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Sovereign AI Infrastructure

Architecting your
private
intelligence.

We engineer the infrastructure that moves your operations off the public grid. Deploy air-gapped, high-performance AI ecosystems inside your own perimeter.

Air-Gapped / Sovereign
DPDP Act Verified
Model Agnostic
Secure your intelligence The 6-phase roadmap
BITHOST SOVEREIGN AI — PRIVATE DEPLOYMENT client: enterprise-corp · env: air-gapped · compliance: DPDP Act verified ● OPERATIONAL SOVEREIGN PERIMETER — NO PUBLIC CLOUD EGRESS PUBLIC CLOUD APIs OpenAI / Gemini / Claude ⊘ DATA LEAVES PERIMETER PRIVATE LLM ENGINE Llama 3.2 · 70B params · fine-tuned vLLM inference · GPU: 4× A100 80GB LIVE BLOCKED VECTOR DB Qdrant · local deploy 12.4M embeddings indexed RAG PIPELINE LangChain · private tunnel Latency: 240ms avg AGENT ORCHESTRATION 4 agents active · supply optimizer running ● Agentic Supply Optimizer · scheduled: 06:00 INTERNAL CONNECTORS — SECURE READS ONLY ERP / SAP connector: active SQL Databases 3 schemas indexed Email Server on-premise exchange Knowledge Base 84,000 documents AUDIT LOG — LAST 4 EVENTS 06:04 Agent completed supply report · 0 external calls 05:58 RAG query · 127 chunks retrieved · local only 05:30 Model health check · all nodes OK 00:00 Security patch applied · LLM container restarted COMPLIANCE STATUS ✓ DPDP Act (India) · no cross-border data transfer ✓ Air-gapped deployment · zero internet egress ✓ Encryption at rest · AES-256 ◎ ISO 27001 audit · in progress STACK: Llama 3.2 · vLLM · LangChain · Qdrant · Kubernetes · PyTorch · HuggingFace · Docker · Prometheus bithost-sovereign ~
Data egress to cloud
Zero
100% on-premise intelligence
Deployment status
DPDP Verified Air-Gapped
Private LLM Deployment Air-Gapped AI RAG Pipeline DPDP Act Compliance Agent Orchestration Model Fine-tuning Sovereign Intelligence Vector Database ERP Connectors Data Sovereignty On-Premise AI Private Cloud LLM Private LLM Deployment Air-Gapped AI RAG Pipeline DPDP Act Compliance
Why build your own

Using public AI is like
working in a glass office.

We build the vault. Every query, every document, every inference stays inside your perimeter. Your intelligence becomes a permanent asset, not a monthly subscription.

01

Custom Architecture

We engineer the structural blueprints for your compute, models and pipelines for your specific enterprise use cases. Not a generic install. A purpose-built private intelligence system.

02

True Sovereignty

When you rent AI, your intelligence is a bill. When you build with Bithost, the AI is a permanent asset on your balance sheet. You own the model, the weights and the entire stack.

03

Connected Intelligence

A model in isolation is useless. We weave your private AI into your internal ERP, email servers and databases so it performs actual autonomous work on real business data.

The cost of dependency

What you are paying for
every single month.

Public AI APIs charge per token, transfer your data across jurisdictions and can shut down or change pricing without notice. Sovereignty eliminates all three risks permanently.

$0

Per-token cost after sovereign deployment

Marginal inference cost drops to near zero. At scale, 12 months of API bills typically pays for the entire sovereign stack.

100%

Of your queries stay inside your perimeter

No data crosses a border, reaches a vendor's training pipeline or appears in a breach notification. Full data residency guaranteed.

4 wk

Proof of concept delivery timeline

From engagement start to a working private LLM connected to your internal data. Full enterprise rollout typically takes 12 to 16 weeks.

∞

Model lifespan with agnostic architecture

Containerised stacks let you swap Llama 4 for the next generation in days. Your intelligence stack never becomes obsolete.

