From Raw Data to Reliable Insights: Transforming TrustMyApps with Calibo

TrustMyApps (TMA) empowers parents to make informed decisions about their children’s digital experiences by evaluating and ranking educational and child-friendly apps. These evaluations consider parameters such as safety, educational value, cost-effectiveness, and language friendliness.

With the increasing use of AI and large language models (LLMs), TMA needed to modernize its data pipeline. Its goal was to ensure data accuracy, transparency, and scalability — making AI-generated insights both reliable and explainable.

Calibo partnered with TMA on a pro bono proof of concept (PoC) to optimize its data transformation, verification, and retrieval workflows for AI integration.

Main Challenges

Complex and Nested Data

Raw assessment data stored in DynamoDB was deeply structured and difficult for LLMs to process.

Limited Verification

Summaries and severity groupings lacked automated consistency checks or duplication detection.

Data Reliability Issues

Inconsistent records and conflicting insights reduced parental confidence.

Low Query Efficiency

Existing keyword searches couldn’t provide contextual or evidence-based responses.

Business Impact (Metrics)

+60%

Data ingestion speed

Improved by 60% through automated pipelines

+40%

Verification accuracy

Increased by 40%, reducing manual QA time

Query efficiency

Transitioned from keyword search to RAG-powered natural queries

Reliability

Consistent, evidence-backed insights across all assessment records

Delivery speed

PoC delivered in a few weeks, ready for scale-up

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Solution

Calibo built an AI-optimized data and verification framework tailored to TMA’s evaluation ecosystem.

Key elements included:

Data Transformation: Converting raw JSON assessment data into clean, structured markdown summaries grouped by severity (High, Moderate, Strengths).

Hierarchical Chunking: Defined multi-level data hierarchy (App → Safety Concerns → Dark Patterns → Strengths) for improved LLM precision.

Automated Verification: Introduced profiling, duplication detection, and contradiction identification for higher data integrity.

RAG Integration: Developed a Retrieval-Augmented Generation chatbot that references verified data for transparent, evidence-based responses.
REST API Exposure: Enabled external access to the Snowflake knowledge base through a Flask JSON API, supporting client integrations and analytics tools.

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Implementation

The PoC was executed collaboratively between Calibo and TMA through an agile, four-phase workflow:

Data Ingestion: Loading raw assessments into Snowflake.
Transformation: Generating markdown summaries and hierarchical chunks.
Verification: Automating QA checks, profiling, and issue visualization.
Testing: A Streamlit RAG chatbot supported internal validation and demo while TMA used their own UI.

Workshops and sprints were completed within a few weeks, ensuring rapid iteration and transparent knowledge transfer to TMA’s internal team.

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Solution Benefits

Reliable Insights: Every output is traceable to verified evidence, improving parental trust.

LLM-Ready Structure: Transformed data now supports natural language queries and explainable responses.

Operational Efficiency: Reduced manual QA time and eliminated redundant verification steps.

Scalable Framework: Architecture supports expansion into future app categories and additional data sources.

Proven Speed & Collaboration: Calibo delivered a fully functional PoC in weeks, integrating seamlessly with TMA’s existing systems.