Whiz IT ServicesCustom software development
Web, mobile, and enterprise platforms built 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
On-premises LLMs, private AI agents and intelligent assistants—with software, GPU infrastructure, security and support from one team.
Preparing your 3D view…
Drag to rotate01 / PRIVATE INTELLIGENCE
Your AI. In your hands.
Complete delivery, under one roof
Every layer.
One accountable team.
From AI machines and system software to private models and business applications—we connect every layer.
01 / Design & size02 / Build & integrate03 / Secure & operate
FROM APPLICATION TO PHYSICAL INFRASTRUCTURE
- 06Business applications & integrationsWeb · Mobile · ERP · CRM · APIsAPPLICATIONS
- 05AI agents & intelligent assistantsControlled workflows · Copilots · ConversationsEXPERIENCES
- 04Private LLMs & enterprise knowledgeInference · RAG · Document ingestion · Vector searchINTELLIGENCE
- 03System & low-level softwareLinux · Firmware · GPU drivers · CUDA / ROCm · ContainersRUNTIME
- 02Storage, networking & platformNVMe · Networks · Orchestration · BackupsPLATFORM
- 01AI machines, GPU servers & clustersCompute · Integration · Power · Cooling · DeploymentHARDWARE
Security & operations across every layerIdentity · Host hardening · Network isolation · Encryption · Audit · Monitoring
Enterprise software engineering
Business applications.
Built to work together.
Build new applications. Connect existing systems. Keep your business moving.
OPERATIONS
03Purchase orders
OrderSupplierReview
PO-1042NorthstarReviewed
PO-1043Acme SupplyApproval
PO-1044Vertex WorksReviewed
DocumentsWorkflowMobile
TEAM REVIEWPO-1043 · Ready on mobile
Integration & modernization
Connect ERP, CRM, data, and APIs. Modernize the systems your teams rely on.
Explore system integration →Cloud & software operations
Deployment, monitoring, maintenance, and continuous improvement.
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.
AI machines, enterprise GPU servers, and clusters—built and deployed with the software and security they need.
Sized for your models, users, data, and operating requirements.
Concept illustration. Equipment is selected for your workload.
AI Machines & Workstations
Local AI development, model evaluation, and departmental workloads.
Plan an AI machine →Enterprise GPU Servers
Production inference with compute, storage, cooling, and private model serving.
Plan a private inference server →GPU Clusters
Connected GPU systems designed for larger workloads and future expansion.
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 brief27B private LLM · 10-user design targetView specifications & GPU options
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 ecosystem.
Private models. Controlled automation. Useful answers.
Summarise this service report.
RequestPrivate LLMResponse ready
RESPONSE
Routine service completed. One follow-up inspection is required.
Request and inference within your environment
01 / Model foundation
On-Premises LLMs
Private model serving, optimized and integrated with your applications.
Plan private inference →REVIEW REQUESTPurchase order · PO-1043
SampleRead request01
Check records02
Prepare update03
Human approval04
Awaiting human approvalNo change is applied automatically.
02 / Controlled action
Private AI Agents
Automate approved workflows with scoped tools, human review, and audit records.
Explore AI agents →Where is the approved travel policy?YOU
Answer with a source
The approved policy is in the HR library. Check its effective date before use.
REFERENCED DOCUMENTTravel policy.pdfHR library · Approval status included
Ask your company knowledge…↑
03 / Everyday assistance
Intelligent AI Assistants
Help teams find answers and work with approved organizational knowledge.
Try an assistant example →How will AI use your data?
Your knowledge.
Put to work.
Try an example: answers, workflows, or 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.
Find answers in approved sources, with references your teams can inspect.
- 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.
Set tool permissions, approval gates, and failure handling. Keep an execution record.
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.
Extract and validate documents. Review exceptions before updating business records.
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 at every layer.
Control at every step.
Identity, data, hosts, and networks—protected through an agreed operating plan.
Identity & data access
Roles, identity, and source permissions.
Scoped tools & approvals
Permitted actions. Human approval gates.
Host & network security
Host hardening, segmentation, and encryption.
Resilience & operations
Monitoring, backups, recovery, and patching.
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
Updates follow an agreed support plan, with validation and rollback before release.
Built around your industry
Practical AI.
Real business contexts.
Choose a starting point for your team.
Clinical knowledgeExample
Find the approved care protocolClinical library · Source attached
Source foundHealthcare
Approved knowledge for care teams.
Procedures and operational guidance, with source references.
Explore healthcare →
Digital recordsExample
Application REC-204Indexed records · Review queue
Ready for reviewGovernment
Documents into useful services.
Controlled records workflows and multilingual information.
Explore government →
Document reviewExample
Income statement · Date mismatchException flagged for a reviewer
Human reviewBanking & Finance
Review records with confidence.
Extraction, validation, and human exception review.
Discuss finance workflows →
Maintenance assistantExample
Line 04 · Service guideEquipment manual · Source attached
Manual locatedManufacturing
Operational knowledge, on hand.
Approved maintenance guidance for industrial teams.
Explore manufacturing →AI-generated scenes and sample interfaces illustrate potential use cases.
Illustrative reference architectureHow a private knowledge assistant connectsView architecture & validation criteria
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 your use case. We’ll plan the software, AI, infrastructure, and validation together.
- Your use case and users
- New hardware or existing infrastructure
- Performance and security requirements
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
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Share a short outline. Our team will contact you to discuss it.