01
Sentrix
AI execution integrity — patent filed (India)
The problem
Teams deploy AI tools with no control over what the AI actually does. It can drift from the task it was given, breach policy, or act outside its mandate — with no record of when or how.
What it does
Locks the user's intent at the start of a task, then checks every AI output against it in real time — flagging deviation, drift, and policy violations with a full audit trail. Ships as a CLI, VSCode and Chrome extensions, and an MCP server.
How it differs
Most guardrail products inspect content — toxicity, PII, unsafe topics. Sentrix checks fidelity to intent: did the AI do what was actually asked. Those are complementary layers, not substitutes.
Honestly: intent-integrity is a young category and larger security vendors are now entering it. My edge is shipping depth — working IDE, browser, and MCP integrations today — not incumbency.
TypeScriptVSCode APIChrome ExtensionMCPNode.js
Verified · Runs
~6.9k lines core TS
0 compile errors
CLI + clients working
02
VNOL
Agent-state portability protocol — patent pending
The problem
When an AI agent crashes, a session ends, or you switch providers, its working state — goals, reasoning, constraints, next steps — is gone. Teams restart complex work from zero and stay locked to one vendor.
What it does
Serializes an agent's full working state into a compact, AES-256-GCM encrypted, integrity-sealed snapshot — and restores it on a different model (OpenAI, Anthropic, Llama, Mistral) so work resumes where it stopped.
How it differs
Framework checkpointing (e.g. LangGraph persistence) saves workflow state inside one framework. VNOL saves the agent's cognitive state as a model-agnostic, sealed artifact designed to boot on a different provider — with a security audit run before anything is sealed.
Honestly: if you live entirely inside one framework and one provider, its built-in persistence may be all you need. VNOL is for when you can't afford that lock-in.
TypeScriptnpmMCPAES-256-GCMMulti-model
Verified · Tested
7.4k lines core TS
44/44 tests passing
0 compile errors
03
Cikitsu
Multi-agent business strategy SaaS — in production
The problem
Small and mid-size businesses can't afford a cross-functional consulting team — finance, marketing, operations — working one problem together.
What it does
Eleven specialised AI consultants collaborate on a submitted business question — challenging each other's reasoning — and deliver a structured strategy report. Deployed with subscription billing and PDF reporting.
How it differs
It isn't a wrapper around one chat model: agents form teams dynamically and debate before concluding — and the whole product runs on the same governance and state layer (VNOL, UTCD) I build as infrastructure. It's the working proof of the stack.
Honestly: AI consulting tools are a busy space. Cikitsu's claim isn't "the only one" — it's "live, billing, and built on infrastructure I can show you."
Next.jsFastAPIPythonPostgreSQLDocker
Live · Paying users
Production deploy
Subscription billing
Built on VNOL + UTCD
04
Azhwar
AI code governance engine — patent pending
The problem
AI coding assistants generate code fast, and much of it quietly violates accessibility, security, or design standards — unnoticed until it breaks in production.
What it does
Audits AI-generated code at the syntax-tree level against 250+ rules across 30+ industry profiles, suggests corrections, and runs wherever code is written — npm CLI, GitHub Action, or MCP server inside the editor.
How it differs
General linters check syntax and style. Azhwar is profiled for the specific failure modes of AI-generated code, mapped to industry compliance profiles rather than generic rules.
Honestly: established scanners (Snyk, Semgrep) are strong on classic vulnerabilities. Azhwar's lane is the AI-slop layer they weren't designed for — it complements them rather than replacing them.
TypeScriptASTnpm CLIGitHub ActionsMCP
Verified · Ships
CLI + Action working
250+ rules
30+ profiles
05
Baliza
Bounded AI delegation for founders — launch ready
The problem
Founders can't hand communication to AI without guardrails — it may approve things they wouldn't, miss their tone, or overstep authority they never granted.
What it does
The founder defines authority rules, escalation triggers, and voice first; the AI drafts only inside those bounds. Every action is logged, explainable, and reversible with a kill switch. Billing and onboarding built.
How it differs
Most email-AI products optimise for more automation. Baliza optimises for safe delegation — the rules engine and audit trail are the product, the drafting is downstream of them.
Honestly: it is launch-ready, not launched. The engineering is done; the market test hasn't started.
Next.jsNode.jsPostgreSQLGmail APIStripe
Launch ready
Phases 1–6 complete
Billing integrated
Pre-launch
06
PhantomInfer
On-device LLM inference engine — research
The problem
Most AI requires a cloud connection and sends data off-device — unusable in low-connectivity environments and risky in privacy-sensitive sectors.
What it does
A C++ inference engine for running language models directly on mid-range Android hardware — custom kernel, mixture-of-experts routing, thermal management, and JNI integration. Groundwork for private, offline AI.
How it differs
Rather than wrapping an existing runtime, it's a ground-up engine targeting the constraint nobody optimises for: 6GB-RAM consumer phones, not flagship hardware.
Honestly: this is research-grade. On-device throughput for large models on mid-range hardware is measured in tokens per second, not magic. I publish what the hardware actually does.
C++Android NDKCMakeJNIGGML
Research
70+ C++ source files
Custom MoE router
Active development