Whiz IT ServicesCustom software development
Web and mobile applications, enterprise platforms, and customer or employee portals shaped around your business.
Explore application development →Enterprise Software, Private AI & Infrastructure
Software. AI.Infrastructure.Built as one.
Every layer. One engineering partner.
- Enterprise software
- Private AI & RAG
- On-premises AI servers
We build enterprise applications, private AI, and the infrastructure that runs them. From AI machines and GPU servers to system software, RAG, agents, and infrastructure security—designed, integrated, and deployed under one roof.
Software + intelligence + infrastructure
From business applications
to on-premises AI.
Web & mobileBusiness applications
ERP & CRMConnected operations
WorkflowsPeople & processes
Approved knowledge · Enterprise data · APIs
01Private LLMsUnderstand & generate
02AI agentsPrepare & act
03AI assistantsRetrieve & support
System softwareLinux · GPU drivers · CUDA / ROCm · Containers
AI machinesGPU serversGPU clusters
Storage · Networking · Power & cooling
Illustrative stack · Built for your premises
Complete delivery, under one roof
Every layer.
One accountable team.
Whiz IT engineers the complete system: physical infrastructure, low-level software, private AI, business applications, and the controls that connect them.
Build a new on-premises AI environment or extend your existing estate. We bring the hardware and software work together, from design and integration through deployment and support.
01 / Design & size02 / Build & integrate03 / Secure & operate
FROM APPLICATION TO PHYSICAL INFRASTRUCTURE
- 06Business applications & integrationsWeb, mobile, ERP, CRM, APIs, and business workflowsAPPLICATIONS
- 05AI agents & intelligent assistantsControlled automation, enterprise copilots, and conversational AIEXPERIENCES
- 04Private LLMs & enterprise knowledgeModel serving, RAG, document ingestion, and vector searchINTELLIGENCE
- 03System & low-level softwareLinux, firmware configuration, GPU drivers, CUDA / ROCm, and containersRUNTIME
- 02Storage, networking & platformNVMe storage, network design, orchestration, and backup planningPLATFORM
- 01AI machines, GPU servers & clustersCompute sizing, system integration, power, cooling, and on-site deploymentHARDWARE
Security & operations across every layerIdentity · Host hardening · Network isolation · Encryption · Audit · Monitoring
Enterprise software engineering
Business applications.
Built to work together.
From new products to established enterprise systems, we design, develop, integrate, and support the software your teams use every day.
Integration & modernization
Connect applications, ERP, CRM, databases, and APIs. Modernize existing systems while preserving the workflows that matter.
Explore system integration →Cloud & software operations
Plan deployment, release processes, monitoring, maintenance, and ongoing improvements across your software lifecycle.
Explore managed operations →Explore our software platforms
Built for knowledge, care, governance, and commerce.
On-Premises AI Infrastructure
AI machines. GPU servers.
Built for your premises.
We design, build, integrate, and deploy on-premises AI infrastructure—from a dedicated AI machine to enterprise GPU servers and multi-GPU clusters. Hardware, system software, private AI, and infrastructure security are delivered together.
Sized around your models, users, data, response targets, and operating environment.
Concept illustration. Equipment is selected for your workload.
AI Machines & Workstations
Integrated machines for local AI development, model evaluation, private conversations, and departmental workloads.
Plan an AI machine →Enterprise GPU Servers
On-premises GPU servers with validated compute, memory, storage, networking, cooling, and private model serving.
Plan a private inference server →GPU Clusters
Multi-GPU and multi-node systems with compute interconnects, shared storage, orchestration, and expansion planning.
Discuss a multi-GPU system →Swipe to explore hardware
1 of 3
Hardware engineeringGPU selection, server build, storage, networking, power & cooling
System softwareLinux, drivers, GPU runtime, containers & inference optimization
Secure deploymentHardening, access control, backups, monitoring & handover
Example deployment brief
A private 27B LLM.
Your knowledge. On your premises.
Private conversations and retrieval-augmented generation (RAG), using approved organizational documents and a vector database. Knowledge is retrieved at query time; adding documents does not require retraining the base model.
- Model configuration
- 27B 4-bit quantization
- Concurrency target
- 10 active conversations
- Per-user speed target
- 20–30 output tokens / second
- Deployment boundary
- On-premises client-controlled data storage
Benchmark before commitment. Aggregate throughput target: 200–300 output tokens/second. Validate the selected model, quantization method, context length, KV-cache memory, inference runtime, and 10 concurrent requests. A 24×7 operating requirement also needs an agreed resilience, cooling, power, and support design.
