NEW Charcoal raises $2.5M, led by Mischief & angels from Cursor, Notion & more Read

Agentic search
tailored to your corpus.

Finally, a retrieval agent that works for legal discovery construction claims insurance underwriting financial compliance clinical research legal discovery, construction claims, insurance underwriting, financial compliance, clinical research

Your Document Corpus
πŸ“„
PepsiCo Q1 2022
Earnings Call
πŸ“„
Walmart Q1 FY2023
Earnings Call
πŸ“„
P&G Q3 FY2022
Earnings Call
πŸ“„
Costco Q2 FY2022
Earnings Call
πŸ“„
Coca-Cola Q1 2022
Earnings Call
πŸ“„
Unilever Q1 2022
Earnings Call
πŸ“„
Kraft Heinz Q1 2022
Earnings Call
πŸ“„
Target Q1 FY2022
Earnings Call
πŸ“„
Kroger Q4 FY2021
Earnings Call
πŸ“„
Mondelez Q1 2022
Earnings Call
πŸ“„
Colgate Q1 2022
Earnings Call
πŸ“„
NestlΓ© Q1 2022
Earnings Call
πŸ“„
General Mills Q3 2022
Earnings Call
πŸ“„
Amazon Q1 2022
Earnings Call
πŸ“„
Dollar General Q1 22
Earnings Call
πŸ“„
Home Depot Q1 FY22
Earnings Call
πŸ“„
Kellogg Q1 2022
Earnings Call
πŸ“„
Conagra Q4 FY2022
Earnings Call
πŸ“„
J&J Q1 2022
Earnings Call
πŸ“„
Hershey Q1 2022
Earnings Call
πŸ“„
Tyson Foods Q2 FY22
Earnings Call
πŸ“„
Sysco Q3 FY2022
Earnings Call
πŸ“„
Church & Dwight Q1
Earnings Call
πŸ“„
Campbell Soup Q3 22
Earnings Call
+89 more documents
Charcoal Trace
$ charcoal query "Which companies raised prices vs. absorbed inflation in Q1 2022?"
β–Έ Analyzing query...
β†’ Cross-document comparison detected
β†’ Decomposing into 3 search facets
β–Έ Facet 1: Pricing actions in consumer staples
β”œβ”€ search("consumer staples price increases Q1 2022")
β”œβ”€ search("CPG pricing vs volume Q1 2022")
└─ 9 relevant findings
β–Έ Facet 2: Inflation absorption strategies
β”œβ”€ search("grocery retail margin compression 2022")
β”œβ”€ search("cost absorption vs pass-through retail")
└─ 7 relevant findings
β–Έ Facet 3: Earnings guidance on pricing
β”œβ”€ search("pricing guidance earnings call Q1 2022")
└─ 5 relevant findings
β–Έ Cross-referencing findings across 113 documents...
β†’ Merging overlapping evidence
β†’ Ranking by relevance and specificity
βœ“ Complete: 21 findings across 113 documents
Sources:
β”Œβ”€ PepsiCo Q1 2022 "pricing actions of ~7% across NA beverages"
β”œβ”€ Walmart Q1 FY2023 "chose to absorb majority of cost increases"
β”œβ”€ P&G Q3 FY2022 "price increases avg 5% across all categories"
β”œβ”€ Costco Q2 FY2022 "delayed price increases by 3-6 months"
β”œβ”€ Coca-Cola Q1 2022 "price/mix contributed 7pts to organic growth"
└─ Unilever Q1 2022 "underlying price growth was 8.3%"
Backed by investors from
Mischief Cursor Notion Figma Yelp Front First Harmonic Weekend Fund
The problem

Chunking.
Reranking.
Agent loops.

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.

Use cases

Yes, even for your data.

One API to search, reason, and synthesize across your entire corpus.

Real Estate

Question

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

Findings
23 leases with non-standard escalation terms
5 conflicts with master lease Β§6.3
Learning loop

Deep research that gets smarter every week.

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.

Retrieval quality over time
0 samples 25 samples 50 samples
How it works
01

We ingest your query patterns

Charcoal learns from the real queries your agent makes, mapping them to the underlying terminology, aliases, and patterns unique to your corpus.

02

Any signal on wrong results

Thumbs down, a flag, a correction: any feedback your users or team give on bad results becomes a direct training signal.

03

RL checkpoints ship automatically

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.

I love it.
CTO
AI healthcare company
Compliance

Built for enterprise trust.

AICPA SOC 2
SOC 2
Independently audited controls for security, availability, and confidentiality.
GDPR Compliant
GDPR
Compliant with EU data protection, subject rights, and cross-border transfer rules.
HIPAA Compliant
HIPAA
We handle protected health information. BAAs available on request.
Get started

Retrieval tailored to your corpus.

Thirty minutes with an engineer. Ship a tuned agent over your data within a week.