Sthala

Your AI's place.

Most AI deployments let LLMs touch everything — computation, decisions, execution. That's the failure mode. Sthala is built on one hard constraint: LLMs narrate, code executes. Always. Enforced at build time.

An open-source reference framework for governed AI runtime placement on commodity x86 hardware. Airgapped by default. Built for SMBs, CA firms, clinics, schools — any organisation where data movement must stay under explicit boundary control.

"Sthala" (स्थल / ஸ்தலம்) — place, site, ground. Where your AI actually runs.

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The Problem

Modern AI systems let interpretation, decision-making, and execution share one trust boundary. That lets probabilistic systems directly own deterministic consequences — the root cause of prompt injection, unsafe automation, context contamination, and authority confusion.

This project is part of the context-stack, which separates interpretation from authority: intelligence proposes, governance validates, execution authorizes. Intelligence can suggest anything. Authority stays deterministic.

Full doctrine: https://github.com/kannanokannan/context-stack

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Why Sthala

• Cloud AI is expensive — 3-year TCO cloud vs Sthala appliance: ~6× savings
• Compliance is tightening — EU AI Act (Aug 2026), India DPDP Act (Stage 2: Nov 2026), MCA backup mandates
• LLMs are commodities — the moat is the pipeline, not the model
• Refurb hardware works — a ₹1.5L node serves 50–200 concurrent SMB users on 7B–34B models

Sthala is not a product. It is a documented pattern. Read it, fork it, deploy it, adapt it.

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Family

Project	Role
ContextOps	How to govern AI context
ContextBoundary	Where AI context can flow
Sthala	Where AI actually runs

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What You Get

Boot a commodity PC → drop your data → get AI-powered analysis
Nothing crosses the approved boundary unless you explicitly approve it

• Immutable Linux base (no drift, atomic updates)
• Sarvam-30B default (Apache 2.0, Indic-optimised, 24GB VRAM Q6_K)
• Multi-model verifier pipeline (LLM extracts → code computes → LLM narrates)
• Egress consent gateway (you see what leaves, you approve every send)
• Vertical recipes: CA firm, distributor, school, clinic

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Quick Start

curl -fsSL https://raw.githubusercontent.com/kannanokannan/sthala/main/install.sh | bash

Or with a profile:

curl -fsSL .../install.sh | bash -s -- --profile ca-firm

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Repository Structure

sthala/
├── SPEC.md                    ← Framework specification
├── AGENTS.md                  ← Machine-readable build contract for AI coding agents
├── CLAUDE.md                  ← Context file for Claude / AI assistants
├── CONTRIBUTING.md            ← Contribution guide
├── install.sh                 ← Idempotent bootstrap script
├── profiles/                  ← Vertical presets
│   ├── generic.yaml
│   ├── accounting-firm.yaml
│   ├── clinic.yaml
│   ├── distributor.yaml
│   └── school.yaml
├── hardware/
│   └── detect.sh              ← Auto-detect CPU/GPU/RAM, recommend tier
├── stack/
│   ├── docker-compose.yml     ← Full service stack
│   ├── inference/             ← Inference services
│   ├── ingestion/             ← Ingestion services
│   ├── observability/         ← Metrics and tracing
│   └── rag/                   ← Retrieval services
├── recipes/
│   ├── _template/             ← Six-stage recipe template
│   ├── in/                    ← India recipes
│   ├── eu/                    ← EU recipes
│   ├── us/                    ← US recipes
│   └── global/                ← Global recipes
├── research/
│   ├── 2026-05-market.md      ← India SMB AI market research
│   └── 2026-05-standards.md   ← Standards & academic landscape
└── docs/
    ├── architecture.md        ← Architecture deep-dive
    ├── compliance.md          ← Compliance notes
    ├── egress.md              ← Egress guidance
    ├── hardware.md            ← Hardware guidance
    └── models.md              ← Curated model catalog with HuggingFace links

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Design Principles

1. LLM-as-component, not LLM-as-magic — LLM extracts, code computes, LLM narrates
2. Deterministic compute, fuzzy understanding — numbers never come from an LLM
3. Egress-by-consent — nothing leaves without explicit human approval
4. Commodity hardware first — if it needs a $30K GPU, it's out of scope
5. Vertical recipes over generic stacks — a CA firm is not a chatbot

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Standards Alignment

• NIST AI RMF 1.0 + GenAI Profile
• ISO/IEC 42001 (AI management systems)
• EU AI Act (non-high-risk tier by default)
• India DPDP Act 2023 compliance posture
• 16-Factor App for AI (Google, 2024)
• OpenTelemetry GenAI semantic conventions
• CycloneDX 1.7 AI BOM auto-generated

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License

Apache 2.0 — use it, fork it, ship it, build on it.

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Author

Kannan Okannan — Chennai, India

Part of the ContextOps / ContextBoundary / Sthala open-source family.

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Part of the Stack

This project is one of three sibling open-source projects under github.com/kannanokannan.

Project	Question	Repo
ContextOps	How does an org govern its AI context?	github.com/kannanokannan/ContextOps
ContextBoundary	Where is data allowed to go?	github.com/kannanokannan/ContextBoundary
Sthala	Where does the AI actually run?	github.com/kannanokannan/Sthala

Canonical terminology and cross-project decisions: context-stack