AI Brand Reports for CPG Decision-Makers
Case Study: Brandtuit
Brandtuit partnered with Melt Studio to develop an AI-powered brand management platform for CPG brand managers—bringing brand data into one place, analyzing it with AI, and generating ready-to-use, client-presentable reports in a secure, multi-tenant environment.
THE CHALLENGE
Unifying Global + Private Data for Faster Insights
Brand teams needed actionable insights from many sources. The platform required global industry knowledge plus tenant-specific private data—with strict tenant isolation—so users could ask questions, uncover patterns, and generate reports efficiently.
INITIAL STATE
OUR SOLUTION
We designed a multi-agent system with a two-tier knowledge layer (Global + Tenant-Specific) to ingest, analyze, and synthesize information in real time—producing non-obvious recommendations and professional-quality reports through iterative sprints.
Requirements
Two-tier knowledge scope.
Defined the Contextual Intelligence Layer: a Global Knowledge Layer (frameworks, definitions) and a Tenant-Specific layer for proprietary content—plus retrieval rules that preserve isolation.
Application
Agent + retrieval building
Specified backend services via API, integrating LangChain for retrieval orchestration and agent workflows, LangGraph for complex reasoning flows (as needed), and LangFuse for observability.
Prototype
Report pipeline iteration
Prototyped knowledge-store needs: vector storage and metadata for traceability and access control, with vector DB options noted (Pinecone/Weaviate), plus embedding APIs and search endpoints.
DELIVER STATE
CUSTOMER FEEDBACK
Report Quality Improved via V10 Evaluation Pipeline
Sprint reviews documented report-quality gains in “Report V10,” driven by a new evaluation + score aggregation system. The team hardened the pipeline with a model-specific “prime” step and improved preparation for client-ready delivery.
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Suyo
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October 9, 2020
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Happyly
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January 7, 2021
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