Not AI opening Ads Manager
Control is server-to-server through Marketing APIs. The agent can only request predefined tools, and the system checks permissions and rules before execution.
Reblo
It connects your store to ad platforms, calculates profitability after real costs, then runs an agent through official interfaces — with limited tools and a policy engine that keeps every action inside your limits.
What Reblo does
- Connects store data with Meta Ads and more, and calculates whether ads are profitable after product cost, discounts, fees, shipping, and ad spend.
- Continuously monitors campaigns and ads, and surfaces what deserves more budget and what wastes money.
- Connects accounts with OAuth and official APIs — no passwords, and no click simulation inside Ads Manager.
- Proposes decisions, then runs them through organization policy before any execution: budget caps, approvals, and risk checks.
- Learns from the outcome of each decision on your account: what worked, and what damaged performance.
- Commerce integrations in order: Salla → Zid → other commerce platforms. Ads: Meta first, then Google and TikTok.
The critical layer: Policy Engine
Even if AI recommends a 40% budget increase, if your limit is 15% or the change needs approval — it does not go beyond. The hand is constrained by Reblo’s safety system.
Intelligence without training a model from scratch
A strong base model + limited tools + Reblo rules + account memory + outcome loops. We collect real decisions first, then evolve a proprietary model only if it becomes worthwhile.
Profit after costs, not ROAS alone
Before any ad decision, true contribution is calculated after costs. Financial reality leads the media decision — not the other way around.
Decision path
AI proposes. Policy governs. Execution is safe. Outcomes are reviewed.
Monitor and analyze
Read performance, profitability, inventory, and tracking quality from the store and ad accounts.
Recommend with limited tools
Increase/decrease budget, pause an ad set or ad, draft a campaign, run an experiment — only through predefined tools.
Policy, risk, and approval
Financial and operating rules, customer limits, and approval requirements before any change reaches the platform.
Execute, verify, and learn
Execute through the API, read the result after write, and record impact after 24/48/72 hours in account memory.
Decision example
The agent proposes a 40% budget increase. The Policy Engine says: the allowed limit is 15%, or approval is required. Only what is allowed is executed, then the outcome is measured — no limit bypass even if the model is wrong.
An AI media buyer working 24/7 on your account — through official APIs, with a hand constrained by Reblo policy. Not just a dashboard that gives analysis.
Capabilities used
Commerce
Products, catalog, pricing, inventory, orders, cart, and inventory reservations.
Data and analytics
KPIs, reports, events, and alerts inside several products.
Integrations
Webhooks, inbox, adapters, and ERP, payments, and shipping connection patterns.
Artificial intelligence
Summarization, search, assistance, and contextual processing inside work systems.
Automation
Events, workers, outbox, idempotency, and operating rules inside the systems.
Talk to us about your system
Share a short brief. We will follow up to understand the process and what can be built on the existing foundation.