Role
Technical Product Lead
Work / AI and Data Product Prototype
Designing an AI-Powered Intelligence Pipeline
Transforming fragmented public data into structured, searchable and decision-ready intelligence.
A functional prototype and technical product case focused on turning public information into a reusable intelligence workflow through data pipelines, semantic search and AI-assisted analysis.
Context
AI and Data Product Prototype
Focus
Data Pipelines, Semantic Search, LLMs and Product Strategy
Data Sources Map
From Fragmented Sources to Structured Data
Company websites
Public databases
Industry sources
Documents
External APIs
Collection Layer
Data Collection Layer
Consistent data flow
Company websites
Public databases
Collection Layer
Data Collection Layer
Consistent data flow
External APIs
Industry sources
Documents
Challenge
The Challenge
Relevant business information was distributed across multiple public sources, making research slow, repetitive and difficult to scale.
The challenge was to design a product capable of collecting, structuring and enriching this information for faster analysis and decision-making.
Pipeline Diagram
Intelligence Pipeline
01
Sources
Public and external data.
02
Collection
Automated data acquisition.
03
Processing
Cleaning and normalization.
04
Enrichment
AI-assisted classification and context.
05
Structured Storage
Organized and searchable records.
06
Search and Analysis
Relevant insights for decision-making.
What I Did
What I Did
The role combined discovery, documentation, orchestration and product decision-making.
- Defined the product scope and MVP
- Mapped data sources and processing stages
- Designed the end-to-end user and data flow
- Evaluated technical alternatives and trade-offs
- Structured requirements for enrichment and semantic search
- Estimated infrastructure and processing costs
- Built and documented a functional prototype
The Solution
The Solution
A data intelligence pipeline designed to transform fragmented information into structured and searchable insights.
- Automated data collection
- Information normalization and enrichment
- Structured storage
- Semantic search
- LLM-assisted analysis
- Traceable source references
- Scalable processing architecture
Semantic Search
From Question to Traceable Answer
01
User question
02
Semantic retrieval
03
Relevant records
Retrieved before response generation.
04
AI-assisted response
05
Source references
Product Approach
Product Approach
The prototype was shaped around a simple product goal: reduce research friction while preserving source traceability and technical viability.
Each design decision connected user needs, data quality, retrieval relevance and operational sustainability.
Flow
01
Business question
02
Data discovery
03
Collection
04
Enrichment
05
Search
06
Decision support
Technologies and methods
Product
MVP definition / User flow design / Technical trade-offs / Cost estimation
Data
Automated collection / Normalization / Enrichment / Structured storage
AI
Semantic retrieval / LLM-assisted analysis / Traceability / Search experience
Additional evidence
Additional evidence
These diagram spaces are intentionally portfolio-safe and explain how the prototype connected public data, semantic retrieval and AI-assisted analysis without relying on fake screenshots.
Decision Criteria
Product and Architecture Trade-offs
Decision Core
Technical Product Decisions
Scalability
Data quality
Traceability
Processing cost
Maintainability
Scalability
Data quality
Decision Core
Technical Product Decisions
Maintainability
Traceability
Processing cost
Impact
Impact
The project demonstrated how fragmented information could be transformed into a reusable intelligence product.
It also showed my ability to connect product definition, technical architecture, AI capabilities and operational cost analysis.
Contact
Need someone who can connect product thinking, data workflows and AI capabilities into a credible prototype?
This case shows how I turn a research-heavy problem into a structured product concept with technical direction, traceability and realistic implementation trade-offs.
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