Your models. Your data.
Your compute. Your terms.
Choose the right model, build the dataset, fine-tune it, and run it on compute you control.
Neural Infrastructure helps organisations lower their dependency on frontier models and the cost of running AI. Then it puts that intelligence to work: AI employees for growth teams, KshetraIQ for learning and development teams. Every deployment is measured on one cockpit, cost and output side by side.
Compute. Services. Solutions.
Three layers, one team. Start at whichever layer your problem lives in, and move between them without changing vendors.
Run it where it costs least.
Owned GPUs, cloud GPUs or your own keys, sized to the model. Plus open-weight models and curated datasets you hold yourself.
Make an open model yours.
Fine-tuning and small language models that beat their base model blind, and cut frontier dependency, cost and latency.
Put it to work.
AI employees for marketing and revenue teams. KshetraIQ for learning and development teams. Same stack, human in the loop.
From model to production in four moves.
Choose the model
An open-weight base picked for the job and the hardware, not the leaderboard. Frontier APIs only where they earn their cost.
Build the dataset
Hand-reviewed exemplars, a designed scenario matrix, quality gates and a leak test. The dataset is the moat.
Fine-tune and evaluate
LoRA, DPO or GRPO, judged blind against the base model on held-out prompts. If it does not win, it does not ship.
Run on your compute
Owned GPUs, cloud GPUs or your keys. Monitored on one cockpit: cost, output and approvals side by side.
Intelligence, put to work.
Two solutions on the same infrastructure. Isolated instance per client, a human in the loop, every action logged, cost and output on one cockpit.
AI Employees. A workforce, not a chatbot.
Persistent AI employees with a job title, tools, memory and outcome ownership: a marketing intelligence agent, a brand-locked writer, an analyst, an SDR, a project manager.
- Hire a role, run a fleet, or own the stack
- 900+ tools connected with scoped access
- Human approval on high-stakes actions
KshetraIQ. Pitch intelligence for the frontline.
Recorded sales pitches from the counter and the field, audited against your product catalogue and training rubric: a score out of 100, four justified dimensions, coaching in the rep’s own language, benchmark comparison, and an L&D dashboard.
- Pitch Audit: score, four dimensions, transcript with visual context
- Best Pitches & Compare: benchmark overlay and delta coaching report
- L&D Overview: pass rate, elite rate, region health matrix
Secure by architecture.
Every client runs on an isolated instance with scoped credentials, human approval on high-stakes actions, and full audit logs. Servers expose HTTPS only. Admin access rides identity-gated tunnels, not open ports. Deploy in your own VPC and your data never leaves your walls.
A whole business already runs on it.
RYVR is a marketing agency operated by AI employees on Neural Infrastructure: an autonomous CMO, SDR, Content Writer, Finance Manager, and a self-improving Skill Keeper, working the same tools your team uses. Cost, output and approvals sit side by side on one cockpit. Not a roadmap. A working business.
Strategy and engineering, in the same room.
One founder has run go-to-market and revenue operations for mid-market and enterprise firms. The other builds and ships the models. We eat our own cooking: RYVR runs on this stack.
Jigesh Shah
Runs Neural Infrastructure and RYVR, the marketing agency operated end to end by AI employees on this platform. Before that, he built RevOps and go-to-market systems for mid-market and enterprise firms. His thesis: the barrier to enterprise AI is not the models, it is the infrastructure around them.
Pratham Shah
Applied ML research engineer working on vision-language models, fine-tuning (QLoRA, DPO, GRPO) and small-model deployment on constrained hardware. Open-source contributor to Google’s JAX/Flax vision model stack. Research at BARC, IIT Bhilai and TIFR; ML engineering at the State Bank of India. B.Tech in Computer Science, VIT Vellore.
Book a working session.
We’ll review your current stack and infrastructure requirements, then map out the right deployment plan. No pitch deck. Just a technical conversation.