The roadmap

Your path to
independence.

Six phases from cloud dependency to full sovereignty. We walk this path with your team. At the end you hold all the keys and your IT team runs the system independently.

01
Leakage Audit

We map how your intellectual property currently escapes through public cloud APIs. Every service sending data to OpenAI, Gemini or Claude is identified, quantified and risk-rated.

02
Compute Provisioning

We scope and source the GPU infrastructure required to run your specific models. From single-node workstations for smaller workflows to multi-node A100 clusters for enterprise-scale inference.

03
Model Curating

Fine-tuning open-weights models on your internal data, technical jargon and business context. The result is a model that understands your organisation the way a new hire never could.

04
RAG Pipeline

Connecting your private model to your internal knowledge base via secure vector tunnels. Your AI can query 84,000 documents, your ERP and your SQL databases in real time.

05
Agent Orchestration

Deploying autonomous agents that perform scheduled tasks, generate reports, query connectors and write audit logs. Intelligence that works while your team sleeps.

06
The Handover

Transferring all keys, credentials and architecture documentation to your team. Full training for your IT staff. You own the system completely. We remain available as a Sovereign Care partner.

Phase 01 — Leakage Audit Live example
Leakage audit — IP flowing to public APIs today
Customer contracts
82% exposed
Internal pricing data
68% exposed
Engineering docs
45% exposed
HR records
91% exposed
API spend (monthly)
₹4.2L/mo

HR records at 91% exposure means employee data is training a foreign model. This is a DPDP Act violation risk that most enterprises discover only during this audit phase.

Compute stack — recommended for this client
4× NVIDIA A100 80GB (sourced via private cloud)Handles 70B parameter model at 4-bit quantisation with 180ms average inference latency.
Kubernetes cluster on private bare-metal nodesFull isolation from public cloud. No managed Kubernetes service. All control plane on-premise.
vLLM inference server with continuous batching4× throughput improvement over naïve serving. Handles concurrent agent and human queries efficiently.
Estimated annual infrastructure cost: ₹38LCurrent API spend at this query volume is ₹50L/year. Full break-even at month 18.

Many workflows run on smaller configurations. We right-size to your actual query volume. Not every client needs 4× A100s. Some run efficiently on a single-node A100 or on private cloud instances.

Model fine-tuning — training outcomes
Domain accuracy
91%
Base model (GPT-4o)
73%
Jargon recognition
97%
Hallucination rate
4%
Fine-tune duration
8 days

Domain-specific fine-tuning outperforms GPT-4o on this client's supply chain queries. A smaller model that knows your business beats a larger generic model on every metric that matters in production.

RAG pipeline — knowledge sources connected
84,000 internal documents indexed in QdrantTechnical manuals, SOPs, contracts, meeting notes. All embedded and searchable in 240ms.
Live ERP connector via read-only SQL tunnelSupply chain data, inventory levels and order status available to the model in real time.
On-premise email server indexed (last 24 months)Securely parsed, embedded and searchable. No data leaves the server at any point.
Incremental re-indexing every 4 hoursNew documents, updated records and new emails picked up automatically without human intervention.

Everything the model knows about your business is sourced from your own data. No knowledge from public internet. No hallucinated procedures. Answers cite the exact internal document they came from.

Agent orchestration — active deployments
Agentic Supply Optimizer — runs daily at 06:00Queries ERP, analyses stock levels and demand forecast, generates procurement recommendation report.
Compliance Monitor — runs weeklyReviews new contracts and vendor agreements against DPDP Act and internal policy. Flags deviations.
Executive Briefing Agent — runs Monday 07:30Compiles weekly performance summary from ERP, email and project data. Delivered to leadership inbox at 08:00.
Customer Query Agent — in stagingHandles tier-1 internal support queries using the knowledge base. Go-live planned for next sprint.

