Five services.
One lab.
Production-grade.
AI agents, data pipelines, usability testing, Postgres optimization, vector databases, and real-time streaming — built to ship, not demo.
What we build for you.
Five focused services covering the full ML + data stack. Each one is production-grade, not a prototype.
Persona Swarm
Eight distinct AI user personas each drive their own headless browser through your app and report every dead end, blocker, and confusing flow — ranked by priority.
- →No test scripts to write
- →8 personas in parallel
- →Regression diffs on every re-run
- →Accessibility + SEO audit included
Postgres Performance Analyzer
Upload your slow query log or paste a connection string. Get EXPLAIN ANALYZE run across your worst queries, missing index suggestions, bloat reports, and a ranked fix list.
- →Instant slow query analysis
- →Missing index detection
- →Vacuum + bloat diagnosis
- →Actionable ranked fixes
Data Quality Monitor
Schedule automated checks across your pipelines — null rates, schema drift, distribution shifts, row count anomalies. Alerts to Slack or email when something breaks.
- →Great Expectations + Soda Core
- →PSI + KS drift detection
- →Slack & email alerts
- →Dashboard with history
Vector DB & Embeddings Manager
Set up pgvector, ChromaDB, or Weaviate for your use case. We handle chunking strategy, embedding pipeline, re-indexing on updates, and similarity search tuning.
- →pgvector / Chroma / Weaviate
- →Optimal chunking strategy
- →Auto re-index on updates
- →RAG pipeline included
Real-Time Streaming Setup
Kafka + Flink + CDC (Debezium) designed and deployed for your stack. Point-in-time correctness, exactly-once delivery, dead letter queues, and schema registry included.
- →Kafka + Apache Flink
- →CDC via Debezium
- →Exactly-once delivery
- →Schema registry + DLQ
AI Agent Setup
Custom AI agents built for your exact workflow — single agents, multi-agent orchestration, or RAG-powered assistants. Wired into your tools (Notion, Slack, CRM) and deployed to production.
- →Single agent or full multi-agent system
- →Claude / GPT-4 / open-source models
- →Tool use, memory, error recovery included
- →Notion, Slack, database integrations
From request to result.
Each service has a clear, repeatable pipeline. Here's exactly what happens after you hit go.
Drop your URL + goal
Plain English. No test scripts. The same brief you'd send a human tester in a Slack DM.
Site is mapped
A crawler walks the target once and builds a page graph every persona uses for orientation.
Eight browsers launch
Each persona gets its own isolated Chromium — different viewport, patience level, and mindset.
Personas navigate in character
Each agent perceives the live page and decides its next move as that persona, logging every friction point.
Judge ranks the findings
An independent AI judge scores each run and produces a prioritised fix list — not a wall of logs.
Watch the swarm run.
Persona Swarm in action — eight agents each navigate your app simultaneously. The animation below reflects a real run.
Shipping cost only shown on the final checkout step — 3 personas abandoned at this point.
Have a problem that doesn't fit the five?
Describe your ML or data challenge. We'll scope it, price it, and get back within 24 hours.