NovaQuantiX
Available for new engagements

Production-grade
AI infrastructure
delivered.

Custom MCP servers, fine-tuned open-weight models, and autonomous agent swarms. Engineered in TypeScript, Rust, and C — Python reserved for training pipelines.

View live demo

Engineering principles

Signed builds

Ed25519 release signatures

Replayable evaluations

Inspect-AI suites, pinned datasets

Vendor-neutral

Open-weight models & open protocols

Full ownership

Source, keys, runbooks transferred

What we build

Four practices.
One engineering bar.

Every engagement delivers a production artifact your team can run, audit, and extend independently.

01

Custom MCP servers

Connectors engineered for your stack — Claude Opus 4.8, Cursor, Windsurf, Cline, and headless agents. Schema design, authentication, observability, and production hardening.

  • TypeScript & Python SDKs
  • Streaming I/O
  • Signed releases
02

Open-weight fine-tuning

QLoRA, GRPO reasoning training, and distillation on the latest open-weight models. Reproducible runs on Unsloth Studio and torchtune — 70% less VRAM, eval-driven gates.

  • DeepSeek V4 · Kimi K2 · GLM 5.1
  • Qwen 3.7 · Gemma 4 · Llama 4
03

Agent architecture & audit

Multi-agent topologies, RL-routed orchestration, retrieval design, cost-aware token budgets and security review before deployment.

  • Threat model
  • Token & cost budget
04

Autonomous agent swarms

Production swarms with deterministic guardrails, replay logs, sub-agent fan-out, and human-in-the-loop checkpoints. From single tool to 300-agent orchestration.

  • Replayable runs
  • HITL approval gates
Live workflow

From scaffold to signed production in seconds.

Every NovaQuantiX deliverable ships with Ed25519-signed builds, Merkle-chained logs, and replayable Inspect-AI evaluation suites. These are the actual commands.

nova@quantix:~/projects/my-tool
$
Built for distribution

Run anywhere,
deploy everywhere.

MCP servers run in a low-latency mesh across cloud providers, dedicated infrastructure, and on-premise hardware. Health-checked, signed, and replicated by default.

  • → Cross-region low-latency mesh
  • → Signed deployments (Ed25519)
  • → Merkle-chained audit logs
Engagement model

From scoping to running system in six weeks.

Fixed-price phases, verified CI/CD gates, immutable build logs. Clear visibility on what is delivered and what comes next.

  1. 01

    Discovery & architecture

    We map your data flows, agents, and risks. You receive a written Architecture Decision Record before any code is written.

    Week 1 · ADR + budget
  2. 02

    Reproducible engineering

    Each commit triggers an immutable build log, signed artifact, and Inspect-AI evaluation suite. Rust or C for performance paths, TypeScript for MCP, Python for training.

    Weeks 2-6 · CI-gated phases
  3. 03

    Delivery & ownership transfer

    We deploy, document, train your team, and transfer full ownership. Source code, keys, repositories, and evaluation baselines are yours.

    Week 6+ · Source & runbooks
Technology stack

The components
we ship with.

Each component is selected for performance, memory safety, and long-term maintainability. Python is restricted to training scripts where the ecosystem requires it.

Verify each release signature and Merkle root before deployment.

  • 01Rust
    Performance-critical paths
  • 02C
    Low-level control
  • 03TypeScript
    MCP servers · agent interfaces
  • 04Python
    Training pipelines only
  • 05Claude Opus 4.8
    Tool use · 1M-token reasoning
  • 06Open-weight models
    DeepSeek V4 · Kimi K2 · GLM 5.1 · Qwen 3.7 · Gemma 4
  • 07Postgres · Redis · pgvector
    Stateful agents · vector & KV
  • 08Ed25519 · Merkle
    Signed releases · audit logs
Get in touch

Ready to deploy
production AI?

Tell us about your project. We respond with a one-page proposal — scope, budget, risks — within 48 hours.

Response within one business day