Independent by design.
Assurance is only worth something if the verifier has no stake in the outcome. We sell no services into the transactions we verify, we are protocol-agnostic by architecture rather than allegiance, and we are already selling verification on the same rail we are asking others to trust.
- 01The thesis
- 02Why independence is the product
- 03The founder
- 04The practice behind it
Every economy runs on accounting. The machine economy doesn't have any yet.
We started from a simple observation: nobody in the enterprise could answer how much AI they were actually consuming, or who owned the spend. The deeper we went, the clearer the trajectory became. The problem compounds as consumption becomes autonomous. Once an agent can pay, the unanswered question stops being what did it cost and becomes did we get what we paid for.
One question, three stages.
The same discipline, applied further down the transaction as autonomy increases.
Service assurance.
Independent proof that a service an agent selected and paid for was delivered according to the offer: preflight scoring, signed transaction records, execution evidence, postflight verdicts. Live as VERIFY.
Agent spend accountability.
What did my agents buy, what did it deliver, and what did failure cost? Failed tool spend, duplicate purchases, cost per completed task, reconciled against evidence rather than invoices.
Machine economy intelligence.
Are my autonomous agents making economically rational decisions, and can I prove it to a CFO, an auditor, or a regulator? A longitudinal record of how agent-accessible services actually perform.
Independence isn't a value. It's the product.
You cannot build the assurance layer for agentic commerce from the outside, and you cannot sell it while also selling the thing being verified.
We sell no services into the transactions we verify. A verifier with a position in the outcome is not a verifier. It is a counterparty.
We take no position on which settlement standard wins, because assurance sits above all of them. Shipped coverage today is x402; the rest is roadmap, and we say so.
VERIFY is a working x402 seller: autonomous agents pay per call in USDC on Base to get a verdict before they act. Real machine-native payments, real spend controls, real operating data.
Hard daily capacity, a kill switch, and a full transaction ledger. Autonomous spending controls are a core product requirement, not an afterthought.
Every figure carries its provenance: authoritative, reconciled, estimated, or discovery. Numbers you can defend to a CFO and an auditor.
We say plainly what passive telemetry can and cannot see. That honesty is why a CISO and a CFO can both sign off.
Two decades of enterprise infrastructure, pointed at one question.
Technology strategist, founder, and enterprise infrastructure leader with 20+ years of experience spanning AI, cloud, data centers, security, and business strategy.
Nader helps organizations navigate complex technology decisions, improve operational efficiency, optimize costs, and turn emerging technologies into practical business outcomes. His approach combines technical depth, commercial strategy, systems thinking, and hands-on execution, the same combination InferenceView productizes: honest telemetry, defensible numbers, and verdicts you can act on.
The assurance thesis came out of the work, not a whiteboard. Every advisory engagement surfaced the same unanswered question one step further down the stack, and once agents started paying for services themselves, that question became the whole business.
Independent by design.
InferenceView sells no services into the transactions it verifies. Its job is to preserve evidence before payment, test delivery after execution, and report a verdict a machine can act on.
Where AI should run.
Cloud versus local versus colocation, modeled on real workloads and real hardware economics rather than list prices.
What it should cost.
Token usage by team, workflow and model. Chargeback design, routing, and context compression, installed inside the stack.
Who approved it.
Policy, telemetry and human approvals for sensitive workloads: the same discipline InferenceView applies to agent spend.
The fastest way to see it is on a live resource.
If you are building agent payments, running a marketplace of agent-accessible services, or deciding how much autonomy to grant an agent with a budget. That is the conversation.