SuprSend AI

AI-first platform for multi-channel notifications

SaaS

Developer Tools

Artificial Intelligence

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The Agent Skills approach is smart. Pre-loading your notification schema into AI context means the model isn't guessing field names at inference time. At RetainSure we've been building customer-facing alert pipelines, and channel fallback logic when primary delivery fails is the hardest part. Does the MCP server surface delivery failures synchronously to the calling agent, or does it require polling?

Six AI-native interfaces for the same underlying infrastructure is the right call. The Agent Skills piece stands out: loading domain knowledge into context so AI doesn't hallucinate your API schema is what most platform teams miss. How are you handling schema drift when notification configs evolve between agent sessions?

The hardest part of multi-channel notifications isn't the send logic, it's the orchestration layer that decides which channel wins when user preferences conflict with urgency. The AI routing angle is interesting here. How do you handle deduplication across channels when a user might receive the same notification via email and push within seconds of each other?

The Slack Agent is surprisingly useful. Quick lookups that used to mean opening the dashboard - "show me delivery stats for this tenant" or "what preferences does user X have" - now happen right in the Slack channel, visible to the whole team. For the 20 quick checks you do daily, it's way faster than switching apps.

As a marketer, the Slack Agent is surprisingly useful. Quick lookups that used to mean opening the dashboard. "Show me delivery stats for my last campaign" or "what channel is working for campaigns sent to the free tier users" — now happen right in the Slack channel, visible to the whole team. For the 20 quick checks you do daily, it's way faster than switching apps.

I have tried the new agent and Slack App Integration. Honestly, big time saver for me.

Tried building a workflow through the agent and it was surprisingly quick. What I liked is it actually thinks about edge cases I'd probably miss on my own.

The analytics part is what sold me though. Earlier, if something failed, I'd be digging through logs trying to figure out what went wrong. Now I just ask and it tells me — right inside Slack, which is even better.

So now at EOD I literally just ping it — how many notifications went out today, how's engagement looking, did anything fail, and if yes why and how do I fix it. Done in like 30 seconds.

Template creation was our biggest bottleneck. Designing email HTML, creating channel-specific variants for push and in-app, testing with dynamic variables, iterating on copy, testing, observing the stats - a single notification template used to take 6-8 hours end to end. With all these AI tools, I describe what I want and it generates the template with the right structure. Down to under <30 minutes now!

I use the SuprSend Agent daily now. The biggest time saver is delivery tracing - when someone says "I didn't get the notification," I used to dig through logs, check preference settings, channel availability, workflow execution. Now I just ask the copilot "why didn't user X receive the confirmation" and it traces the entire path in 10 seconds. Used to take 15 minutes.

Hey Folks! 👋

I'm Nikita, co-founder of SuprSend. We've been building notification infrastructure since 2022 - used by 500+ companies to send customer engagement notifications across email, SMS, push, WhatsApp, Slack, MS Teams, and in-app.

Today we're launching the AI-first notification platform.

The problem: notification infrastructure has too many moving parts. Workflows, templates, preferences, tenants, channels, routing logic. New customers spent weeks just learning how things connect before writing code. We tried better docs, better UI — it helped but didn't solve it.

So we shipped 6 AI-native interfaces:

→ MCP Server - 24 tools inside Cursor, Claude, Windsurf. Build workflows, create users, manage preferences, debug delivery - all from your IDE. One prompt does what used to take 20 lines of API code.
→ CLI - notification config as code. Pull, version in Git, promote via CI/CD. Your notification stack ships like the rest of your code.
→ Agent Skills - domain knowledge loaded into AI context so it doesn't hallucinate your API.
→ SuprSend Agent - AI copilot in the dashboard for PMs and ops.
→ Slack Agent - trigger, debug, manage from Slack.
→ Claude Extension - use SuprSend directly inside Claude.

What makes this different from just "adding AI" to a product:

→ Every AI action follows notification best practices automatically: channel selection, frequency capping, preference cascades, spam avoidance. You don't need to be a notification expert. The AI already is.
→ Works across your entire team: engineers in Cursor, PMs in the dashboard, ops in Slack. One platform, every interface.
→ Production-safe: destructive operations are restricted, updates do read-before-write, production environments can be locked to read-only.

Results we're seeing:
→ Integration time: weeks → hours
→ Template creation: ~8 hours → 5 minutes
→ Test setup: 15 min manual work → one prompt

Free tier available - try it: suprsend.com/ai

Would love your feedback — what's missing? What notification problems are you still solving manually?

Happy to answer anything here! 🙌