Your AI is making things up.
Corral stops unsupported answers before they ship.
Same model. Same question. One guesses. One proves it or blocks it.
Connect the information your team already trusts. Corral checks every answer against approved sources and either ships it with proof or blocks it before it creates support, compliance, or decision errors.
The product promise is simple: no proof, no shipped answer.
One lane guesses. The other checks the approved information first. That difference is what protects budgets, customer trust, and frontline decisions.
Prompt
What's my runway at current burn?
System State
The system guessed from generic SaaS patterns because no approved forecast model was checked.
Output
Based on typical SaaS metrics, you have about 14 months of runway at the current burn.
Prompt
What's my runway at current burn?
System State
Corral checked the approved finance sources. Historical burn exists, but no forecast model supports a runway projection.
Output
I can't project runway from the approved data yet. Here's the burn trend I can verify from the last 90 days instead.
Stop unsupported AI answers before they ship.
Most teams do not need a smarter guess. They need a system that knows when not to answer. Corral checks the proof before output leaves the workflow.
AI Governance
Set the rules for what AI can say, what it must cite, and what gets blocked.
Governance only matters if it changes what ships. Corral turns approved sources, answer boundaries, and audit trails into runtime behavior.
When a request drifts out of policy or the support is missing, Corral narrows the answer, escalates it, or blocks it before the business pays for the mistake.
How Corral Works
Connect the information your team trusts. Corral checks each answer against it and either ships proof or stops the output.
You bring the source of truth. Corral does the checking so the same model can answer with proof instead of confidence theater.
Step 01
Connect the source of truth
Policies, manuals, product docs, transcripts, spreadsheets, or records. Bring the information your team already trusts.
Step 02
Corral checks the draft
The model can answer only from the approved information for that workflow. If support is partial, Corral narrows the answer and shows the gap.
Step 03
Ship proof or stop the answer
Supported answers move forward with proof. Unsupported answers stop before they create support, compliance, or decision errors.
Bring the workflow, failure case, or answer you cannot afford to get wrong. We'll show you where Corral would ship proof, narrow the response, or stop it.
The Cost of a Wrong Answer
A wrong answer can trigger refunds, escalations, wasted labor, or bad internal decisions. The real cost starts when someone trusts it.
47%
of enterprise AI users made a major decision based on hallucinated content (Deloitte, 2025)
~16 min
net weekly time saved after verification burden is subtracted (Foxit/Sapio, 2026)
$18K–$2.4M
cost per hallucination incident, from support to healthcare (Forrester, 2025)
25%
of U.S. adults trust AI accuracy (Edelman, 2025)
Corral is for workflows where people act on the answer right away. That is where proof matters more than fluent copy.
Connect approved sources
Each workflow starts from the information your team is willing to stand behind.
Check the answer
Corral compares the draft to that approved information before it reaches a person.
Ship or stop
Supported answers move forward. Unsupported ones are narrowed, escalated, or blocked.
About
Corral is built by someone who has spent years living with the cost of bad operational information.
Operational systems first. AI second.
I've spent more than 20 years building and maintaining operational systems that have to keep working when the environment is messy, time-sensitive, and expensive to get wrong.
I currently lead IT for a five-location automotive group while building diagnostic AI search, dashboards, and internal tooling. The throughline is the same: if bad information moves through the workflow, the cost shows up immediately in wasted time, unnecessary risk, and avoidable operational damage.
Corral is the product version of that lesson. Answers should earn the right to ship before somebody acts on them.
Operational background
20+ years building and maintaining systems where messy inputs, time pressure, and bad decisions have real cost.
Pressure-tested context
Diagnostic AI search, dealership IT across five locations, and rollout work inside live operations.
Current stage
Founder-led. Working with early teams on workflows where a wrong answer creates immediate cost.
Based in Ontario
Remote from Ontario, Canada, with working sessions available by video.
FAQ
Questions founders, operators, and technical teams ask once the goal is trusted output.
Questions before you put AI in front of customers or staff?
These are the common ones once the goal shifts from a clever demo to answers your team can defend.
Product & Proof
Rollout & Team
Bring the workflow you can't afford to get wrong.
We'll use your real question to show where the model guesses, where the proof runs out, and what Corral would stop before it ships.
Onboarding early teams with high-risk workflows now.
Ontario, Canada · Remote product reviews and implementation planning