All agent actions are logged and auditable. Every query, every document accessed and every output is recorded locally. Full traceability with no external logging dependency.

Handover — what your team receives
All credentials and encryption keys transferredModel weights, vector DB access keys, Kubernetes admin credentials and all API tokens delivered to your team.
Full architecture documentationSystem design, network diagrams, connector documentation and runbooks for every component.
IT team training: 3-day programmeYour engineers leave able to restart services, update the model, add new connectors and rotate credentials independently.
Sovereign Care package available (optional)Monthly model health checks, security patches and quarterly knowledge base updates. You choose to engage us or run fully independently.

At handover your organisation is fully self-sufficient. You do not need Bithost to keep the system running. The Sovereign Care package exists for teams that want ongoing expertise without managing it themselves.

4 wk
proof of concept
delivery timeline
0
queries reaching
a public API
12+ mo
until sovereign stack
breaks even vs API
100%
ownership transferred
to your team
Intelligence FAQ

The questions that
matter before you decide.

Sovereign AI is the practice of running LLMs and agentic workflows on infrastructure you control. Instead of sending your data to OpenAI or Google, the intelligence sits on your servers. Your data never leaves your network, your queries are never logged by a third party and your model is a capital asset rather than an operating expense that can be repriced or discontinued without notice.
Enterprise plans from public AI vendors are still black boxes. You are renting intelligence from a company whose business model, pricing and continuity are outside your control. Sovereignty means you own the asset, the model weights and the entire infrastructure. You eliminate vendor lock-in, cross-border data transfer risk and the per-token cost that compounds as your usage grows. At scale, the economics of ownership are significantly better than rental.
Yes. The DPDP Act creates obligations around personal data processing and cross-border transfer. By keeping all data processing on-premise or in a private cloud within India, you eliminate the most complex compliance risks entirely. We document the data flows and produce the technical evidence your compliance team needs to demonstrate that no personal data is processed outside your perimeter.
Not necessarily. Many workflows run efficiently on single-node configurations or on private cloud instances where you own the compute but do not manage the physical hardware. We scope the infrastructure to your actual query volume and model requirements. A 7B parameter model fine-tuned on your domain often outperforms a 70B general model and runs on significantly less hardware. We optimise for your scale, not the maximum capability.
A proof of concept with a working private LLM connected to one internal data source takes four weeks. A full enterprise rollout including fine-tuning, RAG pipeline, agent deployment, ERP connectors and IT team handover typically spans 12 to 16 weeks. The timeline depends primarily on the complexity of your internal systems and the number of data sources you want to connect.
Yes. We build secure read-only connectors that allow the model to query your existing SQL databases, SAP, Odoo and other on-premise systems. The connector uses a dedicated read-only service account and all queries are logged. The AI can retrieve live data without being able to modify it, and without any of that data leaving your network.
Our architecture is model-agnostic. We use containerised stacks designed to allow model swaps without rebuilding the surrounding infrastructure. When Llama 4 or a future open-weights model outperforms your current deployment, the swap typically takes a few days of testing and deployment rather than weeks. Your RAG pipeline, connectors and agent logic carry forward unchanged.
Yes. For defence, government or manufacturing sectors requiring zero internet connectivity, we deploy systems that function entirely within your LAN. There is no call-home, no telemetry and no dependency on an external service for inference, authentication or model updates. The system operates identically with or without an internet connection.
Production stack

Every component is open-source and fully under your control after handover.

NVIDIA H100/A100 Kubernetes Llama 3.2 vLLM LangChain HuggingFace Docker Secure PyTorch Qdrant Vector DB Prometheus Grafana Private Cloud

Ready to secure
your intelligence?

Partner with Bithost for a consulting engagement that prioritises your sovereignty, security and long-term autonomy.

Schedule a consultation
Private LLM · Air-gapped deployment · RAG pipeline · Agent orchestration · DPDP Act compliance