Private AI,
Deployed Inside Your
Own Servers.
For banks, insurance companies, NBFCs, healthcare teams, and compliance-driven enterprises that cannot send sensitive data to public AI platforms. We set up offline AI models, document search, and secure AI assistants inside your own infrastructure.
Enterprise data is ready for AI. But compliance blocks public AI.
Sensitive customer data
On-premise legacy systems
Strict compliance approvals
No public cloud AI allowed
Internal knowledge locked in documents
Employees still depend on manual search
We bring AI to your infrastructure - not your data to the internet.
Offline LLM deployment
Internal document Q&A
Secure RAG-based knowledge assistant
Local vector database
Role-based access
Optional fine-tuning/domain adaptation
Admin handover and support
Monitoring and usage logging
Complete private AI environment, configured for your enterprise.
We handle the practical engineering work: server planning, model deployment, document search, internal UI, role access, and handover. The exact architecture is finalized only after checking your infrastructure and compliance expectations.
- Server assessment and hardware sizing
- GPU/server recommendation if new hardware is needed
- Linux/server setup for AI workloads
- Offline model deployment
- Local inference server setup
- Local vector database setup
- Document ingestion pipeline
- Secure internal chatbot or web interface
- Role-based user access
- Department-wise knowledge separation where required
- Audit/logging guidance
- Backup and maintenance guidance
- Admin training and documentation
Open-weight models under 70B parameters.
Qwen 32B
Strong reasoning, coding, and multilingual enterprise assistant workflows.
Qwen 14B / 8B
Lighter deployment option where hardware capacity or latency matters.
DeepSeek-R1 Distill Qwen 32B
Reasoning-heavy workflows that need structured analysis and careful validation.
Mistral Small 24B
Enterprise chat and document Q&A use cases with practical deployment needs.
Gemma 27B / 12B / 4B
Lightweight private assistant options for smaller workloads.
Microsoft Phi-4 14B
Efficient smaller reasoning model for focused internal assistant use cases.
Llama 3.x 8B variants
Lightweight local assistant and basic automation scenarios.
We recommend models only after checking license suitability, customer use case, hardware capacity, and security requirements.
Private AI for real internal workflows.
Insurance claim document assistant
Banking policy and SOP assistant
Compliance and audit document search
HR and employee helpdesk assistant
Legal and contract document summarization
Internal IT support assistant
Customer support knowledge bot
Operations and process assistant
Report summarization
Code and technical documentation assistant
Your data stays inside your environment.
- Customer documents remain inside customer-approved infrastructure.
- No data is sent to public AI APIs unless explicitly approved in writing.
- The AI model, embeddings, vector database, document index, logs, and user interface can all run on-premise.
- Internet access can be disabled after setup if required by customer policy.
- Access can be restricted by department, role, user, or document category.
What SoftwareWale handles.
- Requirement discovery
- Use-case mapping
- Server sizing
- AI environment setup
- Offline LLM installation
- Document search/RAG setup
- Internal chatbot/web interface
- Security-conscious configuration
- Testing with sample enterprise documents
- Admin training
- Handover documentation
- Ongoing support as per agreement
What we promise to do.
- We do not send customer data to public AI APIs without written approval.
- We do not guarantee 100% AI accuracy.
- We do not replace legal, compliance, medical, financial, or human decision-makers.
- We do not train models from scratch unless separately contracted.
- We do not bypass customer IT, security, or audit processes.
- We do not use restricted third-party data without permission.
- We do not deploy models without checking license suitability.
- We do not promise magic automation without clean data and proper validation.
- We do not take ownership of customer data.
From assessment to private AI pilot.
Assessment
We understand use cases, data type, compliance expectations, and existing infrastructure.
Architecture & Server Plan
We recommend whether to use existing servers or buy a new GPU/server setup.
Deployment & Pilot
We deploy the AI model, local knowledge base, document ingestion flow, and internal assistant.
Support & Expansion
We train admins, hand over documentation, monitor usage, and expand to more departments if needed.
Best suited for organizations where data cannot leave the building.
Insurance companies
Banks and NBFCs
Healthcare and diagnostics
Manufacturing enterprises
Government-linked organizations
Legal and compliance teams
Large enterprises with legacy document systems
Engineer-led AI deployment, not just AI consulting.
We approach private AI like a real software and infrastructure project: assess first, deploy carefully, document properly, and explain limitations before you commit.
Bangalore-based software engineers
Practical deployment experience
Enterprise background
We understand on-premise environments
We build working software, not just presentations
We can set up, test, document, and support the system
We explain limitations clearly before deployment
Questions enterprise teams usually ask.
Want AI inside your own enterprise environment?
If your organization cannot use public AI because of compliance, security, or on-premise data restrictions, we can help you plan and deploy a private AI assistant inside your own infrastructure.