All work
AI review aggregator
A shipped product (client withheld for confidentiality) that aggregates and makes sense of reviews at scale — built on Alfred + AWS Bedrock.
Focus:
- Product
- AWS Bedrock
Type:
Product — confidential client
Discipline:
AI application engineering
Tags:
- #Product
- #AWSBedrock
- #Alfred
- #Aggregation
Note: client identities and full system architecture are withheld for confidentiality — enough is shared here to show the shape and substance of the work.
Problem
Reviews and trust signals are scattered across sources, noisy, and slow to digest by hand. The client needed to gather them at scale and turn them into clear, structured insight.
Approach
- Built on Alfred (my multi-agent framework), so collection, extraction, analysis and summarisation each run as specialised steps.
- AWS Bedrock as the foundation-model layer — provider-flexible, production-grade inference.
- Structured, grounded extraction so the output stays faithful to the source reviews.
- Runs continuously, refreshing as new reviews arrive. (Product specifics kept private.)
Result
- Scattered, noisy review data becomes clean, structured, decision-ready insight.
- The multi-agent design lets each stage be improved or swapped without rewriting the pipeline.
- A real, shipped AI product — not a prototype.
Architecture
