VoltageGPU vs Ironclad

Ironclad is a Contract Lifecycle Management platform. It owns the workflow that takes a contract from draft, through approvals and redlining, into signature, and into a permanent repository. That platform is good at what it does, the integrations into Salesforce, Workday, and NetSuite are real, and the enterprise procurement track record is genuine. The CLM is not the thing we are comparing against.

The thing we are comparing against is the AI feature set bolted on top: Ironclad AI Assistant, clause extraction, redlining suggestions, repository question-answering. That layer does not run inside Ironclad. It runs on OpenAI and Anthropic, called from the Ironclad backend on AWS US-East. Every clause your reviewers paste into the assistant, every contract the system summarises, every question your team asks the repository, that payload traverses a US-hosted large-language-model data plane operated by a third party. Ironclad provides no hardware attestation that the LLM cannot retain that payload, and neither OpenAI nor Anthropic publishes a TDX-rooted attestation primitive for their enterprise endpoints today.

VoltageGPU is the replacement for that AI layer. The Contract Analyst agent sends its model calls to TEE models (Qwen3.5-397B-TEE or DeepSeek-V3.2-TEE, depending on the plan) that run inside Intel TDX enclaves at our inference provider, a US sub-processor listed in our register. Our servers run the agent and relay the contract text in clear; the host running the model cannot read its memory. GET /v1/attestation lets an auditor check the serving instances against the Intel root on a nonce of their own; it does not bind a given review to a proof.

The honest framing is: if you already run Ironclad and you want the model behind your contract review in a hardware enclave, you do not need to rip out the CLM. You disable the Ironclad AI features, point your workflow at the VoltageGPU Contract Analyst via webhooks or our OpenAI-compatible API, and feed the review back into the Ironclad repository. The CLM stays; the model call moves to an Intel TDX enclave.

A lawyer drafting a contract is producing privileged work product. An M&A team running due diligence is handling material non-public information. A procurement team reviewing a vendor master services agreement is touching commercial terms that competitors would pay for. None of these workloads are abstract privacy questions; they are concrete legal-exposure questions with defined statutory and bar-association rules attached.

Sending that work product to a US-hosted LLM provider is not a checkbox failure. It is a privilege-exposure question that the courts have not finished answering. The 2024 Mata v Avianca opinion and the subsequent state-bar guidance from California, New York, and the District of Columbia have made one thing clear: lawyers are responsible for understanding where the data they paste into an AI tool actually lands, who can read it, and under what jurisdiction it sits. "Our CLM has SOC 2" is no longer a sufficient answer when the AI feature inside that CLM is calling a third-party LLM under a separate data-processing posture.

Intel TDX changes part of the answer. The CPU encrypts the model's memory with a key the host cannot extract, so the host running the model cannot read it. It does not cover our servers, which relay the contract in clear: a lawful request to us could reach what passes through them. SOC 2 says "Ironclad has access controls." Intel TDX says "the host of the model cannot read its memory." Those are different claims, and neither is "nobody can read your contract".

The same reasoning applies to GDPR Article 28 Data Processing Agreements signed under French law, to patient-record review for clinics, and to PCI DSS scope reduction for fintech vendor onboarding: hardware isolation of the model is one of the technical measures Article 32 lets you document, next to the contract and the access controls.

Ironclad wins on the parts of the problem that are not AI. It wins on having a real contract repository with audit trail and versioning, on having a workflow engine that legal operations teams have already configured for their company, on having native integrations into Salesforce for sales-led contracts and into Workday for HR contracts, on having an e-signature flow that procurement teams already trust, and on having an enterprise sales motion that survives an RFP. If the buyer needs all of that and does not yet have a CLM, the conversation is between Ironclad, DocuSign CLM, Icertis, and Juro, and VoltageGPU is not in that conversation. We do not pretend to be.

Ironclad loses on three specific axes. First, on time-to-value: a typical Ironclad rollout is three to six months of implementation and change management, on top of a procurement cycle that usually adds another quarter. VoltageGPU Contract Analyst goes live in two minutes against an OpenAI-compatible API. Second, on AI architecture: Ironclad AI calls OpenAI and Anthropic on standard AWS US-East hosting. There is no enclave, no Intel TDX, no DCAP attestation between the LLM and the cloud operator. Third, on price posture: Ironclad starts at $30K/year for entry-tier with a $5K-$25K implementation engagement bolted on, scales to $96K/year for mid-tier with $25K-$50K implementation, and runs $150K-$200K+/year at enterprise with $50K-$100K implementation. VoltageGPU is $349 / $1,199 / $3,499 per month with no implementation fee, no annual commit, no RFP, and a public price list.

The honest customer conversation is rarely "Ironclad or VoltageGPU." It is more often "Ironclad plus VoltageGPU" the CLM stays as the system of record, and the model calls of the AI review move to an Intel TDX enclave. For customers who do not yet have a CLM and only need contract review (M&A diligence, vendor onboarding, compliance audits, RFP response), VoltageGPU on its own at $349/mo replaces the Ironclad AI Assistant feature directly without buying the surrounding platform.