Spend stays inside your caps
Hard limits sit outside the agent, so no decision can go past them.
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AI bid & budget management
AI agents that manage bids and budgets inside your limits.
Agents watch spend and pacing across Amazon, Meta and Google, work out why performance moved, and adjust bids and budgets through the official APIs. They start in recommend-only mode, and every action is logged.
What you get
Faster decisions, inside limits you set.
Exceptions handled quickly
Stockouts, creative fatigue and holiday ramps get a response at machine speed, not at the next check-in.
Decisions across platforms
Working from one warehouse, an agent can weigh Amazon against Meta and Google. Platform automation only sees its own data.
Easy to explain to clients
Every decision is traceable, so account managers can show a client what changed and why.
The problem
Beyond static rules
Classic automation is a thermostat: if ACOS > X, lower bid by Y. It works until the situation isn't in the rulebook: a competitor stockout, a creative fatigue curve, a holiday ramp. Human PPC managers handle those by reasoning across context. Agents bring that reasoning to machine speed and machine scale, without the burnout.
Before and after
What changes.
Rules engines
- Static rules: if ACOS is above X, lower the bid by Y
- Rules break on stockouts, creative fatigue and holiday ramps
- PPC managers reason through every exception by hand
Campaign agents
- Exceptions handled without rewriting rules
- Routine bid and budget changes off the team's plate
- Changes made as data lands, not at the next weekly review
- A clear answer when a client asks what changed and why
What campaign agents do
What’s included.
- Continuous monitoring agents that watch spend, pacing, and ACOS/ROAS across every account
- Bid and budget optimization that reasons about context (inventory, seasonality, competition), not just thresholds
- Anomaly investigation: the agent explains why performance moved before proposing the fix
- Action execution through official APIs (Amazon Ads, Meta, Google) with full audit logs
- Approval workflows: recommend-only, approve-to-execute, or autonomous within limits per client
- Weekly digest of what the agent did, what it chose not to do, and why
How we work
How we run it.
Step 1
Agents sit on top of a trusted data pipeline; we build or verify that layer first
Step 2
Start in recommend-only mode so the team can grade the agent before it acts
Step 3
Expand autonomy gradually, guardrail by guardrail, with hard spend limits always enforced
Step 4
Instrument everything: every action traceable, every decision explainable to a client
Typical stack
What it runs on.
- Anthropic / OpenAI APIs
- Amazon Ads API
- Meta / Google APIs
- RAG over campaign history
- Python
- Evaluation & guardrails
Results
Teams we've built this for.
120+ automations live
Tinuiti
We rebuilt the ETL, warehouse and automation layer across Amazon, Walmart, TikTok, Instacart and Criteo.
Read the case study
One pipeline, every client account
SellerFuse
We built the SP-API and Ads API pipelines, live dashboards and automation behind SellerFuse.
Read the case study
50+ campaigns, one dashboard
BTR Media
We built ETL pipelines into one data model and one dashboard for Amazon, Walmart and Instacart campaigns.
Read the case studyFAQ
Common questions.
What can AI agents actually do for ad campaign optimization?
Agents continuously monitor campaign data, diagnose why performance changed, and execute actions (bid adjustments, budget shifts, pausing keywords that waste spend, harvesting search terms) through official ad-platform APIs. Unlike static rules, agents reason about context (inventory, seasonality, competitors) and explain their decisions, functioning like a tireless junior PPC analyst with guardrails.
How is an AI agent different from the rules engines we already use?
A rules engine executes exactly the logic you wrote; it can't handle situations outside it. An agent evaluates the situation against your goals and constraints and chooses an action, including 'escalate to a human.' In practice the best systems layer both: rules for the clear-cut high-frequency decisions, agents for the judgment calls between them.
Is it safe to let an AI agent spend ad budget autonomously?
Only with engineering discipline, which is the actual product: hard spend caps enforced outside the agent, allowed-action lists, approval gates for anything above thresholds, and full audit logs. We start every deployment in recommend-only mode and expand autonomy only as the agent's track record earns it.
Do campaign agents work across Amazon, Meta, and Google together?
Yes. That's where they beat platform-native automation, which only sees its own walled garden. An agent reading from a unified warehouse can notice Amazon ACOS rising while Meta prospecting gets cheaper and recommend a cross-platform budget shift no single platform's automation would ever suggest.
Put an agent on your accounts
Tell us which decisions eat your team's hours. We'll scope an agent that handles them, in recommend-only mode first.