Compare three proposed GPU configurations
Candidate configurations for technical evaluation. GPU memory capacity alone does not establish model compatibility, concurrent-session capacity, or achieved performance.
| Parameter | NVIDIA GeForce RTX 5090 | AMD Instinct MI210 | NVIDIA RTX PRO 6000 Blackwell Server Edition |
|---|---|---|---|
| Architecture | Blackwell | CDNA 2 | Blackwell |
| GPU memory | 32 GB GDDR7 | 64 GB HBM2e, ECC | 96 GB GDDR7, ECC |
| GPU power rating | 575 W | 300 W | Up to 600 W |
| GPU software ecosystem | CUDA | ROCm | CUDA |
| Hardware class | Consumer GPU; assess cooling and serviceability | Data-centre accelerator; validate platform and software support | Data-centre server GPU; validate chassis cooling and power |
| Proposed CPU | AMD EPYC, 16–24 cores | AMD EPYC, 16–24 cores | AMD EPYC, 16–24 cores |
| Proposed system RAM | 128 GB ECC | 128–256 GB ECC | 256 GB ECC |
| Proposed storage | 4 TB NVMe SSD | 4 TB NVMe SSD | 4 TB NVMe SSD |
| Proposed networking | 10 GbE | 10 GbE | 10 / 25 GbE |
| Proposed operating system | Ubuntu Server | ROCm-compatible Linux | Ubuntu Server |
Candidate application stack: vLLM or a compatible inference runtime, Qdrant for vector search, and Python FastAPI for application APIs. Versions, quantization kernels, licensing, GPU support, and availability are validated before the final bill of materials.
Hardware references: NVIDIA RTX 5090 ↗ · AMD MI210 datasheet ↗ · RTX PRO 6000 server specifications ↗
Private AI, engineered into your software
Three AI capabilities.
One connected software ecosystem.
Bring intelligence into your applications and workflows, with private models, controlled agents, and assistants connected to approved knowledge.
Illustrative model serving
Organizational applications
Private model endpoint
Deploy · optimize · manage
Deploy · optimize · manage
01 / Model foundation
On-Premises LLMs
Deploy and integrate suitable language models with your applications, including inference optimization, customization, evaluation, and lifecycle management.
Discuss a private LLM application →Illustrative controlled workflow
Review requestCheck sourcesHuman approvalExecute & log
Permissions define what can happen.02 / Controlled action
Private AI Agents
Connect approved tools and enterprise workflows. Use scoped permissions, human approvals, orchestration, and execution records to control automation.
Explore controlled agent workflows →Illustrative assistant
Where is the approved travel policy?
Find the approved policy and inspect its source.
03 / Everyday assistance
Intelligent AI Assistants
Help teams work with organizational knowledge through chat, voice, document, and enterprise copilots connected to approved systems.
See a knowledge assistant example →How will AI use your data?
Your knowledge. Useful answers.
Controlled actions.
Explore illustrative examples of answers, controlled workflows, and document processing.
Knowledge AssistantIllustrative example
Where is the approved travel policy?
Illustrative response · no live data connection
The approved policy is in the HR policy library. Check its approval status and effective date before using it. Your access permissions determine which sources are available.
Inspect example source · Travel Policy / HR library
Demonstration source only. An actual implementation would open the permitted policy record and show its owner, approval status, and effective date. This illustrative example is not connected to a policy document.
Find answers with context.
Connect approved documents and enterprise sources to permission-aware retrieval. Show sources so users can inspect the evidence behind an answer.
- Respect source access permissions
- Keep knowledge fresh
- Evaluate retrieval and answer quality
Purchase Request AgentIllustrative example
1 · Review request2 · Check approved information3 · Propose action4 · Human approval5 · Execute & log
Approval required before execution. The agent may prepare a proposed action. A permitted reviewer decides whether it proceeds.
If blocked: stop & notify reviewerIf failed: record & retry by policy
Give automation clear limits.
Define tool permissions, approval points, failure handling, and audit records. Multi-agent orchestration is used where the workflow benefits from it.
Discuss an enterprise agent →Document AssistantIllustrative example
Review this supplier document for required fields.
Draft extraction for human review
Extract fieldsApply validation rulesReview exceptionsApprove enterprise record
Missing or uncertain values are flagged for review. No document has been uploaded or processed in this example.
Move from documents to reviewed records.
Combine local intake, OCR, extraction, validation, and human review before sending approved data to an enterprise workflow.
