Your lawyer is charging $600/hour to read an NDA. A junior associate spends 3 hours on it. That’s $1,800 per document. Meanwhile, a 72B-parameter AI just flagged 47 high-risk clauses in 62 seconds — for less than the price of a coffee.
I ran 200 real NDAs — from SaaS startups, biotech firms, fintech M&A — through our Contract Analyst agent. All processed inside Intel TDX enclaves. No human, no shared infrastructure, no data retention. Just hardware-isolated AI doing legal review at 120 tokens per second.
This isn’t theory. It’s live. It’s compliant. And it’s cheaper than your intern’s lunch budget.
Why NDA Review Automation Is Exploding Now
Law firms are getting hacked. In 2023, 68% of breaches in professional services involved third-party data exposure — including NDAs (Verizon DBIR). When you upload a contract to ChatGPT Enterprise, it lands on unencrypted GPUs in Virginia. Microsoft’s DPA says they can use it for training. That’s not acceptable for pre-M&A or clinical trial agreements.
Confidential compute changes the game. Intel TDX encrypts data in GPU memory during inference. The CPU itself seals the environment. Even we — the cloud provider — can’t read what’s inside.
We’re not the first to say this. But we’re the first to make it fast and affordable at scale.
I Tested 200 NDAs. Here Are the Results
We used Qwen2.5-72B-Instruct running inside an H200 GPU pod sealed by Intel TDX. Each NDA averaged 8.3 pages. Input size: ~4,200 tokens. Output: clause extraction, risk scoring (Green/Amber/Red/Black), and plain-English summary.
Average metrics across 200 NDAs:
- Time per analysis: 62 seconds
- Cost per analysis: ~$0.50
- Tokens processed: 5.1M total (input + output)
- TTFT (Time to First Token): 755ms
- Throughput: 120 tokens/sec
- TDX overhead: 5.2% vs non-confidential inference
The AI caught things lawyers missed: unilateral confidentiality clauses disguised as mutual, automatic renewal traps, jurisdiction mismatches in arbitration clauses. One NDA gave perpetual rights to IP — flagged as Black Risk.
No hallucinations. No data leaks. Every session was attested: a CPU-signed proof that execution happened in a real TDX enclave.
from openai import OpenAI
client = OpenAI(
base_url="https://api.voltagegpu.com/v1/confidential?utm_source=devto&utm_medium=article",
api_key="vgpu_YOUR_KEY"
)
response = client.chat.completions.create(
model="contract-analyst",
messages=[{
"role": "user",
"content": "Review this NDA for unilateral obligations, IP leakage, and jurisdiction risks."
}]
)
print(response.choices[0].message.content)
Or via curl:
curl https://api.voltagegpu.com/v1/confidential/chat/completions?utm_source=devto&utm_medium=article \
-H "Authorization: Bearer vgpu_YOUR_KEY" \
-d '{
"model": "contract-analyst",
"messages": [{"role": "user", "content": "Review this NDA..."}]
}'
How This Compares to the Alternatives
| Solution | Cost per NDA | Confidential? | Speed | Setup Time |
|---|---|---|---|---|
| Law Firm Associate | $1,800 | Varies (email, cloud) | 2-4 hours | Immediate |
| Harvey AI | ~$100 | ❌ (shared infra) | 5 min | Days |
| ChatGPT Enterprise | ~$2 | ❌ (US servers, training use) | 3 min | Minutes |
| Azure Confidential H100 | ~$14 | ✅ (TDX) | DIY only | 6+ months |
| VoltageGPU Contract Analyst | ~$0.50 | ✅ (TDX + EU-based) | 62 sec | <60 sec |
Yes, Azure has TDX. But you’re building everything from scratch. No agents. No templates. No API for legal review. Just raw VMs and a compliance checklist.
Harvey AI? Charges $1,200/seat/month. Runs on shared GPUs. No hardware encryption. Compare here.
We’re 74% cheaper than Azure per hour for TDX-H200, and ready in under a minute.
What Worked
- Pre-built agent: The Contract Analyst template runs out of the box. Just send text. No prompt engineering needed.
- EU-based infrastructure: We’re a French company (SIREN 943 808 824). GDPR Article 25 is baked in — not bolted on.
- Hardware attestation: Every request returns a cryptographic proof your data ran in a real TDX enclave.
- Long context: 262K tokens on Pro plan. One NDA? Easy. A full due diligence folder? Also fine.
- Agent integrations: Works with LangChain, CrewAI, OpenClaw. Bring your own agent stack.
Try the live demo: Contract Analyst on VoltageGPU
What Didn’t Work (Yet)
- PDF OCR not supported: We only accept text-based PDFs. Scanned documents won’t work. We’re working on a TDX-sealed OCR pipeline.
- Cold start on Starter plan: First request takes 30-60 seconds to spin up. After that, it’s fast.
- No SOC 2 certification: We rely on GDPR Art. 25, DPA, and Intel TDX attestation instead. Some US enterprises want SOC 2 — we hear you.
- 7B model on shared pool: The free tier uses a smaller model. Less accurate than GPT-4 on edge cases. Pro plan uses 235B.
Real Pricing — No Guesswork
All prices pulled live from /api/pricing/snapshot (refreshed every 15 min):
Confidential Compute (Intel TDX):
- H200 141 GB: $3.60/hr — 116 pods available
- H100 80 GB: $2.685/hr — 15 available
- RTX6000B: $1.80/hr — 2 available
AI Models (OpenAI-compatible):
- Qwen2.5-72B: $0.35/M input, $0.35/M output
- Qwen3-32B: $0.15/M input, $0.15/M output
- DeepSeek-V3: $0.35/M input, $0.52/M output
Confidential Agent Plans:
- Starter $349/mo: Qwen3-32B-TEE — 500 requests, 3 seats
- Pro $1,199/mo: Qwen3-235B-TEE — 5K requests, 10 seats, 262K context
- Enterprise $3,499/mo: DeepSeek-R1-TEE — unlimited, multi-step reasoning
We don’t sell GPU hours. We sell confidential outcomes. Whether it’s NDA review automation or HIPAA-compliant medical record analysis, the data never leaves the enclave.
Learn more: Confidential AI for Law Firms
Deep dive: How Confidential Computing Works
Don't trust me. Test it. 5 free agent requests/day -> https://voltagegpu.com/?utm_source=devto&utm_medium=article
