Endorsements on live book data · reimbursements, cashless, support & onboarding on demo data · ages & SLA computed at open
?
—
my book · live view
total batches
—
on track
—
at risk
—
breached
—
orgs impacted
—
members impacted
—
avg age (days)
—
₹ in breached
—
est. net at risk
TAT health
on track
at risk
breached
pipeline by stage click a bar to filter
aging distribution how long batches have waited
provider breach rate click a row to filter
workload by owner top 10 · breached in red
top accounts by pending batches click any row to drill down
#
account
batches
members
breached
oldest age
batch-level detail
batch id
account
version
provider
plan
stage
created
age (d)
TAT
members
est. net (₹)
assigned
notes
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—
—
batches
—
members
—
breached
—
oldest batch
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⚡ Demo module — sample claims generated for every account in your endorsement book (2–4 each), assigned to each account's real AM. Plug in the real claims export / API and this note disappears.
0
total claims
0
open
0
action needed
0
past due
0
paid
—
claimed ₹
—
approved ₹
0
avg age (days)
Where claims are stuck (open only)
Status pipeline
Member satisfaction (CSAT)
closed cases only · survey sent on closure
Claim ID
Employee
Email
Account
Reason
Type
Claimed ₹
Status
Stuck at
Submitted
Age
Resolve by
CSAT
AM
⚡ Demo module — sample Zendesk tickets generated for every account (2–4 each), assigned to each account's real AM. Plug in the real ZD export/API when the shape looks right.
0
total tickets
0
open
0
urgent / high
0
past due
0
waiting on customer
0
solved / closed
—
first response met
0
avg age (days)
Assignee load (open tickets)
Ticket categories
ZD ticket
Subject
Requester
Account
Priority
Status
Assignee
Channel
Created
Age
Resolve by
AM
—
—
—
—
—
account 360° · everything pending across all modules
⚡ Demo module — onboarding pipeline mirrored on your real Salesforce stages, generated for a subset of accounts and assigned to each account's real AM. Your engineer replaces one array with the Salesforce feed and this goes live.
0
active onboardings
0
stage past TAT
0
go-live at risk
0
in insurer loop
0
waiting on client
0
in policy QC
0
avg days in stage
0
oldest onboarding
Pipeline by stage (Salesforce stages)
Stuck with whom
OB manager load
Account
Stage
Stuck with
In stage
Total age
Policy start
OB manager
AM
Plumy
your AM assistant · live data
⚡ Demo module — cashless pre-authorisation cases generated for every account in your book. Unlike reimbursements, cashless SLAs run in hours: a patient is usually at the hospital desk waiting for approval.
0
active cases
0
patient admitted
0
past SLA
0
awaiting docs
0
denied
—
requested ₹
—
approved ₹
0
avg age (hrs)
Who we're waiting on (open cases)
Pre-auth pipeline
Hospitals with most open cases
Member satisfaction (CSAT)
closed cases only · survey sent on closure
Pre-auth
Employee
Account
Hospital
Treatment
Patient
Requested ₹
Approved ₹
Status
Waiting on
Age
Respond by
CSAT
AM
⚡ Health layer — not another operational dashboard. Every score below is computed from the data already in the other five modules, using the risk-inverted formula in your scoring framework. Scroll to Methodology for the exact weights and the gaps I've flagged.