AI Engineering · India
Multi Agents Platform for Influencer Marketing Agency
We built a system that shifts automation from rule-based logic to AI agents capable of handling subjective decisions, combining LangGraph orchestration, Claude models, and vector memory to not just automate workflows, but scale human judgment.
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Client Overview
About Our Client
The influencer marketing agency is focused on executing high-performance, insight-driven creator campaigns. The agency manages the full lifecycle of campaigns—from strategy and influencer discovery to execution, quality control, and reporting.
- Industry
- Advertising
Tech Stack
- LangGraph
- AWS Bedrock
- Qdrant
- Python
- DynamoDB & more.
Requirement
The agency required a system capable of replicating human decision-making within campaign workflows while automating objective tasks and augmenting subjective ones, ensuring consistent outputs across briefs, shortlists, and quality control, enabling seamless end-to-end orchestration across all campaign stages, and continuously learning from historical campaigns to improve performance over time.
Results
Reduced Manual Dependency
Significant reduction in operational overhead and coordination cost
Faster Campaign Execution
Lower end-to-end campaign turnaround time
Consistent Decision-Making
Reduced variance across outputs (strategy, shortlists, QC)
Scalable Operations
Horizontal scalability without proportional hiring
Improved Output Quality
Higher relevance, coherence, and brand fit across deliverables
Our Process
Workflow Decomposition
Broke down manual agency workflows into atomic steps Identified which steps are: Objective → code Subjective → AI agents
Agent Design
Defined each agent with: Inputs Outputs Decision boundaries
Memory & Context Design
Used Qdrant for: Retrieval-augmented reasoning Cross-agent context sharing
Iterative Development
Simulated real campaigns Refined prompts and flows Introduced human checkpoints
Core Solution
Deterministic systems handle objective, rule-based tasks
LLM-driven agents handle subjective, context-aware reasoning
LangChain provides the core abstractions for: Prompt orchestration Tool calling (APIs, retrieval) Structured output parsing
LangGraph enables stateful, multi-agent orchestration
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