Featured
The Readiness Assessment

A 5-day engagement that maps your data, surfaces high-ROI AI candidates, and recommends a pilot — fixed price.

Read the brief
Case study
Tier-1 bank cuts reconciliation 92%

Agentic reconciliation across 14 source systems — six-week pilot, full rollout in one quarter.

Read the case
New
Private AI on dedicated GPUs

Frontier-class models on isolated infrastructure — your data never leaves the perimeter.

Explore the stack
Latest
Field notes: agentic eval at production scale

How we ship and operate eval harnesses for systems running ten-million-plus actions a month.

Read the write-up
Founder
Rohit Wakode — Founder & Director

B.Tech IIT Bombay · LLB GLC Mumbai. Building intelligent enterprise systems in India since 2014.

Read the profile
Solution · 06

AI Infrastructure & GPU

Private GPU clusters and high-throughput inference, deployed inside your perimeter.

H100/H200vLLMBYO VPC99.95% SLO

Frontier capability shouldn’t mean shipping your data to someone else’s API. We stand up private GPU infrastructure — bare metal or cloud — running open-weight models behind your firewall, operated to real SLAs.

From hardware selection through vLLM/TensorRT serving, autoscaling, and 24/7 SRE, we run the stack so your data never leaves the boundary and your costs stay predictable.

WhatPrivate GPU clusters and high-throughput inference inside your perimeter.
Best forTeams that need frontier models without sending data out.
RunsYour cloud, your colo, or Deneural-managed.
Time to valueCluster live in weeks.
01 — Capabilities

What we build.

Specific, production-grade capability — not a feature checklist.

/ 01

Private GPU clusters

H100 / H200 / L40S / A100 — bare metal or cloud, with autoscaling and spot bursting.

/ 02

High-throughput serving

vLLM, TensorRT-LLM, and SGLang tuned for your model mix and latency targets.

/ 03

In-perimeter by default

Open-weight models behind your firewall; BYO KMS; no public ingress to endpoints.

/ 04

Observability & cost

Per-token cost dashboards, utilisation, and P99 latency SLOs you can hold vendors to.

/ 05

mTLS service mesh

Zero-trust networking between services; encrypted everywhere.

/ 06

24/7 operation

Senior SREs operate the cluster to a 99.95% inference availability target.

02 — How it works

From your problem to production.

01

Size the workload

02

Stand up the cluster

03

Harden & isolate

04

Operate to SLO

STEP 01

Size the workload

We profile your models, traffic, and latency targets to right-size hardware and topology.

STEP 02

Stand up the cluster

Provision GPUs, deploy the serving stack on Kubernetes with mTLS and autoscaling.

STEP 03

Harden & isolate

Network isolation, BYO KMS, audit logging — no public ingress to model endpoints.

STEP 04

Operate to SLO

24/7 SRE, cost and latency dashboards, capacity planning as you grow.

03 — Where it pays

Use cases.

Private model hostingOn-prem inferenceRAG / copilot backendsFine-tuning infrastructureBatch inference at scaleSovereign AI deployments
04 — Engineering

Stack & standards.

Hardware
H100 / H200
L40S / A100
Bare metal or cloud
Spot bursting
Serving
vLLM
TensorRT-LLM
SGLang
Kubernetes
Security
mTLS mesh
BYO KMS
No public ingress
Audit logging
05 — Outcomes

What good looks like.

99.95%
Inference availability
Operated to a real SLO, not best-effort.
In-perimeter
Data never leaves
Open-weight models behind your firewall.
Predictable
Cost per token
Dashboards and capacity planning, not surprises.
06 — Questions

Answers, before you ask.

Cloud, colo, or on-prem?
All three. We deploy in your cloud account, your colocation, or Deneural-managed facilities — whatever your data-residency and cost profile require.
Which models can you run?
Any open-weight model (Llama, Qwen, DeepSeek, Mistral and others), plus routing to commercial APIs where appropriate. You’re never locked to one.
How do you keep costs predictable?
Autoscaling with spot bursting, per-token cost dashboards, and capacity planning — so you see and control spend rather than discovering it on a bill.
Ready when you are

Put AI Infrastructure & GPU into production.

Start with a fixed-price 5-day Readiness Assessment or a 6-week pilot. Senior engineers, measurable evals, and a system you own on handover.

Explore

Related solutions.