Retrieves before it answers
Chat4ED finds the relevant passages in your own documents first, then answers only from them.
Domain-Specific RAG
Grounded in your documents, not the internet.
Domain-specific retrieval connects Chat4ED to your policies, SOPs, contracts and knowledge bases. Answers about how your business works are built from your own documents, with a citation for every claim.
The problem
A general model answers policy questions from what it learned on the internet. That is how an assistant ends up quoting a rule your company never had.
Cites every claim
Each statement links to the document and section it came from, so it can be checked in one click.
Respects document access
People only get answers from documents they are allowed to open.
Sample answer
What an answer looks like.
Illustrative example on sample data.
Which teams have the highest attrition risk, and what is driving it?
Answer 3 of 3 models agreeOvertime above 12 hrs/week drives 38% of attrition risk, followed by no promotion in 18+ months (27%). Customer Support is the most exposed team. Names are only visible to HR business partners.
SQL it ran
SELECT t.team_name,
AVG(r.attrition_prob) AS risk
FROM hr.employees e
JOIN hr.teams t ON t.id = e.team_id
JOIN ml.attrition_scores r ON r.employee_id = e.id
WHERE e.status = 'ACTIVE'
GROUP BY t.team_name
ORDER BY risk DESC;Definitions used
- Individual names masked for non-HR roles
Sources
hr.employeesml.attrition_scoresTry asking
Questions teams ask.
“What is our approval limit for vendor discounts?”
The limit and approver, quoted from the current pricing policy, with a link to the section.
“Which SOP covers a failed KYC check?”
The SOP, the steps it requires and who owns each one.
“Does our leave policy allow carry-over?”
The rule as written in the HR policy, with the clause cited.
Connectors
Works with the data you already have.
Snowflake
BigQuery
Redshift
PostgreSQL
MySQL
SQL Server
Oracle
ClickHouse
Trino- Athena
SAP HANA & B1
Excel & CSV