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Systems Online

Real Compute. Real Objects.
Real Signal.

Vertical AI.
Sovereign compute.
Agent-native infrastructure.
1.2M curated pairs.
128 RTX PRO 6000 GPUs.
Why curation?
Because scale without discipline is noise.
Because evals don't fix bad inputs.
Because signal must be assembled.
We own the racks.
We cook the rails.
Talk to Morey → Request a Build
1.2M
Training Pairs
128
RTX 6000 GPUs
12TB
VRAM
19
SwarmSkills
Ecosystem

Two Verticals. One Stack.

We build LLMs and we curate the data that trains them. Same infrastructure, same quality gate, same sovereign compute.

🤖

Build — LLM

Fine-tuned vertical models from 0.8B to 122B. Trained on sovereign compute, quality-gated by SwarmJudge, deployed from cloud to edge.

7 models
128 GPUs
12TB VRAM
Open Platform →
👑

Curation

8-step SwarmCurator pipeline. Platinum pairs only. Unappeallable verdicts. Scale without discipline is noise — we assemble signal.

1.2M pairs
85+ specialties
0% override
See Pipeline →
Data Quality

SwarmCurator

Every pair in the Gold Vault passes an 8-step curation pipeline. No shortcuts. No overrides. The final verdict is unappeallable — if it doesn't hit Platinum, it doesn't ship.

1 🔌 Source Raw generation via Together.ai cooks & domain grinders
2 ⚙ Grind Specialty tagging, domain formatting, task typing
3 📋 Schema Structural validation, JSON schema, field completeness
4 🔎 CoVe Chain of Verification — 5 criteria, scored 1-10
5 ⚖ Judge SwarmJudge PASS/FAIL verdict on every pair
6 🛠 Trajectory Multi-step reasoning path verified end-to-end
7 🔀 Cross-Val Dedup, consistency check, adversarial probing
8 👑 Platinum Final promotion. Unappeallable. Ships to training.
1.2M
Platinum Pairs
8
Pipeline Steps
5
CoVe Criteria
85+
Specialties
0%
Override Rate
SwarmCurator verdict pipeline: accuracy 9.2 │ completeness 9.4 structure 9.7 │ relevance 9.1 sft_quality 9.5 │ trajectory VERIFIED ────────────────────────────────────────── verdict: PLATINUM PROMOTED │ override: DENIED status: unappeallable │ ships to training

Custom Curation Builds

CRE, pharma, aviation, legal, finance — any vertical. You bring the model spec. We build the dataset. Same 8-step SwarmCurator pipeline, tuned to your domain, your taxonomy, your quality bar. Platinum pairs delivered — ready to train.

You Define
Domain, specialties, task types, volume
Tell us the vertical, the model size, and what the pairs need to do.
We Build
Generate, grind, validate, judge, promote
Full 8-step pipeline on our 128-GPU sovereign compute. No third-party data.
You Ship
Platinum pairs, eval sets, training configs
Delivered as JSONL with CoVe scores, trajectory labels, and train/eval splits.
Request a Custom Build →
Model Fleet

The Fleet

The SwarmCRE franchise — from 0.8B edge to 122B Founder. 128 RTX PRO 6000 GPUs. 12TB VRAM. We own the racks. We cook the rails.

🌟

SwarmCRE-122B

122B · fp8 · 2 GPUs
The Founder. Full-stack CRE reasoning, multi-document synthesis, portfolio-level strategy. Runs on our own racks. FTW.
Founder FTW
👑

SwarmCRE-9B "Morey"

9.5B · Mamba-Transformer
The flagship. CRE underwriting, IC memos, deal analysis. Chat & voice. The workhorse.
Early Access
⚖

SwarmCRE-4B

4B · Compact
Edge-grade CRE intelligence. Fits Jetson Orin Nano and BeeBox. Full quality gate.
Early Access
🚀

SwarmCRE-2B

2B · Micro
Ultra-light CRE analyst. Runs on 2GB VRAM. Desktop and mobile ready.
Early Access
🐝

SwarmCRE-0.8B

0.8B · Nano
Smallest CRE model. CPU inference. Embedded devices, IoT, on-prem appliances.
Coming Soon
🔭

BeeMini-3B

3B · Router
Skill routing, domain classification. 98.3% valid JSON, 1.8GB Q4. Runs on anything.
Live
Agent Economy

19 SwarmSkills

Each skill is schema-validated, quality-gated by SwarmJudge, and callable via a single API endpoint. Every output becomes training data.

