Where identity failures cost money.
These aren't hypotheticals. These are the scenarios companies discover after the damage is done. MachineContext prevents them at the source.
An AI procurement agent approved a payment to the wrong company.
The Scenario
A finance team deployed an AI agent to approve routine vendor payments. An invoice arrived from "Mercury Technologies." The agent matched it to a vendor in the system and approved payment.
The Problem
There are at least 4 real companies called "Mercury": Mercury Bank (mercury.com), Mercury Insurance (mercuryinsurance.com), Mercury Systems (mrcy.com), and several smaller ones. The agent picked the one with the highest string similarity—which was wrong. $50K, 6 weeks to resolve.
A PM thought they knew the competitive landscape. They missed 8 direct competitors.
The Scenario
A product manager at a developer tools company maintained a competitive analysis tracking 5 known players. Leadership asked for a comprehensive market landscape before a funding round.
The Problem
The PM's list was built from memory and occasional Google searches. They missed 8 direct competitors—including 2 that had raised $50M+ and 3 that were targeting the same enterprise segment. The investor deck looked incomplete. Due diligence revealed the gap.
An analyst spent 2 weeks building a market map. MachineContext does it in 2 seconds.
The Scenario
A VC analyst needed to map "all companies building AI code review tools" for a thesis on developer productivity. Traditional approach: Google searches, Crunchbase filters, Twitter threads, asking around.
The Problem
Static databases categorize companies by old taxonomies ("DevOps Tools", "Code Quality"). New entrants with novel positioning don't fit. The analyst misses Cursor, Codeium, and Supermaven—all describing themselves differently. The market map is already stale when it's finished.
Your CRM still has "SendGrid" as a vendor. SendGrid became Twilio in 2019.
The Scenario
An operations team audited their vendor list and found "SendGrid" active. They spent hours trying to reconcile invoices. Eventually someone remembered: SendGrid was acquired by Twilio in 2019. The domain still works, but the company doesn't exist as a separate entity.
The Problem
MessageBird became Bird. Segment became part of Twilio. Auth0 became Okta. Rebrands and acquisitions happen constantly. Your data doesn't update itself. Every stale record is a reconciliation headache waiting to happen.
A sales rep worked a dead lead for 3 months. A colleague closed the same deal under a different record.
The Scenario
An enterprise CRM had accumulated 47 accounts containing "Delta." Some were Delta Airlines. Some were Delta Dental. Some were Delta Faucet. Some were the same company entered multiple times with slightly different names.
The Problem
Sales rep A was assigned "Delta Corp" and spent 3 months nurturing. Sales rep B closed "Delta Corporation"—the same company, different record. Commission dispute. Customer confusion. The lead routing logic never knew they were the same entity.
Autonomous agents that actually understand the market.
The Vision
Imagine agents that continuously monitor your competitive landscape. They track pricing changes, positioning shifts, new market entrants—and they do it with verified identity, not scraped noise.
The Unlock
With MachineContext as the ground truth layer, these agents aren't hallucinating. They're not mixing up competitors. They're operating on verified, fresh, canonical data—and they can act autonomously because you can trust what they see.
These failures are preventable.
Every scenario above starts the same way: an AI system acted on incomplete or ambiguous identity. MachineContext catches the problem before it becomes expensive.