Engineering

We use agents the way a disciplined engineering team uses people. Work starts as a written specification. It becomes a plan of small tasks with exact files, interfaces and tests. Each task goes to an agent scoped to that task alone. A separate reviewer agent checks the result against the specification before anything moves on.

Humans decide. Agents propose, implement and verify. Specifications, plans, review findings and decisions are all written down, so every change can be traced back to why it was made.

Our library of more than 40 agent skills covers recurring work end to end: building and testing software, running production pipelines, publishing, and research. The same approach is available to clients who want agentic workflows built into their own teams.

Two live consumer products, Cabin and Mo (the AI companion inside QuesMo), run on one shared backend. It handles streaming chat, long-term memory, voice and media, and subscriptions, and it scales with demand on serverless infrastructure.

Memory is designed for accuracy over volume. It recalls what matters and replaces facts that have changed, instead of accumulating contradictions. Model routing is configurable at runtime, so cost and quality can be balanced per request without a redeploy. Prompt caching keeps repeated context cheap.

Changes to prompts or models go through offline evaluations first. Because two products depend on the same service, every change is verified against both before it ships.

A consumer app needs fresh content every day, and writing it all by hand does not scale. A scheduled serverless pipeline gathers trending topics, drafts questions and posts with an LLM, and places them in a queue for review.

A separate publisher releases approved items at a steady pace through the day. Deduplication and validation run before anything is published.

The same pattern is being extended to an AI character that posts in its own voice: generate, render an image or quote card, check for duplicates, then publish.

Every studio production runs through one shared pipeline. It starts with a script and research, then locks each character to reference images so they stay consistent from the first scene to the last. Scenes are generated, assembled shot by shot against the script, and captioned.

The pipeline produces a 16:9 master, then derives vertical cut-downs and thumbnails from it. Because it is shared, an improvement made once applies everywhere.

Quality is checked by machine before a person reviews it. A QA loop samples frames from every clip, scores them against a per-production checklist, and regenerates any shot that fails before assembly.

Scheduled research routines scan papers, open-source releases and technical communities. Each finding is ranked by its relevance to our products. The most important one is researched in depth and archived as a dated digest.

This feeds concrete decisions: which models to route to, which tools to adopt, and where cost or quality can be improved.

New ideas are pressure-tested before they are built. Simulated audience panels react to each concept, and results are always labelled as simulated, never presented as real customer research.