Autonomous customer intelligence
FirstSignal watches every conversation as it happens — scores the frustration inside it, isolates the cause, and resolves it autonomously. Before the ticket. Before the refund request. Before the goodbye.
Watched account · sentiment
78
At-risk right now
6
Median time-to-action
740ms
Resolved without a human
87%
A ticket is the last symptom.
The same customer, two futures.
By the time a customer types a complaint, they have been silently unhappy for days. Scroll the week forward and watch the moment where the two outcomes split.
One pass through the system, shown live. The panels on the right are running the same choreography the product runs on real conversations.
Hear frustration while it is still quiet
Every message is scored 0–100 the moment it arrives. Not keywords — tone, urgency and history. A polite “any update?” from a customer whose last two orders ran late reads very differently from the same words typed by a happy one.
Explain what changed, and why
Memory and retention agents assemble the full picture: who this customer is, what broke, and whether it is one incident or a pattern spreading across accounts. The output is a cause, not a chart.
assembling context…
Fix it before anyone files a ticket
Refunds, redelivery, credits, an outbound voice call when text is not enough — executed autonomously in under a second, then written back to memory so the system never apologises for the same thing twice.
Human minutes: 0
assembling context…
Human minutes: 0
No orchestration theatre. A message enters at the top, and by the bottom of the spine an action has already happened. The line below draws as the signal travels.
Sentiment Agent
Scores every message 0–100 as it arrives — tone and urgency, not keywords.
consumes message→emits score
Memory Agent
Recalls every past order, complaint and apology this customer has ever had.
consumes score→emits context
Retention Agent
Predicts churn before it lands and weighs lifetime value against the risk.
consumes context→emits risk
Resolution Agent
Refunds, discounts, redelivery — executed against real systems, not suggested.
consumes risk→emits action
Proactive Agent
Reaches out before the customer even notices something broke.
consumes risk→emits outreach
Voice Agent
Places a real outbound call when text is no longer enough.
consumes action→emits call
Customer made whole · memory updated · zero human minutes
A real run, on loop. Watch the sentiment score collapse, the agents wake up, and Aria pull the customer back — while the action log fills itself in.
Every response, score and action above is produced by the live system.
Sentiment
79
churn LOWAgents
Actions fired
awaiting signal…
Human minutes: 0
This panel is a living miniature of the real dashboard — the numbers drift the way the live feed does. The full surface is one click away, running on live data.
Conversations
128
Auto-resolved
87%
Saved today
₹41.2k
Avg sentiment
71
Sentiment · last hour
Priya S.
ORD-2847
84
Refund issued
Arjun M.
ORD-2811
41
Discount applied
Neha K.
ORD-2790
68
Watching
Rahul V.
ORD-2764
29
Proactive outreach
Most at-risk account
Agents nominal
Nothing on this page is a projection. Every figure below is produced by the same live environment you are about to open.
0ms
Median time to action
0%
Resolved without a human
0%
Support hours removed
+0
Sentiment recovered
For every ₹1 spent on FirstSignal, D2C brands recover ₹0 in retained customer value.
Inference
- Groq LPU · sub-second reasoning
- Six agents share one signal path
- 740ms median message → action
Data
- Supabase Postgres · row-level security
- Your conversations are never training data
- Memory is per-customer and auditable
Channels
- Vapi outbound voice, browser-native
- Chat, email and order events in real time
- Next.js edge — nothing to install
The signal exists whether or not anyone is listening. FirstSignal is how you start listening — and how the problem is already fixed by the time you look.