Defence & Government AI

AI engineered for classified environments.

Zero data egress. On-premise. Auditable.

75d

Fastest classified delivery

Defence deployments

Data egress incidents

99.9%

On-premise uptime

A slow aerial drift over a city at night, roads and rail lines picked out in light — Film: an aerial pass over a city at night. Illustrative of the operating environment — not footage from any programme.

Overview

India's defence and government institutions need AI systems that cannot phone home. We build full-stack AI — computer vision, acoustic intelligence, document processing, and multilingual transcription — designed from the ground up for air-gapped, LAN-only, zero-egress environments. Everything runs on client-controlled hardware. Nothing leaves the perimeter.

The Problem

Most AI vendors cannot work without cloud connectivity. Their models depend on SaaS APIs, their pipelines require internet access for inference, and their data governance is designed for enterprise SaaS — not classified networks. Government clients cannot use these systems. They are left either building in-house (expensive, slow) or going without.

Our approach

We deliver everything on-premise. Models are fine-tuned on client-controlled GPU clusters, quantised to run on air-gapped hardware (GGUF/ONNX), and deployed on isolated LAN networks. We architect for RBAC, audit logging, and full data sovereignty from day one — not as an afterthought. Architecture Decision Records document every data flow and security boundary before the first line of code is written.

Deliverables

Air-gapped deployment architecture
On-premise model fine-tuning
Zero-egress inference pipelines
RBAC and audit logging
Security boundary documentation
Post-deployment SLA support

Tech stack

YOLOv8xWhisper ASRLlama 3 70BGGUF QuantisationLangChainRAG PipelinesFastAPIPostgreSQLKubernetesAir-gapped Deploy

In practice

Zero egress is a test, not a setting

A claim of no outbound traffic is worth exactly what its test is worth. We verify with the network interface physically down, not merely firewalled, and the system is expected to run through it unchanged — inference, alerting, recording, audit. No telemetry, no licence check, no model call that quietly needs a route out. Where a component cannot survive that test, it does not ship. The same rule governs the AI layer: if the record cannot leave the building, neither can the prompt containing it.

Sized against sustained load, before procurement is signed

Hardware is specified against the real workload — sustained inference, not a benchmark burst — and throughput is proved before a purchase order is raised. ICCS is the worked example: eight fused detection models in approximately 28 GB of VRAM, 80 configured cameras on a single GPU node, with roughly 15–20 concurrently active 1440p streams. That ceiling turns out to be CPU-bound rather than GPU-bound, which is the kind of finding that changes a bill of materials. You get those numbers while they are still useful.

Accreditation, and the procedure that comes with it

The controls an assessor asks for are present before they ask: three enforced roles, JWT authentication, a tamper-resistant audit log with CSV export, dormant TOTP MFA and encrypted camera credentials. Detection, sensors, drones, mapping, recording and access control sit in one system with one audit log — in a review, five vendors is five arguments. Alongside the software you get the written operating procedure for a site with no path in or out: update, backup and incident, on signed controlled media.

How the system tells you it is degraded

An air-gapped platform has nobody watching a cloud dashboard, so it has to report on itself. Host CPU, RAM, disk and GPU metrics, a per-camera circuit breaker, an engine liveness heartbeat, and tamper and blind-spot detection — a covered lens is a failure, not a quiet feed. The sensor path is fail-closed. Alerts are graded at three levels and batched to operators in 50 ms, so a degraded state reaches the duty officer on the same path as a threat.

Where this has run

MINERVA

Sovereign large-language-model document intelligence over a classified corpus — quantisation, serving, retrieval, evaluation and the offline update procedure delivered as one scope.

Brief to production in 75 days. Processing cut from three days to eight minutes, at 94% extraction accuracy. Llama 3 70B, GGUF, RAG over pgvector, nothing leaving the premises.

ARGUS

Air-gapped multi-camera perimeter intelligence with real-time threat detection.

97.8% detection accuracy at a 0.18% false-positive rate across 40+ concurrent feeds. YOLOv8x on PyTorch, NVIDIA A100, no network path.

ICCS

Detection, multi-sensor ingest, autonomous drone response and operator command as one air-gapped platform rather than five integrations.

Release v1.25.2, 27 June 2026. Eleven capability domains shipped, eight or more fused detection models, four drones over MAVLink and ArduPilot, fully offline-capable.

Questions

What buyers ask about this specifically.

By capability and measured outcome, both stated in full. Programmes appear by codename — ARGUS, MINERVA, GARRISON, SENTINEL, SCRIBE — with what the system does, what it was measured at, what it was built with and when it went into service. What we withhold is who and where: no client, no unit, no installation, in a deck or a reference call or after the engagement ends. That sits in employee NDA and IP assignment with liquidated damages attached — contract, not courtesy.

That is planned from the first brief. Operators get interfaces built for someone who did not choose the software — three enforced roles, and a PIN-gated tablet view giving a response team the alert, a read-only map and the nearest drone, nothing else. You get the runbook, training records and the documented offline procedure. Source code, schema, infrastructure definitions and documentation are yours, on an open-source base with no per-seat licence at the platform layer.

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CI/CD, GPU clusters, air-gapped Kubernetes.

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