Lantern connects NetSuite, Salesforce, Zendesk and Snowflake, runs AI steps between them, and keeps a readable trace of every record it moved, every field it mapped and every call it made.
14,209 syncs in the last hour at Halvard Supply. 3 waiting for a person.
nice flow!Most integrations fail quietly. A field changes in Salesforce, an order never reaches NetSuite, and someone hears about it a week later from a customer. Lantern shows each sync as it runs, explains every AI step in one plain sentence, and asks a person before anything risky goes through.
View use casesA live map of your systems with throughput and health on each line. Click a line to read the last 50 records that crossed it.
NamecompanyNameexactBillingCountrydefaultAddress.countryexactTax_ID__cvatRegNumber0.99Payment_Terms__c"Net 45" → 45 · from 200 records, 0 conflictsLantern proposes field matches with a confidence score and a sample value. You accept, edit or reject each one, and nothing maps silently.
"We were charged twice for invoice INV-55120, please refund the second payment of $6,250."
PII redacted: 1 email, 1 phoneintent: billing.duplicate_chargeinvoice: INV-55120amount: 6250.00
Classify a ticket, check credit terms, pull fields from a vendor bill. Every AI step shows its input, its output and one sentence on why.
Rule: credit memos over $5,000 need Finance. Ticket #88214, duplicate charge on INV-55120.
Assigned to Hannah Cho, ControllerSet a rule once: memos over $5,000, new vendors, price changes. Lantern pauses the run and asks the right person in Slack or in the app.
Open any run and read it top to bottom: the trigger, each call, each mapped field, each AI decision, with timing and payloads. When something fails, the trace shows the field, the record and the reason.
{
"waiting": "trigger"
}
Most teams have their first flow in production in five to ten working days, mapping review included.
Sign in to each system with OAuth or a scoped key. Lantern reads the schema and samples 200 records from each object.
Start from a template or a trigger. Accept the suggested mappings, add AI steps and approval rules, then test on real records in staging.
Promote to production. Every run leaves a trace, failures open an incident with the affected records, and the audit log keeps who changed what.
SalesforceAI credit checkNetSuiteSlack
Closed deals become sales orders in NetSuite within a minute. An AI step checks credit terms, and anything over $25,000 waits for Finance.
41 s median from Closed Won to sales order, down from next morning
NetSuiteLanternSalesforceStorefront
Stock levels leave NetSuite every five minutes and land on the account and the product page, so reps stop promising what the warehouse does not have.
5 min stock freshness, down from 24 hours
ZendeskAI intentNetSuiteFinance queue
Tickets about billing are read by an AI step, matched to the invoice in NetSuite and routed to Finance with the amount and history attached.
62% of billing tickets routed without a person
GmailAI extractApprovalNetSuite
Vendor bills, refunds and credit memos reach NetSuite with approvals attached and a full trail your auditors can filter by account and date.
3 days faster month-end close at Halvard Supply
148 connectors maintained by our team, plus REST, GraphQL, webhooks and SFTP for everything else.
“We used to find broken syncs when a customer called. Now the trace shows us the field, the record and the reason before anyone notices.”
Mara LindqvistHead of Business Systems, Halvard SupplyTomás Ferreira published Order to cash v13
Hannah Cho approved credit memo CM-4471
Ama Owusu accepted mapping Payment_Terms__c
Gus Whitaker rotated the Snowflake key
Mara Lindqvist set Ticket to credit memo to approval mode
$490per month
For one ops team getting its first flows off spreadsheets.
$1,900per month
For companies running finance and support on synced data.
Customannual contract
For regulated teams with many systems and their own models.
One execution of a flow from trigger to last step. Retries and replays of the same run are not counted again.
A hosted model under our data agreement by default. Enterprise plans can point AI steps at their own model endpoint. Inputs are redacted for PII first and never used for training.
Only if you allow it. Each flow runs in one of three modes: shadow (read and propose), approval (a person confirms every write) or live.
Lantern retries with backoff, holds the run and opens an incident listing the affected records. When the API recovers you replay them in one click.
No. Teams move flows over one at a time, and Lantern can run in shadow mode next to your current tool while you compare traces.
Five to ten working days for the first flow in production, including a mapping review with one of our engineers.
Book 30 minutes with an integration engineer. Bring one flow and we will build it in staging on your real schema.
Or open the live app with sample data from Halvard Supply.