Collect and prepare
Automated workflows ingest heterogeneous media inputs and prepare them for analysis, search, and retrieval.
ContextFounder-led live AI product
My roleFounder and AI Engineer across product, architecture, backend, AI, and infrastructure
Verified outcomeA working end-to-end product from ingestion to review interface
Media analysis starts with fragmented evidence.
Relevant information arrives through many sources and formats. Analysts need a consistent way to collect it, assess sources, connect related stories, and inspect the evidence behind an AI-generated answer.
One operating path from raw feeds to review-ready results.
Analyze sources
Credibility and source-analysis workflows turn raw inputs into signals that can be inspected in the product.
Connect stories and evidence
Story clustering, vector retrieval, and grounded question answering connect outputs to supporting material.
Deliver a usable product
FastAPI services and a product interface expose the workflows for source review, search, and analysis.
Ownership across product and production engineering.
Product and architecture
Defined the product direction, system boundaries, core workflows, and the path from a technical capability to a usable service.
AI and backend delivery
Built data processing, credibility analysis, story clustering, pgvector retrieval, grounded answering, and product APIs.
Cloud and operations
Implemented deployment, serverless inference, CI/CD, monitoring, and the operational workflows required by a live system.
Continuous product work
Connected technical decisions with interface needs, evidence quality, release priorities, and production maintenance.
A live system with inspectable workflows.
A complete AI product, not an isolated model.
The live system connects media collection, source signals, evidence retrieval, backend services, cloud operations, and a review interface. The outcome is demonstrated by the working product and its end-to-end operational scope.
Where this delivery capability creates value.
AI prototype to production
Connect models and data to the backend, infrastructure, monitoring, and interface required for a real release.
RAG and knowledge products
Build retrieval workflows where users can inspect the evidence behind generated responses.
Media and intelligence systems
Turn fragmented inputs into structured analysis, search, and review workflows.
Production reliability
Design deployment and observability as part of the product rather than as work left after the prototype.
Moving an AI prototype toward production?
Share the current product, technical constraints, and the release outcome you need.