Custom AI, deployed inside your boundary.
Private LLM deployment. Company-specific knowledge bases. Document and analytics intelligence — running on your servers, your VPC, your data. Full sovereignty, full customisation.
SaaS AI doesn't fit enterprise reality.
The constraints
- Sensitive data leaves your network for "the cloud"
- Generic models trained on internet, not your domain
- No control over model updates that break workflows
- Vendor lock-in priced by token, scaling unpredictably
The control
- Models run on your hardware. Data never leaves.
- Fine-tuned on your documents, terminology, processes
- You control model versions, update cadence, rollbacks
- Predictable cost — compute, not tokens
Six enterprise AI capabilities.
Private LLM Deployment
Llama, Mistral, or fine-tuned models running on your GPUs — on-prem or in your VPC.
Knowledge Base AI
Company GPT trained on your wikis, docs, SOPs. Cites sources. Updates as you do.
Document Intelligence
Contract analysis, invoice parsing, report extraction at enterprise volume.
Analytics AI
Natural-language queries over your data warehouse. Charts, insights, anomalies.
Custom Integrations
Build the integrations your team needs. Salesforce, SAP, Workday, custom legacy systems.
Enterprise Security
RBAC, SSO, audit logs, encryption, SOC 2 / ISO 27001 patterns built in.
A reference architecture for private enterprise AI.
Assess
Map your data, compliance needs, GPU resources, integration surface.
Design
Reference architecture: model choice, infra, security, integration plan.
Deploy
Install + fine-tune on your hardware. Bake in audit, SSO, RBAC.
Iterate
Quarterly model refresh. New use cases as needs evolve.
Six enterprise AI applications.
Hover (or tap) any card.
Internal Copilot
Hover →Company GPT, every employee
- 01Trained on your wikis, docs, SOPs
- 02Available in Slack, web, mobile
- 03Cites sources, respects ACLs
Knowledge Search
Hover →Semantic search across systems
- 01Indexes Confluence, SharePoint, Drive, Notion
- 02Natural language queries, with citations
- 03Respects per-user access controls
Contract Analysis
Hover →Faster legal review
- 01Extracts key terms, obligations, dates
- 02Flags clauses that deviate from playbook
- 03Routes to right reviewer
Report Generation
Hover →Natural language to BI
- 01Ask in plain English
- 02AI queries warehouse, generates charts
- 03Exports to slide/doc on demand
Compliance Assistant
Hover →Stay ahead of regulations
- 01Monitors regulatory changes
- 02Maps to your policies + controls
- 03Drafts updates, flags gaps
Custom Integration
Hover →Connect anything to anything
- 01Salesforce, SAP, Workday, legacy
- 02Two-way sync with conflict resolution
- 03AI handles edge cases that scripts can't
Enterprise AI works wherever there's complexity.
Five common questions.
Depends on scale. A single A100/H100 GPU handles most internal AI workloads (200–500 concurrent users). For larger deployments, 4–8 GPU clusters. We size + procure as part of the engagement.
Primarily Llama 3.x, Mistral, Qwen, Mixtral, Phi, and any custom fine-tune. We benchmark for your use case before deployment — typically 2–3 candidates evaluated.
Yes. Most clients refresh quarterly. We provide a model-update playbook: A/B testing, rollback strategy, eval-set verification — so updates don't break workflows.
Typical: 8–12 weeks from kickoff to production. Discovery (2w) → architecture (2w) → infra + fine-tune (4w) → integration + UAT (2–4w).
One-time setup (₹15–60L depending on scale) + monthly retainer for monitoring, updates, and continuous improvement. Compute cost is yours (predictable). No per-token pricing.
Your data. Your hardware. Your AI.
A 60-minute architecture call. We sketch a reference design specific to your environment.