systems built with the data, APIs and observability that let them evolve with AI.
Practical Delivery
Focus on production-ready solutions rather than prototypes that never ship.
Knowledge Transfer
We help your teams understand, operate and evolve the systems we build — then step back.
About Velrigen
Built by engineers who have designed and operated real-world technology systems.
Velrigen is a group of experienced technology professionals who have spent their careers building products, data platforms and cloud infrastructure — and being on call when they broke. We started Velrigen to offer the kind of partner we always wanted to work with: senior, honest, hands-on, and accountable for results.
Collective expertise
- Engineering
- Data
- Analytics
- Cloud
- DevOps
- Product development
- AI
Senior by default
The people you meet are the people who build.
Remote-first
Based in India, working with teams worldwide.
Insights
Practical thinking on AI, data, cloud and engineering.
Data Engineering
How to Build an AI-Ready Data Platform
AI projects rarely fail because of the model. They fail because the data underneath isn't trustworthy, accessible or governed. Here's what an AI-ready foundation actually needs.
· 8 min read
Generative AI
RAG vs Fine-Tuning: What Should Your Business Use?
Both techniques customize how an LLM behaves, but they solve different problems. A practical framework for choosing — and why the answer is often both, in the right order.
· 7 min read
Data Analytics
Designing Data Platforms for Real-Time Analytics
Real-time is a spectrum, not a switch. How to decide what actually needs sub-second freshness, and how to architect streaming without doubling your operational burden.
· 9 min read
Contact
Have a technology challenge? Let's solve it.
Tell us what you're building, what isn't working, or where you want to go next.
- 1
We read every message
A senior engineer reviews your note — not an automated funnel.
- 2
A focused first call
30–45 minutes to understand the problem, constraints and goals.
- 3
A clear recommendation
Honest next steps, whether or not we're the right fit.
- info@velrigen.com
- India · Working with teams worldwide
- We typically reply within one business day