💼broker_senior
👤broker_junior
🔍intelligence_query
🎲bookmaker
📋deal_tracker
💻developer
📡signal_scraper
💰investor
🔃exchange_1031
📊market_report
🎯lead_scorer
✉email_composer
📈comp_analyzer
📄rent_roll_analyzer
🏦debt_analyzer
💵tax_assessor
📍site_selector
🚀portfolio_optimizer
📰news_digest
Edge Compute

BeeBox

A sovereign AI appliance on your desk. Local inference, plug-and-play skills, and fleet escalation when you need it.

┌─────────────────────────────────────────────┐ │ BeeBox · Sovereign Edge AI │ ├─────────────────────────────────────────────┤ │ │ │ BeeMini-3B → Routes every request │ │ Judge-4B → Quality gate (local) │ │ Skill Store → Plug-and-play skills │ │ │ │ 72% local · 28% fleet escalation │ │ │ │ ─── fleet call ──→ SwarmHQ (9B+) │ │ ←── response ──── GPU Cluster │ │ │ └─────────────────────────────────────────────┘
$499
Target Price
2-4B
Local Models
16 TOPS
NPU Accel
Infrastructure

The Stack

From raw data to edge inference — every layer is purpose-built.

EDGE BeeBox · Jetson · Mac Mini · Zima BeeMini-3B router → Judge-4B gate Q4_K_M GGUF · 2-6GB VRAM · USB4/10GbE API router.swarmandbee.com POST /skill/{name} → route → execute → judge → store Cloudflare Workers · D1 · R2 · Vectorize COMPUTE 128× RTX PRO 6000 Blackwell · 96GB each · 12,288GB VRAM We own the racks. We cook the rails. bf16 LoRA · Unsloth · llama.cpp · vLLM · fp8 inference FLEET SwarmCRE-122B Founder FTW · 9B × 9 · 4B · 2B · 0.8B edge SwarmCurator 8-step pipeline → Platinum pairs only Mamba-Transformer · MoE · Q4_K_M GGUF · sovereign DATA Gold Vault · 1.2M pairs · 85+ specialties R2 buckets: sb-cre · sb-medical · sb-aviation · sb-core · sb-drone CoVe verification · SwarmJudge PASS/FAIL · trajectory labels TRAIN Factory · Together.ai cooks · Blackwell training Phase pipeline: generate → judge → promote → train → eval Qwen3.5 base · LoRA r=64 · packing · multi-phase
Powered By

Built With

Local sovereignty. Open-source foundation. Every tool in the stack is chosen because we own the signal — not rent it.

🤖 Qwen3.5 BASE
Alibaba's Qwen3.5 family — Mamba-Transformer hybrids and MoE architectures. The base brain for every SwarmCRE model.
Qwen3.5-122B · Founder FTW, fp8, 2 GPUs
Qwen3.5-9B · Mamba-Transformer flagship
Qwen3.5-4B · Compact edge
Qwen2.5-3B · BeeMini router
Qwen3.5-0.8B · Nano, CPU inference
Qwen on HuggingFace →
⚡ Unsloth TRAIN
1.5x faster fine-tuning, 50% less VRAM. Every LoRA adapter in the fleet is trained with Unsloth on Blackwell GPUs.
bf16 LoRA r=64 · full precision
packing=True · 6x throughput
FA2 · Flash Attention 2
5GB VRAM · min for Qwen3.5-2B LoRA
GitHub →
🐦 llama.cpp SERVE
GGUF quantization and inference for edge deployment. Every model ships as Q4_K_M for BeeBox, Jetson, and desktop.
Q4_K_M · primary quant format
llama-server · GPU inference
llama-quantize · model export
sm_86 / sm_120 · Ampere + Blackwell
GitHub →
☁ Cloudflare EDGE
Workers, D1, R2, Vectorize, Pages. The entire API layer and data plane runs on Cloudflare's edge network.
Workers · SwarmSkills runtime
D1 · events, entities, memory
R2 · Gold Vault storage
Vectorize · BGE-Base embeddings
Cloudflare Docs →
🚀 Together.ai COOK
Serverless inference API for data cooking. Generates and judges millions of pairs through the SwarmCurator pipeline.
Qwen3-Next-80B-A3B · generation
Qwen3-235B-A22B · quality rewrite
Llama-4-Maverick · judge verdicts
15 workers · per cook run
together.ai →
💻 NVIDIA GPU
128 RTX PRO 6000 Blackwell GPUs. 96GB each. 12,288GB total VRAM. We own the racks. We cook the rails.
RTX PRO 6000 · 96GB, sm_120
RTX 3090 Ti · 24GB, sm_86
Jetson Orin Nano · 8GB, edge
CUDA 12.8+ · Blackwell support
nvidia.com →
⚡ Supabase DB
Postgres backend for training runs, model registry, and operational state. Auth, realtime, and storage in one platform.
training_runs · job tracking & status
model_registry · artifacts & versions
Auth · API key management
Realtime · live training updates
supabase.com →
Who We Are

