Self-hosted inference, fine-tuning, agents, training, vision, and sovereign AI Factories.
Explore AI SolutionsClustered compute for molecular dynamics, CFD, climate modelling and materials science.
Explore HPC SolutionsAdobe CC, 3D/VFX, virtual production, game dev and audio, on one hardware line.
Explore Pro CreatorsGenesys Labs designs, builds and deploys AI and HPC infrastructure for organisations across India — from GPU workstations to sovereign AI Factories. The company is based in Kochi, Kerala, and the team has been building infrastructure for around ten years.
The team started in conventional IT systems and smaller infrastructure projects. Over roughly the last six years the work moved decisively upmarket — into large server deployments, HPC, AI infrastructure and data-centre-scale projects. Today that experience is focused on AI and HPC infrastructure, sovereign AI, AI Factories, and the services around them — designed, built and supported from Kochi.
Founder, Genesyslabs
Co-Founder
On-prem isn't universally better, and a partner who claims otherwise is optimizing for their own invoice. Cloud wins on fast starts, elasticity and experimentation. On-prem wins above a utilization threshold, and on data control, latency, egress cost and architectural freedom.
| Cloud is stronger at | On-prem / private AI is stronger at |
|---|---|
| Fast start, no infrastructure ownership | Data control and sovereignty |
| Elasticity and managed services | Predictable cost at sufficient utilization |
| A broad service ecosystem | Lower egress cost and lower latency |
| Experimentation and short projects | Custom architecture and hardware control |
| — | Security, independence, long-term utilization economics |
Which side you land on depends on workload, utilization, compliance, data sensitivity, latency, scale and financial model. That's what the TCO engagement is for.
TCO & Business CaseSovereign AI means the customer controls the infrastructure, the models, the data and the operations. Not a hyperscaler region that happens to be in-country — physical infrastructure under the customer's control, in a location the customer chooses, operated under their own access policies. For regulated industries, public-sector workloads, critical infrastructure and anyone whose IP is the business, that distinction is the whole requirement.
Sovereign AI EnablementMost vendors sell one layer of a five-layer problem — the boxes, or the orchestration, or the application. Genesys Labs designs and builds the physical and fabric layers, deploys the runtime, builds the platform layer as GLIDS, and partners on the application layer.
The stack, layer by layerWe don't sell boxes against a spec sheet someone else wrote. We don't quote a GPU count before understanding the workload. And we don't put a number on a benchmark we haven't run on your configuration.
NVIDIA Studio Certified · NVIDIA GeForce Authorized System Integrator · Intel Gold Partner · AMD Arena Member
Selected by NVIDIA as one of six Indian system builders for its Made in India RTX AI PCs. MSME registered.
Systems deployed for AI workloads are built on the GLabs AI Stack and deployed and validated through GLIDS — the GLabs Intelligent Deployment System. Every other deployment ships under GLIDS alone. GLIDS is not something sold separately: it's the standard every system is delivered under, Pro Grade or GLabs Enterprise, before it reaches your floor. The four stages below are how GLIDS deploys.
| Stage | Detail |
|---|---|
| Validate | Burn-in testing and a GenesysBench™ benchmark pass before handover — no system ships on spec-sheet numbers alone. |
| Standardize | A single GLIDS base image on every GLIDS deployment — the same baseline on one workstation or a 2,000-node cluster. |
| Integrate | Racking, cabling, network fabric and power validated on-site — or pre-cabled at the factory for POD-class systems — before go-live. |
| Support | One SLA and support playbook across the entire line, Edge AI through AI Factory. |
A solutions architect responds directly — no ticket queue.
