Pilot Guide for Growth Marketers

Create a Synter account and select a plan based on expected agent actions and experiment cadence. Solo suits a single operator building against the API; Scale suits sustained automation on a brand workspace.

Record the expected monthly costs for the pilot before connecting accounts. Paid plans include plan flexibility so unused capacity is preserved.

Configure role-based access control, approval workflows, single sign-on, and hard spend caps to preserve operational control while enabling autonomous agent runs.

Add an approver who will receive paused launch notifications and define a budget cap that maps to the pilot daily spend.

Connect the ad accounts for the platforms included in the pilot and verify that each platform entity required can be read and written by Synter.

Synter provides native integrations for Google Ads, Microsoft Ads, LinkedIn, Meta, Reddit, and X. Supported entities include:

• Campaigns and ad groups/sets
• Creatives and audiences
• Negative keywords
• Rollback actions

Record API credential scope and token expiry so the pilot runs without interruption.

Connect analytics and CRM endpoints to normalize conversions and enable multi-platform ROAS reporting during the pilot.

Synter can auto-detect click identifiers (gclid, etc.) and fan out conversion events to ad platforms and analytics tools. Supported integrations include GA4, PostHog, Mixpanel, and HubSpot.

Prepare a one-paragraph brief that states objective, primary KPI, target audience, daily budget, and timeline. The AI agent translates the brief into a channel mix and audience plan.

Include explicit constraints such as maximum daily spend per platform, CPA targets, and creative variants required.

Execute a dry-run preview to inspect the platform-native objects the agent will create. Perform a paused launch to validate API writes without immediate spend.

Synter supports dry-run preview mode and creates campaigns that start paused for safety. Use the dry run to confirm:

• Targeting settings
• Creative formats
• Budgets and bid strategies

Capture the dry-run export, note any differences between brief intent and generated settings, and approve the paused launch when settings meet pilot criteria.

Configure agent optimization rules to perform multi-armed creative allocation, automated budget reallocation by ROAS or CAC rules, and negative keyword management.

Establish a cadence for agent runs (e.g., once per day at UTC midnight) and capture the expected number of optimization runs per week for cost forecasting.

Track usage for chat queries, dry runs, launches, and optimization runs to measure the operational cost of experiments.

Record daily usage alongside daily spend and conversions so the pilot report includes time saved, cost efficiency, and incremental ROAS or CAC changes.

Export the immutable audit log for the pilot period and execute a one-click rollback on a recent change to validate traceability and operational recovery workflows.

Synter logs every action with a rationale from the model, timestamps, actor, and old-to-new values. Logs are immutable and exportable in CSV/JSON formats.

Run a rollback on a low-risk entity during the pilot and measure rollback latency and the fidelity of restored values for your post-pilot operations playbook.

Use Synter's developer API and SDKs to export per-platform ad performance and normalized attribution data, then feed these exports into existing BI or ML pipelines.

Generate a pilot dataset that contains timestamps, usage, platform spend, conversions, and agent decisions so the ROI model can attribute performance changes directly to Synter activity.

You now have a complete, operational pilot plan that produces measurable outputs:

• Campaign creation time saved
• Cost efficiency and unit economics
• Autonomous optimization actions and their impact
• Audit log exports for compliance
• Normalized ROAS data for decision-making

The sensible next move is to execute the pilot under the configured governance and reporting framework, collect the exported datasets, and use the results to quantify time saved and the marginal impact of agentic optimization on CAC and ROAS.