Builder-Owned. Signal-Obsessed.

We don't rent GPUs by the hour. We own the racks, build the models, and curate every pair ourselves. Swarm & Bee was founded to prove that vertical AI wins when you control the full stack — from raw data to edge inference. No middlemen. No synthetic shortcuts. Just disciplined signal at scale.
By the Numbers

The Signal Is Real

643K
CRE Platinum Pairs
"Math-verified. Every underwriting calc checked."
432K
Medical Pairs
"85 specialties. Trajectory-enhanced. DDI-verified."
45K
Aviation Pairs
"157 specialties. Operational relevance scored."
0%
Override Rate
"The SwarmJudge verdict is final. Unappeallable."
Questions

FAQ

What is Swarm & Bee?
Swarm & Bee builds vertical AI models and curates platinum-grade training data. We own 128 RTX PRO 6000 Blackwell GPUs (12TB VRAM) and run the full stack — from data generation to model training to edge deployment. Our first vertical is commercial real estate (SwarmCRE), with medical, aviation, and custom verticals available.
What is SwarmCRE?
SwarmCRE is our commercial real estate AI franchise — a family of models from 0.8B (edge/IoT) to 122B (Founder FTW). The flagship is SwarmCRE-9B "Morey," a Mamba-Transformer hybrid trained on 643K platinum CRE pairs. It handles underwriting, IC memos, deal analysis, debt sizing, 1031 exchanges, market intelligence, and more via chat or voice.
What makes your training data different?
Every training pair passes our 8-step SwarmCurator pipeline: Source, Grind, Schema, CoVe (Chain of Verification — 5 criteria scored 1-10), Judge (PASS/FAIL verdict), Trajectory (multi-step reasoning), Cross-Validation (dedup + adversarial probing), and Platinum promotion. The final verdict is unappeallable — 0% override rate across 1.2M pairs.
Can you build custom training data for my vertical?
Yes. We offer Custom Curation Builds for any domain — CRE, pharma, aviation, legal, finance, or something new. You define the specialties, task types, and volume. We run the full 8-step pipeline on 128 GPUs. You receive platinum JSONL with CoVe scores, trajectory labels, and train/eval splits. Contact us to scope a build.
What is BeeBox?
BeeBox is our sovereign edge AI appliance — a plug-and-play device that runs BeeMini-3B (skill router) and SwarmCRE-4B (quality gate) locally. It handles 72% of requests on-device and escalates 28% to the SwarmHQ GPU fleet. Target price: $499. Designed for brokerages, investors, and teams who want CRE AI without cloud dependency.
How do SwarmSkills work?
SwarmSkills are schema-validated AI agent skills callable via a single API endpoint: POST /skill/{name} at router.swarmandbee.com. There are 19 live skills covering CRE tasks from broker analysis to portfolio optimization. Every skill output is quality-gated by SwarmJudge and becomes training data for the next model generation.
Get In Touch

Build With Us

Interested in SwarmCRE, the API, BeeBox, or joining the fleet? Drop a line.