AI-Powered Sidekicks to Supercharge Your Success

NinjaCat AI Agents are task‑focused, autonomous assistants that analyze your unified marketing data to monitor performance, surface insights, and accelerate routine work (e.g., PPC hygiene, creative checks, spend monitoring, taxonomy audits, benchmarking, and client comms). They run on top of NinjaCat Data Cloud and your connected sources.

Generic assistants answer prompts. NinjaCat AI Agents are data‑grounded, always‑on workers with defined roles, inputs, and outputs. They read governed marketing datasets, follow guardrails, and deliver repeatable outcomes (alerts, briefs, CSVs, dashboards, recommendations, optimizations), not just ad‑hoc replies.

Across the funnel and channel mix, Agents can handle Paid Search tasks like negative keyword discovery, search‑term mining, geo optimization, and budget reallocation guidance. In Paid Social and Display, they monitor pacing and spend, detect anomalies, perform creative rotation QA, and manage ad operations functions such as yield optimization to maximize inventory performance. For SEO, they generate rank tracking summaries and uncover opportunities. They also support analytics and reporting with cross‑account benchmarking, KPI trend digests, taxonomy/naming audits, and advanced capabilities like media mix modeling to inform budget allocation. In addition, they assist client success teams with renewal alerts, win‑highlights emails, and call preparation briefs.

CMOs and VPs of Marketing use them for performance roll‑ups, budget efficiency tracking, and risk/opportunity alerts. Performance marketers rely on them for PPC hygiene, copy audits, pacing checks, and optimization recommendations. Insights and analytics teams benefit from anomaly detection, cross‑channel benchmarking, and rapid analysis, while marketing and revenue operations teams use them for taxonomy governance, QA, and data quality checks. Client services and agency account management teams employ them for meeting preparation, win summaries, and renewal/expansion cues.  Data, analytics and engineering teams leverage them for governance and custom agent build‑outs on trusted data.

Agents read from your governed marketing datasets in NinjaCat Data Cloud and connected sources (e.g., Google Ads, GA4, Meta, Microsoft Advertising, LinkedIn, CallRail, Sheets, CRM/CDP, data warehouses). You decide which accounts, fields, and time ranges are in scope.

NinjaCat offers 100+ native connectors plus AI Connectors, MCP servers, and Custom Connectors. Data can be shared or written to Snowflake (your account or NinjaCat‑managed), and governed data can be delivered to BI tools (e.g., Tableau, Looker Studio) and downstream destinations.

No. Customer data (including prompts and outputs) is not used to train models. Data is processed to operate the agents, improve reliability, and meet security/compliance obligations under strict agreements.

We leverage leading large‑language and code‑execution models (e.g., OpenAI, Anthropic) orchestrated by NinjaCat. Model routing and prompts are tuned for marketing data tasks. Where appropriate, agents fall back to deterministic logic (rules, SQL, validators) for consistency.

Yes. Data is encrypted in transit and at rest, access is controlled via RBAC and MFA, and services run on hardened cloud infrastructure. Agents operate in a closed system; customers choose what sources are connected and what data is in scope. Governance artifacts (logs, versions, approvals) are retained.

Most agents analyze, audit, and recommend with human‑in‑the‑loop approvals. Where supported, agents can trigger alerts, generate structured files (CSV/Sheets), or call APIs/webhooks to hand off changes to your workflow tools. Write‑back automations are enabled with explicit permissions and guardrails.

Ready to Hire agents are pre‑built and production‑ready once your data connections are set. In Training agents are in Beta or development and available for pilots and early feedback.

Yes. Typical path: define the job‑to‑be‑done → connect/shape the dataset → configure prompts/guardrails → test with sample accounts → set alerts/approvals → roll out across portfolios. NinjaCat can co‑design and templatize agents for scale.

Role‑based permissions, review and approval flows, and confidence thresholds are used to govern outputs. We also employ specialized QA Agents that score and evaluate other agents to ensure accuracy and reliability over time. Agents retain artifacts such as inputs, reasoning summaries, and outputs for auditing. Critical actions require human approval by default.

After connecting your key data sources, most Ready‑to‑Hire agents begin returning insights immediately on historical + current data. Roll‑outs across large portfolios follow a template‑driven onboarding.

Dashboards show what happened. Agents explain why and what to do next, push alerts when thresholds are hit, and draft the assets (briefs, CSVs, emails) to expedite action. Combined, they speed the cycle from marketing data analysis to decision to change.

Yes. NinjaCat runs on Snowflake and can integrate data from any external warehouses (BigQuery, Redshift, Databricks) and BI tools (Tableau, Looker Studio, etc.).

We track hours saved per workflow, time‑to‑insight, reduction in wasted ad spend, lift in conversion rate or ROAS from recommended changes, improved data quality, and client retention or expansion influenced by agent outputs—all of which directly tie to our core value pillars: helping you make money, save money, and mitigate risk.

Pricing reflects the number/type of agents, data volumes, and users, with pilot options for qualified teams. To get a tailored plan, Request a Demo.

Browse the AI Agent Showcase for agents like Negative Keyword Discovery, Search‑Term Mining, Pacing/Spend Monitoring, Cross‑Account Benchmarking, Ad Copy QA, Rank Tracking, Renewal Alerts, and more.

1) Identify 1–2 high‑impact use cases (e.g., PPC hygiene + anomaly monitoring).
2) Connect required sources.
3) Enable the Ready‑to‑Hire agents or build custom agetns.
4) Set alerts/approvals.
5) Run a 30‑day test and benchmark against a control group.