Heavy hardware
Giant GPU clusters, or a remote API you rent by the token.
SKY
Sovereign Knowledge, Yours.
Sovereign AI that runs on efficient hardware, thinks with your company’s own knowledge, and stays fully in your hands.
Frontier AI is a global encyclopedia. Your business needs its own truth. Your company runs on a small, specific body of knowledge.
GPT, Claude, Grok and DeepSeek know a little about everything.
You pay for breadth your business never uses, and the model fills gaps with plausible text.
Global encyclopedia
GPT · Claude · Grok · DeepSeek: mostly irrelevant knowledge.
Data leaves your boundary
Latency, cost, policy and vendor exposure become part of your system.
SKY is Sovereign AI, built around your own knowledge.
Not an encyclopedia. A focused intelligence that fits your hardware, your data and your rules.
Efficient hardware
A right-sized footprint instead of GPU farms or per-token bills.
Relevant knowledge
Your policies, SOPs, contracts and decisions, not the whole internet.
Owned boundary
Knowledge, compute, policy and egress stay under company control.
Remember: Sovereign ≠ “no cloud”. You own the boundary and choose where intelligence may leave it.
Less irrelevant capability means less hardware.
A compact model on a focused corpus, with cache and selective escalation, needs far less compute per answer.
Sovereignty is a knowledge boundary, not just a hosting choice.
SKY keeps four things under your control.
Knowledge
The company corpus stays governed by the company.
Compute
Inference runs inside the enterprise trust boundary.
Policy
The control plane enforces what models may decide.
Egress
External calls are explicit, gated options.
Your boundary
Javelin keeps reasoning high and cost optimal.
Your knowledge
The engine: reasoning on your knowledge. Retrieval, a compact reasoning model, verification and policy working as one.
Retrieve
Relevant sources. Only your approved knowledge enters the answer.
Reason
High reasoning. Strong answers from a compact model on the right evidence.
Verify
Verification and policy work as one with retrieval and the compact model.
Decide
Hard cases escalate under policy. Repair and escalation are bounded paths, never open-ended agent loops.
Optimal TCO
Cheapest path first; expensive reasoning only when needed.
Pluggable Packs add new skills without building a new engine.
One Javelin core. Each Pack brings the knowledge, verifiers and task logic for a domain. Swap or add Packs as needs grow.
Javelin core
Control · retrieval · reasoning · verification · trace.
Packs
Legal & Regulatory first (KELP / ID-LEGAL). Next: Policy & SOP, Contracts, Product knowledge.
Domain Profile
Your company corpus and policy context, plugged into any Pack.
Spend intelligence only when the task requires it.
Start cheap. Escalate only when evidence, risk or quality demands it. North star: intelligence per unit of compute.
Cache / rule
Zero or deterministic.
cost per answer ↑
Local control
Small bounded model.
cost per answer ↑
Retrieval
Evidence path.
cost per answer ↑
Generation
Reasoning when required.
cost per answer ↑
Adjudication
Hard cases only.
cost per answer ↑
LodeStar is Javelin in a box your company fully owns.
An appliance: Javelin installed on pre-configured hardware, sized to your requirement.
Pre-configured
Hardware and software tuned together.
Right-sized
Specs set to your requirement, not a generic rack.
Fully owned
Hardware, data, models and traces belong to you.
Air-gap ready
Runs entirely inside your boundary.
Packs · Legal · Policy · ContractsJavelin · control · reasoning · verifyModels + retrieval + cachePre-configured hardware
From your requirement to an appliance you own.
A repeatable path that turns a company’s needs into a delivered, owned system.
01 Requirement
Users, workload, domain and security needs.
02 Sizing
Hardware spec matched to the requirement.
03 Packs + corpus
Domain Packs and your knowledge loaded.
04 Delivered
Installed in your boundary. Owned by you.
The result: a system your team can audit, upgrade with new Packs, and keep.
Every verified decision makes the next answer cheaper and better.
Verified traces train a local model, so each deployment depends less on outside intelligence.
Rizzo
SKY’s local small language model. It learns from verified traces and gradually takes work over from external models.
Jev
External supervisor model that bootstraps the loop until Rizzo can carry the task locally.
The flywheel
Enterprise tasks → verified traces → Rizzo learns locally → lower cost, more usage.
We start with one domain and expand Pack by Pack.
Bottom-up: target organisations × annual value of one LodeStar deployment plus Packs.
Total
Global sovereign AI spend: US$48.9B in 2026 (Grand View Research).
Serviceable
Indonesia / APAC enterprises needing owned AI.
Beachhead
Legal & regulatory research with KELP / ID-LEGAL.
Why SKY wins against the alternatives.
Efficient hardware
A right-sized appliance instead of GPU clusters or token bills. Vs private model stacks and APIs.
Relevant knowledge
A single-domain corpus with no global noise. Vs frontier AI.
Verified output
Evidence checks and a decision trace on every answer. Vs plain RAG and model-only stacks.
Fully owned
Hardware, data, models and traces stay with the company. Vs hyperscaler and telco clouds.
Pluggable Packs
Add a domain skill without rebuilding the engine. Vs single-model stacks.
Optimal TCO
Cheap path first; escalate only when needed. Vs per-token pricing.
What is architecture today must become evidence tomorrow.
Until measurements arrive, we call this a thesis, not traction.
Prove the pipeline
Gate 0 + P0 runtime coherence · T0 coverage · Replay + trace integrity
Prove the economics
Compute / latency reduction · False-exit rate · Cost per verified outcome
Prove the flywheel
Verified trace growth · Local model improvement · Less external dependency
Prove repeatability
Second and third domain · Pack portability · LodeStar deployment repeatability
Gate 0 / P0: first checkpoint confirming the pipeline runs coherently end to end. T0: cheapest verification tier, deterministic checks that can end a request early. False-exit: an answer wrongly accepted at T0.