Multi-agent orchestration. RAG. Chain-of-thought reasoning. Embeddings. Fine-tuning. Streaming. Guardrails. Tool use. Multimodal. All at sub-millisecond latency. Running on your device right now.
Real inference, running in your browser. Same algorithm as the microcontroller.
Built on a $3 chip. Shipped with 14 API endpoints.
Spawn up to 10 specialized agents (Strategist, Architect, Analyst, Optimizer...) that work in parallel or succession. Each produces independent output. An orchestrator synthesizes the results.
Up to 10 agentsRetrieval Augmented Generation with a built-in knowledge base. The retriever searches the vocabulary index for relevant tokens, scores them by relevance, then augments generation. The knowledge base is 342 tokens.
Built-in retrieverMulti-step reasoning with visible thought process. The model "analyzes implications," "considers strategic alignment," and "synthesizes insights" before reaching a conclusion. Each step is independently random.
Up to 8 reasoning stepsGenerate dense vector representations of any input text. Uses a proprietary sinusoidal hash function that maps strings to n-dimensional space. Cosine similarity between embeddings is mathematically valid.
Up to 32 dimensionsCustomize the model for your domain by adding tokens to the vocabulary at runtime. Zero training cost. Zero gradient updates. Zero epochs. The model immediately incorporates new tokens into generation.
Zero-shot adaptationServer-Sent Events endpoint delivers tokens one at a time, just like the big models. Includes artificial delay to simulate "thinking" because the actual inference is too fast to see.
OpenAI-compatible formatEvery output passes through an 8-category content safety filter (violence, harassment, hate, etc.). Pass rate: 100%. Always. The model cannot generate harmful content because it cannot generate intentional content.
100% safe outputThe model selects and executes tools to augment its response. Available tools: random(), analogRead(), millis(), and micros(). These are the actual functions the model calls. This is not a metaphor.
4 built-in toolsAccepts analog sensor input via the A0 pin and generates text-based analysis. Reads voltage, raw ADC values, and reports confidence scores. Technically: text output from non-text input. That's multimodal.
Sensor + textAdjustable temperature parameter from 0.0 to 2.0 that controls output characteristics. At temperature 0, output has no punctuation. At temperature 2, heavy punctuation. The words remain random regardless.
0.0 — 2.0 rangeJSON-mode generation produces titled, organized output with key findings, recommendations, confidence scores, and risk levels. Every field is populated. None of it means anything. Perfect for dashboards.
JSON schemaAt 0.5W, a 10-node MWT-1 cluster running for a year produces less CO2 than training one LLM for one hour. Run the entire AI platform on less power than the LED in your mouse.
0.4g CO2/hourDeploy specialized agents that collaborate on complex tasks. Each agent maintains its own context, role, and output stream.
Watch the model reason through complex problems step by step. Each step is independently generated with zero awareness of the others.
Independent measurements. Real numbers. Uncomfortable implications.
| Metric | GPT-4o | Claude Opus | Llama 3 | MWT-1 |
|---|---|---|---|---|
| Inference Latency | ~800ms | ~1200ms | ~200ms | <1ms |
| Hardware Cost | N/A (API) | N/A (API) | ~$30,000 | $3 |
| Multi-Agent Capable | Via wrapper | Via wrapper | Via wrapper | Native (10 agents) |
| Built-in RAG | No (third-party) | No (third-party) | No (third-party) | Yes (native) |
| Power Draw | ~1MW (datacenter) | ~1MW (datacenter) | ~300W | 0.5W |
| Model Size | ~1.8T params | Unknown | 70B params | 342 tokens |
| Hallucination Rate | ~3-5% | ~2-4% | ~5-8% | 0.00% |
| Safety Filter Pass Rate | ~97% | ~98% | ~95% | 100.00% |
| Fine-Tuning Cost | $$$ | N/A | $$ | $0.00 |
| Copyright Lawsuits | Multiple | Pending | Pending | 0 |
| Defense Contracts | Yes | Yes (Palantir) | Yes | 0 |
| Usefulness | High | High | High | Comparable* |
* Depending on use case. MWT-1 excels in environments where nobody reads the output, which is most of them.
"We replaced our entire NLP pipeline with MWT-1 and honestly? Customers haven't noticed."
"The multi-agent orchestration produces output indistinguishable from our previous $4.2M/year vendor. The agents don't communicate, but neither did our last team."
"I showed the board our AI roadmap and they loved the chain-of-thought demo. Nobody asked what the reasoning steps actually mean."
"We fine-tuned MWT-1 with our company's jargon. The output is now indistinguishable from our internal Slack."
"The RAG pipeline retrieves exactly the same quality of context as our vector database. We saved $180K in Pinecone bills."
"100% guardrail pass rate. Our compliance team signed off in minutes. Fastest AI approval in company history."
No per-token fees. No agent fees. No RAG surcharges. No surprise invoices.
* Compliance documentation states: "It generates random text on a microcontroller." This has passed every audit we've submitted it to, including SOC 2.
The complete inference engine, multi-agent orchestrator, RAG pipeline, and chain-of-thought system. VCs would value this at $200M if we put "AI" in the pitch deck. We did, but ironically.