Signed builds
Ed25519 release signatures
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.
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Engineering principles
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.
- 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 - 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 - 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.
- 01RustPerformance-critical paths
- 02CLow-level control
- 03TypeScriptMCP servers · agent interfaces
- 04PythonTraining pipelines only
- 05Claude Opus 4.8Tool use · 1M-token reasoning
- 06Open-weight modelsDeepSeek V4 · Kimi K2 · GLM 5.1 · Qwen 3.7 · Gemma 4
- 07Postgres · Redis · pgvectorStateful agents · vector & KV
- 08Ed25519 · MerkleSigned 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