01 / Research
Game enrichment agent
Public game sources become a structured research record after required-field and completeness checks.
Proof in production
StreamGist from 0 → 1.
Its one-page read helps game studios understand streamer demand, reach, and audience before outreach. The underlying AI/data product runs end to end in production.
StreamGistRecommendation
Example gameScore 82
RisingWhy this game?
Clear choices give viewers something to react to, with enough pace to keep a stream moving.
Warning tags checkedEvidence-backed
Decision support
A practical explanation, not just a score.
The product combines public Twitch activity, game research, creator preferences, and warning signs before someone goes live.
Open the live product1M+product signals processed each week
47automated checks running around the clock
1,000+creators reached by the product
Why it matters
The company runs on its own execution system.
StreamGist is a self-funded SaaS operated by one person through an agentic orchestration layer. Recurring research, monitoring, and decision preparation run through bounded workflows, creating operating scale that would otherwise require a larger team.
The customer-facing core remains deterministic and testable. AI coordinates and accelerates the work around it, while consequential changes stay reviewable and a model failure can't take down the product.
Representative agents
Different jobs. The same operating contract.
Three examples from a broader agentic operating system. Each has defined inputs, code-enforced controls, and reviewable outputs.
02 / Monitor
Recommendation quality agent
User choices and stream results are checked against preset quality and alert rules to produce a daily quality read.
03 / Draft
Content agent
Game research and product data become a reviewed editorial draft after source and claims checks plus human review.
Public documentation
Inspect how StreamGist operates.
Here's how StreamGist turns signals into decisions, where AI can act, and what still works when it can't.
The operating model
See how recurring signals become reviewable decisions, who owns the call, and what happens afterward.
See the operating modelThe AI boundary
See where StreamGist uses AI, where ordinary code takes over, and what keeps working when a model doesn't.
See the recommendation architectureNeed a product leader who can build and operate AI in production?
StreamGist is the proof. If you're building something similarly ambitious, let's talk.