Real Estate
Which of our 340 leases have rent escalation terms that conflict with the master lease agreement?
340 documents Β· 4 search hops Β· 18 findings
Finally, a retrieval agent that works for legal discovery construction claims insurance underwriting financial compliance clinical research
Complex queries still break. In legal, a missed exhibit. In construction, seven figures left on the table.
You're building retrieval infrastructure. You should be building your product.
One API to search, reason, and synthesize across your entire corpus.
Which of our 340 leases have rent escalation terms that conflict with the master lease agreement?
340 documents Β· 4 search hops Β· 18 findings
Do any of these 200 contracts have a liability cap under $1M that conflicts with the master agreement?
200 documents Β· 3 search hops Β· 12 findings
Were there any adverse events across these 1,200 trial documents that correlate with protocol deviations?
1,200 documents Β· 6 search hops Β· 31 findings
How have the risk factor disclosures changed across our last 8 quarters of 10-K filings?
847 documents Β· 5 search hops Β· 14 findings
Does this claimant have any prior losses on file that conflict with their current submission?
524 documents Β· 4 search hops Β· 9 findings
Are there any conflicting environmental impact assessments across these permit applications?
453 documents Β· 5 search hops Β· 16 findings
Which of our carrier agreements have force majeure terms that differ from our standard contract?
831 documents Β· 3 search hops Β· 11 findings
Charcoal uses RL directly on your data to learn how best to navigate it. Every production query, every correction, every flag feeds back into the model.
Charcoal learns from the real queries your agent makes, mapping them to the underlying terminology, aliases, and patterns unique to your corpus.
Thumbs down, a flag, a correction: any feedback your users or team give on bad results becomes a direct training signal.
Feedback feeds into our RL loop. Improved model checkpoints are validated and deployed β no manual tuning required.
This is exactly what we were trying to build ourselves. An extremely well-tuned model for our specific use case, with evals and RL built in, so I don't have to build or maintain any of it.
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Thirty minutes with an engineer. Ship a tuned agent over your data within a week.
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