Discuss document intelligence →01 / APPROVED SOURCESDocuments · databases · ERP · CRM
02 / PERMISSION-AWARE KNOWLEDGEIndex, retrieve, and respect access
03 / PRIVATE MODELGenerate within the approved boundary
04 / ANSWER OR ACTIONReferences, approvals, and audit controls
Technical detail: RAG, CAG, KAG, and GraphRAG
These are architectural options, selected through evaluation rather than mandatory products. We assess data relationships, update frequency, retrieval quality, access boundaries, and model behavior. Optional external connections are explicitly agreed; they are not assumed to be part of a private deployment.
Can you operate it with control?
Security across the system.
Improvement through its lifecycle.
Security spans your applications, AI layers, Linux hosts, networks, and data storage. We design the controls and operating responsibilities alongside the system.
Identity & data access
Assess identity integration, roles, and source permissions.
Scoped tools & approvals
Define permitted actions and human review boundaries.
Host & network security
Harden hosts, segment networks, encrypt communications, and control approved external connections.
Resilience & operations
Plan logs, backups, recovery, monitoring, patching, and operational ownership across hardware and software.
Continuous upgrades, with a release decision.
- MonitorObserve the system
- EvaluateAssess quality and risk
- StageTest the proposed change
- ApproveMake a release decision
- ReleaseDeploy with rollback ready
Rollback is planned before release. Model updates, knowledge refresh, security maintenance, and performance tuning follow an agreed support scope.
Industries & Technical Proof
Start with a use case.
Validate the system behind it.
Example use cases, tailored and validated for your industry.
Healthcare
Hospital knowledge assistance
Help staff find approved procedures and administrative information with source references.
Explore healthcare solutions →Government
Controlled document intelligence
Support approved records workflows and multilingual service information.
Explore government solutions →Banking & Finance
Reviewed document processing
Extract and validate internal records with access controls and exception review.
Discuss financial workflows →Manufacturing
Industrial knowledge assistance
Help teams retrieve approved maintenance guidance and operational knowledge.
Explore manufacturing solutions →Illustrative Reference Architecture
A private knowledge assistant for internal operations.
Problem: approved knowledge is distributed across documents and enterprise systems.
Design: a permission-aware knowledge layer connects approved sources to private model serving and an authenticated assistant.
Validate: access boundaries, retrieval quality, answer accuracy, source traceability, latency, and load behavior.
Talk to an AI Architect →Approved organizational sourcesDocuments · permitted enterprise data
Permission-aware retrievalIdentity · indexing · source metadata
Private model → authenticated assistantGrounded answers · source inspection · audit
Concept architecture. Hardware, model licensing, source access, and performance are validated during discovery.
UDICHI · Private AI platform initiative. Ask our team about available capabilities and demonstrations. Meet the Whiz IT leadership →
Engineering-led delivery
A practical path
to private AI.
- Discover & designAgree users, data, integration, security, and acceptance criteria.
- Build & validateTest the proposed system with a focused proof of concept.
- Deploy & evolvePlan release, rollback, operational ownership, and ongoing support.
Technology leadership · Vivek, CTO
Experience across distributed systems, AI, and cybersecurity.
Questions before you start
Do you deliver hardware and software together?
Yes. We design, build, integrate, and deploy AI machines, GPU servers, storage, networking, system software, private models, RAG, agents, assistants, and business applications. The scope includes infrastructure security, validation, handover, and an agreed support plan.
Can we use our existing hardware?
Often the starting point is an assessment of what you already have. Suitability depends on models, memory, workload, compatibility, and operating requirements.
How does AI connect to our organizational data?
We define approved sources and permissions, then evaluate a knowledge and integration architecture around your use case. Access boundaries apply to retrieval and tool actions.
Does the system need an internet connection?
Some designs can operate offline. Air-gapped delivery requires reviewing dependencies, provisioning, telemetry, integrations, and controlled update paths.
How are agents prevented from taking unapproved actions?
Scoped tools, role-based permissions, approval gates, execution limits, and audit logs define what an agent may do and when a person must review it.
What happens after deployment?
The engagement defines monitoring, evaluation, knowledge refresh, model upgrades, security maintenance, release controls, rollback, and agreed support coverage.
Talk to an AI Architect
Plan your complete
AI environment.
Tell us what you need across software, private AI, AI machines, GPU servers, system software, and infrastructure security. We will define the delivery and validation plan together.
- What applications, AI capabilities, and users should it support?
- Do you need new on-premises hardware or integration with existing systems?
- What performance, security, and operating requirements should we validate?
Please do not submit credentials, confidential records, or sensitive production data.
contact@whizit.co.in
+91 808 528 4440
contact@whizit.co.in
+91 808 528 4440
Tell us where you want to start.
Share a short outline. Our team will review your requirements and contact you to discuss the next step.