Applied AI for decisions where being wrong gets expensive.
A surprising amount of the evidence is already public.
Public filings, regulatory records, and market data can get us a long way. This bond is one example: we show what the public record supports, link the sources, and make the gaps visible.
Your data makes the workflow specific to you.
Add your contracts, positions, policies, invoices, or internal records. We turn the combined evidence into a workflow your team can use again.
Another example
The same approach works in procurement.
The bond above is one example. The same approach works when the decision is about spend, contracts, or vendors.
Procurement
Should we accept this vendor increase?
Compare the contract, market prices, past pricing, public alternatives, and your own purchase data. Then show what the vendor uniquely provides and what you could replace.
Keep it. Renegotiate it. Knock it out.
How we work
Command. Reveal. Run. Keep.
Name the decision. Show the evidence. Run the workflow. Keep what we build.
01 / Command
Name the decision.
Pick the question, workflow, report, or spend decision that matters now.
02 / Reveal
Show the evidence.
Bring the sources together and make clear what is known and what is still missing.
03 / Run
Run the workflow.
Turn the evidence into a report, monitor, review flow, or other working process.
04 / Keep
Keep what we build.
The data work, rules, workflow, and documentation stay with your team.
A fair question
“Can I just ask Claude?”
Yes. We do too. The problem is knowing what to trust when a model sounds confident.
We connect the model to the right evidence, checks, and workflow so your team can use it in a real decision and run the same process again.
Two examples built on real data, with the sources visible.
Source check
Ask a question, then check the sources.
Loom uses public data to respond to questions. Each response shows the source, what changed, and the date the data was true. Ask for evidence and you can open the full source trail.
An agent built this signal from public data in days. It combines energy prices, producer prices, and industry use to show when an increase may hold. A team's contract data makes it more precise.