Compliance is often seen as a bottleneck—slow, manual, and error-prone. But what if AI could turn compliance into a competitive advantage? At ROIads, I helped automate creative generation and multi-step compliance validation using LangChain and LangGraph. In this post, I'll share how LLMs can be deployed not just to generate content but also to enforce policies and validate requirements in dynamic, rule-heavy domains.
The Use Case: Creative + Compliance
In digital advertising, creating engaging ads is just one part of the puzzle. Ensuring that they meet platform policies, regional laws, and internal standards is equally crucial. Manual validation can be slow and inconsistent.
We built an AI workflow that:
- Generated ad creatives based on product metadata
- Evaluated language and imagery against policy constraints
- Validated keywords against geo-specific blacklists
- Logged compliance results and flagged human review if needed
LangChain + LangGraph = Modular AI Workflows
LangChain gave us the tools to define structured prompts, while LangGraph helped orchestrate multi-step workflows.
Key components:
- PromptTemplates: Tailored inputs to different ad formats
- Agents: Coordinated decision-making for generation vs validation
- Chains: Linear and branching workflows to support conditional logic
Example:
- Agent A generates a creative.
- Agent B checks it against legal guidelines.
- Agent C classifies the tone.
- Results logged into DynamoDB for traceability.
Why LLMs? Why Not Rules?
LLMs offer flexibility and nuance. Rules are great for black-and-white checks, but LLMs can:
- Interpret vague language
- Understand cultural tone
- Generalize compliance patterns across campaigns
We combined LLMs with traditional checks for a hybrid system.
Trust but Verify: Human-in-the-Loop
Not every decision can or should be automated. Our workflow tagged outputs with confidence levels:
- High-confidence passes went live automatically
- Medium-confidence items were flagged for review
- Low-confidence or contradictory results were discarded
Integration with AWS Stack
Our AI workflow was just one part of a larger system:
- Lambda triggered the chain
- S3 stored generated creatives
- Step Functions coordinated between LLMs, validations, and human feedback queues
This allowed us to deploy AI within our already-compliant AWS infrastructure.
Challenges Faced
- LLM hallucinations: Mitigated by strong prompt design and output verification
- Latency: Parallelized checks using LangGraph
- Auditability: Every AI decision logged with inputs and outputs
Key Benefits
- Faster time-to-market for new campaigns
- Fewer compliance errors and regulatory issues
- AI-assisted creativity that adapted to new guidelines without retraining
Conclusion
This project demonstrated that AI can go beyond generation and contribute to governance. LangChain and LangGraph enabled us to build modular, verifiable workflows where creativity and compliance coexisted. The future of AI in operations is not just about output but about trustworthy, auditable systems that evolve with regulations.