TECHNOLOGY / FOR EVALUATORS

Sovereignty enforced by topology, not by promise.

A technical brief for ML and security evaluators. These are the engineering principles behind every system Sumedh builds — shown here in DRISHTI, our first platform. Every claim below is a property of how the system is built.

01

Sovereignty Architecture

DRISHTI's critical path — ingestion, embedding, clustering, post-engine analysis, GIS, and primary inference — executes entirely within the deployment perimeter. There is no outbound dependency required for the system to function. Map tiles are pre-cached; language models run locally; storage is local. The only optional external path is the Groq inference fallback, which is absent by configuration on air-gapped installations. Because no data crosses a national boundary, no foreign data-protection regime has jurisdiction over it.

CRITICAL PATH · WITHIN PERIMETER

Llama 3.1 / Ollama inference and MapLibre + Survey of India GIS attach locally.

DRISHTI sovereign architecture A linear processing pipeline — Ingest, MuRIL embedding, UMAP dimensionality reduction, HDBSCAN clustering, and PEA evidence layers — feeding an analyst console. Local storage, local inference, and geospatial services attach inside an air-gapped deployment perimeter. The only external path, an optional Groq API fallback, is blocked at the perimeter boundary on air-gapped builds. DEPLOYMENT PERIMETER — AIR-GAPPED INGEST Open signal EMBED MuRIL REDUCE UMAP · 50-dim CLUSTER HDBSCAN EVIDENCE PEA 0–4 ANALYST CONSOLE React 18 · ranked narratives STORAGE pgvector · MinIO INFERENCE Llama 3.1 · Ollama GEOSPATIAL MapLibre · SoI tiles ATTACH LOCALLY BLOCKED ON AIR-GAPPED BUILDS OPTIONAL Groq API
Every stage on the critical path executes inside the perimeter. The Groq fallback is the only path that would cross the boundary — and it is absent by configuration on air-gapped installations.
02

Language Stack — MuRIL

DRISHTI embeds text using MuRIL (Multilingual Representations for Indian Languages). English, Hindi in Devanagari, and code-switched Hinglish are represented in a single shared space with no intermediate translation step. The operational consequence: a Hinglish narrative is understood as written, not lossily translated to English first — preserving the code-switching that adversarial messaging often relies on.

03

Clustering Pipeline — UMAP / HDBSCAN

High-dimensional MuRIL embeddings are reduced to 50 dimensions with UMAP, preserving local and global structure, then clustered with HDBSCAN. HDBSCAN is density-based: it discovers an unknown number of narratives and explicitly labels noise rather than forcing every point into a group. No cluster count is pre-specified and no keyword seed is required — which is what makes detection genuinely unsupervised.

04

Post-Engine Analysis — PEA Layers

PEA operates on 72-hour sliding windows. PEA-0 anchors integrity with SHA-256 hashing. PEA-1 characterises each narrative. PEA-2 tracks mutation as framing evolves. PEA-3 flags statistically significant spikes at z ≥ 2.0. PEA-4 produces a ranked, exportable assessment with an unbroken, hash-verified chain of custody — the property that makes outputs defensible rather than merely indicative.

PEA-0
Integrity
SHA-256 anchor
PEA-1
Characterise
Theme · language
PEA-2
Mutation
Framing shift
PEA-3
Spike
z ≥ 2.0

PEA-3 · 72h SLIDING WINDOW · z ≥ 2.0

SHA-256 9f3a7c2e…c1d7 · cluster_hash be20af11…7740 · custody ✓ unbroken

05

Inference & GIS — local reasoning, local maps.

Primary inference runs on Llama 3.1 via Ollama inside the perimeter. The geospatial layer uses MapLibre GL over pre-cached Survey of India tiles, so mapping never queries an external service. Both are designed to satisfy the same constraint: nothing essential leaves the box.

06

Specification & Footprint

Embeddings
MuRIL — native English / Hindi (Devanagari) / Hinglish, no translation layer
Dimensionality
UMAP → 50 dimensions
Clustering
HDBSCAN — density-based, unsupervised, noise-aware
Analysis
PEA 0–4 — 72h sliding windows · spike threshold z ≥ 2.0 · SHA-256 custody
Inference
Llama 3.1 via Ollama (local) · Groq API optional online fallback
Geospatial
MapLibre GL + pre-cached Survey of India tiles — no external map API
Backend
FastAPI · Celery · pgvector · MinIO
Frontend
React 18 analyst console
Packaging
Docker Compose / Kubernetes · single-command bring-up
Minimum spec
64 GB RAM · 16-core CPU · 500 GB SSD