Our work - Proven solutions for real-world problems.

Custom ETL pipelines ingest data from APIs, CSVs, public databases, and scraped websites into a unified PostgreSQL schema with entity relationships across sources.

A RAG system with pgvector handles unstructured content while MCP exposes structured queries as tools, enabling researchers to ask questions in natural language.

For developers, we set up Claude Code with shared conventions, custom skills, and quality hooks that increased prototyping speed by 3x.

For business users, we created a sandboxed environment with no-code AI tools and mandatory security scanning, enabling them to build internal dashboards without engineering support.

We designed a four-stage pipeline where deterministic validation and fraud checks run before any AI evaluation, ensuring auditability and regulatory compliance.

The system reduced average processing time from 5 days to 4 hours while improving fraud detection rates by 40%.

We built a four-stage pipeline that combines deterministic scraping with LLM-powered scoring and tailoring, processing hundreds of applications per day with 3x higher response rates.

The system handles multiple ATS platforms with dedicated handlers and falls back to AI-guided browser automation for unknown application forms.

We identified the key signals that reveal AI-generated text: vocabulary patterns, uniform sentence length, structural consistency, and the absence of natural imperfection.

Through experiments including fine-tuning on personal chat data, we developed practical techniques for more natural AI conversation.