AI teammates that run your operations
Your team juggles dozens of tools, hundreds of daily handoffs, and a constant stream of alerts that bury the work that actually matters. Nixs.ai gives you autonomous AI teammates — they watch your systems, reason through problems, take action, and get smarter every day. You stay in control; they handle the volume.
What your teammates can do
Each teammate combines four capabilities — from handling the simplest events automatically to catching problems before they happen.
Watch continuously
Monitor every event across your connected systems — around the clock, in real time. Routine patterns are handled instantly without waking anyone up.
React to known patterns
When something matches a playbook — a known alert, a standard approval, a routine escalation — your teammates handle it immediately. No queue, no lag.
Reason through the hard stuff
Ambiguous situations get full reasoning: pulling context from across your systems, weighing trade-offs, and either acting or escalating to a human with a clear recommendation.
Spot what's coming
Over time, your teammates learn which patterns repeat, which escalations can become automations, and which risks are forming before anyone notices. They get ahead of problems, not just react to them.
You already know what's eating your team's time
The tools are fine. Your team is good. But between the alerts, the handoffs, the status checks, and the “can you just quickly…” interruptions — the real work keeps getting pushed to tomorrow. Most AI promises more dashboards or another chatbot. That doesn't fix anything. The gap isn't information — it's action.
How your AI teammates work
Every task follows the same loop. Your teammates observe what's happening across your systems, figure out what matters, decide what to do, take action, and feed what they learned back into the next cycle. Simple things are handled instantly. Complex situations get full reasoning — or a human, with complete context.
Observe
Your teammates connect to your tools and watch everything: events, metrics, thresholds, anomalies. Continuously, in real time. Nothing gets missed and no one gets paged for noise.
How your AI teammates work
Every task follows the same loop: observe, reason, decide, act, learn.
Observe
Your teammates connect to your tools and watch everything: events, metrics, thresholds, anomalies. Continuously, in real time. Nothing gets missed and no one gets paged for noise.
Reason
Not every signal needs a response. Your teammates separate what’s routine from what’s unusual — applying context from across your systems, not just the tool that fired the alert.
Decide
For straightforward situations, they act on their own. For high-stakes or ambiguous calls, they surface a recommendation to a human — with full context, not a raw alert dump.
Act
They don’t just recommend — they execute. Trigger workflows, update records, route communications, close loops. Actions flow across your connected systems in real time.
Learn
Every cycle makes the next one better. Patterns that needed reasoning become automatic. Escalations that repeat become rules. Your teammates compound their intelligence — they cost less and do more over time.
Connected across your stack
Your AI teammates plug into the platforms you already use — no switching tabs, no copy-pasting between tools.
WhatsAppSend and receive WhatsApp messages
GmailRead, send, and manage email
Google CalendarCreate, update, and monitor calendar events
SlackPost messages and monitor channels
GitHubTrack repos, PRs, and deployments
JiraSync issues and track project progress
ShopifyMonitor orders, inventory, and storefront data
Meta AdsMonitor and adjust ad campaignsMore connectors shipping every month.
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Protecting ROAS, around the clock
A worked example. Paid media is one of the most data-intensive workflows in operations — here's what one teammate handles end to end.
Observe
Ingests spend, revenue, and delivery signals continuously — every campaign, every placement, in real time.
Reason
Evaluates ROAS attribution-adjusted, so campaigns with longer conversion windows aren't killed on day-of snapshots.
Decide
Waste threshold breached, or a winner underleveraged? It picks the move: pause, reallocate, scale — or escalate with full context.
Act
Daily summaries, real-time breach alerts, and reallocation recommendations — each with quantified financial impact.
Learn
False alerts recalibrate thresholds. Repeated escalations become rules. The teammate gets cheaper and sharper every cycle.
Campaign "Festive Drop" is down 31% vs. its 7-day attribution-adjusted baseline.
Surfaced 11 minutes after anomaly detection · full context in thread · also sent as daily email digest
Breaches surface the day they form, not at the Monday review.
Every recommendation arrives with projected financial impact before a human approves it.
False alerts tune the thresholds. Repeated escalations become rules. Accuracy compounds.
Your highest-volume workflow could run the same way.
We're onboarding a small number of early design partners and building hands-on with each one.
Recovering revenue you're already losing
A worked example. Indian D2C brands lose up to 70% of carts to abandonment and 25–40% of COD orders to returns before delivery. Here's what two teammates handle — one recovering carts, one protecting order quality.
Observe
Watches every checkout start, cart update, and COD order placement in real time — across Shopify, payment gateway, and logistics signals.
Reason
Abandoned for 30 minutes, or just comparing? COD order from a repeat buyer, or a flagged pin code? Each signal gets context — not just a timer.
Decide
Cart recovery: pick the channel, timing, and incentive. Order quality: auto-confirm low-risk, verify medium-risk, flag high-risk — within guardrails you set.
Act
Sends a WhatsApp nudge at 30 min, an email at 4 hours, a time-limited discount at 24 hours. Triggers COD verification calls for risky orders before they ship.
Learn
Recovery sequences that convert become the default. RTO patterns sharpen risk scores. Both agents feed intelligence to each other — cart behaviour informs order risk.
Customer left a ₹3,200 cart (3 items) at checkout — payment method selected but not completed.
- → WhatsApp reminder sent (30 min)
- → Email with cart link scheduled (4 hrs)
- → 10% discount offer queued (24 hrs, only if needed)
Detected 4 minutes after cart abandonment · sequence adapts if customer returns · daily digest at 9 AM
New COD order ₹4,800 from pin code 844101 (Muzaffarpur, Bihar).
- First-time buyer, no purchase history
- Pin code RTO rate: 34% (above 25% threshold)
- Incomplete address (missing landmark)
Scored 90 seconds after order placement · customer confirmation pending · Riya flagged this buyer abandoned 2 carts last month
Abandoned carts get a recovery sequence within 30 minutes — not a next-morning batch email.
Every COD order gets a risk score with projected RTO cost. High-risk orders are verified before they enter fulfillment.
Cart recovery patterns feed order quality scores. The teammates share context — so the platform gets smarter across the entire purchase journey.
Every checkout. Every order. Handled end to end.
This is what an AI teammate looks like in e-commerce operations — from the moment a cart is created to the moment the package is delivered. Same platform, different domain, same compound learning loop.