Your personalized agent. Always running.

When you set up Nylo, the first thing the system does is learn about your business. Industry, funnel stage, audience, geography, goals, KPIs, budget priorities.

From this context, Nylo auto-generates a personalized AI agent tuned to your specific situation. A fashion e-commerce brand gets a different agent than a B2B SaaS company. An agency managing 10 clients gets an agent that understands multi-account dynamics.

This isn't a chatbot with a system prompt. It's a persistent, always-running decision engine that understands what matters to your business.

Your agent combines three types of intelligence:

Structured data

Your marketing and sales data from every connected platform. Normalized, validated, and continuously updated. Spend, revenue, conversions, creative performance, audience metrics.

Market intelligence

Automated web research that adds context you can't get from your own data. Platform algorithm changes, competitor moves, seasonal trends, industry benchmarks. Your agent reads the news so you don't have to.

ML predictions

Statistical models trained on your historical data. Marketing Mix Models, time-series forecasts, anomaly detection, cohort analysis. Not estimated by a language model. Calculated with mathematical rigor.

These three layers combine into a synthesis that no single data source can provide alone.

The output isn't a dashboard. It isn't a chart. It's a concrete recommendation:

"Shift $12K from Meta prospecting to Google brand search this week. Expected ROAS improvement: 15-22%. Confidence: 87%. Evidence: Meta prospecting CPMs increased 28% due to auction competition (web research). Google brand ROAS has been consistently above 5x with capacity headroom (your data). MMM model confirms Google brand is below saturation point (ML prediction)."

Every recommendation comes with:

• A confidence score. How sure is the system?
• Supporting evidence. What data and analysis backs this up?
• The reasoning. Not just "do this" but "here's why."

Insights don't require logging into another tool. They come to you:

• Slack. Formatted messages with key findings and action buttons.
• Microsoft Teams. Adaptive cards with confidence badges and deep-links.
• Email. Scheduled digests with PDF reports attached.
• Google Chat. Webhook-based cards with reaction tracking.
• Dashboard. Full interactive view when you want to dig deeper.

Choose daily, weekly, or custom frequency. Set quiet hours. Configure which types of insights matter to which team members.

This is what separates a recommendation engine from a decision engine.

After every recommendation, Nylo tracks:

• Was it followed? Did the team actually make the change?
• Did it work? What happened to the target metric after the change?
• How accurate was the prediction? Was the confidence score calibrated?

This feedback loop feeds back into the agent. Recommendations that worked get reinforced. Predictions that missed get recalibrated. The system gets smarter with every decision cycle.

Over time, your agent develops an understanding of what works for your specific business. Not generic best practices. Learned patterns from your own decision history.

Every client account gets its own personalized agent. Each one learns the client's specific patterns, goals, and seasonal dynamics. You walk into client meetings with the diagnosis, the context, and the recommended next steps already prepared.

Your personalized agent becomes the analyst you could never hire. Always watching, always analyzing, always ready with the answer. When the CEO asks "why did revenue dip?" you have the answer before they finish the question. Backed by data, ML, and market context.