This happened last week
An AI agent wired $50,000 to the wrong company.
The model was confident. The name matched. But "Mercury" resolved to the wrong entity—and no one caught it until the money was gone.
This is what happens when identity isn't infrastructure.
Built for teams shipping AI into production.
The Hidden Tax
Every company building AI is paying it—without knowing.
Your CRM has 47 accounts named "Mercury." Your competitor research is contaminated with data from lookalike domains. Your market map missed 3 rebrands last quarter. Your AI agent can't tell Delta Airlines from Delta Faucet.
No one budgets for this. Everyone pays for it.
This problem existed for decades. AI just made it impossible to ignore.
The Platform
MachineContext is the identity and discovery layer for AI systems.
The guardrail layer before payments, routing, and agent execution. Every response is explainable and auditable.
Resolve
Noisy inputs → canonical entity. "Mercury", "mercury bank", "mercuryfi" all collapse to mercury.com.
Enrich
Get verified brand objects: canonical domain, legal name, aliases, confidence score, freshness, and source attribution.
Search
Find companies by semantic intent. "AI code review tools" → Codium, Qodo, Sourcery. Grounded in what companies say about themselves.
Similar
Find competitors and alternatives. "Companies like Stripe" → Adyen, Square, Checkout.com. Based on actual positioning, not taxonomies.
Monitor
Detect when brands change. Domain migrations, rebrands, acquisitions. Know when SendGrid becomes Twilio.
The API
See the hard cases.
"messagebird""AI code review tools"{
"resolved": true,
"resolution_type": "deterministic",
"confidence": 0.99,
"brand": {
"id": "brand_908...",
"name": "Stripe",
"domain": "stripe.com",
"description": "Financial infrastructure platform for the internet.",
"name_source": "schema:Organization",
"signals": [
"schema_match",
"og_site_name_match",
"high_traffic_rank"
]
}
}Why Now
Identity wasn't infrastructure—until AI agents started making decisions.
For 20 years, company identity lived in spreadsheets. Humans caught duplicates. Analysts knew which "Delta" was which. MDM tools cleaned data quarterly.
Then AI agents started acting autonomously. They don't double-check. They execute on whatever data they're given.
MachineContext is the verification layer LLMs depend on when correctness matters.
Guarantees
Same input produces the same output. No probabilistic drift. Safe to cache indefinitely.
Every response includes source attribution. Know exactly why a resolution was made.
Continuous crawling with freshness timestamps. Know when data was last verified.
Uncertainty is surfaced, not hidden. AMBIGUOUS is a valid response, not an error.
Stop guessing. Start resolving.
If your AI touches real companies, this layer is inevitable.