Product
Ask once. Approve once. Run it again.
Inspect and correct the AI agent's work.
Traceable
What happens when you ask Bridgr a question?
-
Connect
- Bridgr looks at what's in each data system you connect.
- Everyone only sees the data they're allowed to see. How access works
-
Understand
- Bridgr spots when two records are the same customer, and explains why.
- Fix something once and Bridgr remembers it.
-
Answer
- Choose Ask, Plan, or Auto mode to control AI agent actions.
- The plan shows the steps, sources, and cost before it runs, and you approve it once.
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Verify
- Pick any figure in an answer, like "14 customers," and see the exact records behind it.
- Bridgr tells you when data was missing or shaky.
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Monitor
- Repeat runs reuse the same work, so they stay cheap.
- If your data changes, Bridgr adjusts and lets you know.
All your data systems now talk to each other.
Found the same customer in three systems
- Databases
- Cloud storage
- Documents & PDFs
- Spreadsheets
The living map
- Web & APIs
- Email & inbox
- Dashboards & BI
- Orders & commerce
This spreadsheet feeds that report
Built to connect to PostgreSQL, Snowflake, Neo4j, MongoDB, Delta Lake, Parquet, CSV and Excel files, and unstructured text.Product names are trademarks of their respective owners.
Rely on agent-run analysis with transparency.
Which city has my biggest customer base?
Winston-Salem, NC has almost 3 out of every 4 of your customers (74.7%).
- From your customer records13,666 of your 18,306 customers are in Winston-Salem. That's about 11 times more than Greensboro, your next-biggest city, with 1,231.
- From your CRMYour CRM shows 74.6% of the contacts your sales team tracks are in Winston-Salem.
- From your call logIn each of your top six cities, about the same share of calls reach the customer (37.5% to 40.7%). Winston-Salem leads because more customers are there.
- From your customer recordsYour biggest opportunity is winning customers back. 61.6% of Winston-Salem customers haven't ordered in the last 2 years.
customer-base.ipynbagent-authored
In [1]:
bridgr.sources()Out[1]:
| erp | PostgreSQL | erp.customers | 18,306 rows |
| crm | Snowflake | crm.contacts | 21,442 rows |
| calls | CSV file | call_log.csv | 96,120 calls |
In [2]:
-- one question, three systems
SELECT e.city_norm,
COUNT(DISTINCT e.customer_id) AS customers,
COUNT(DISTINCT c.contact_id) AS crm_contacts,
AVG(k.connected) AS engagement,
COUNT(DISTINCT e.customer_id) FILTER (
WHERE e.last_order_at < now() - '24 months'
) AS inactive_24m
FROM erp.customers e
LEFT JOIN crm.contacts c ON c.erp_ref = e.customer_id
LEFT JOIN call_log.csv k ON k.phone_e164 = c.phone_e164
GROUP BY e.city_norm
ORDER BY customers DESC
Bridgr matched 66% of ERP customers to CRM records using customer IDs and phone numbers.
Out[2]:
| city_norm | customers | crm_contacts | engagement | inactive_24m |
|---|---|---|---|---|
| Winston-Salem | 13,666 | 10,444 | 38.2% | 8,415 |
| Greensboro | 1,231 | 966 | 40.7% | 702 |
| High Point | 894 | 712 | 37.5% | 511 |
… 73 cities · 3 sources · 1 query
Representative example based on a real analysis. Figures are illustrative.