A production software estate — search engines, evaluation tooling, multi-agent runtimes, and the infrastructure that runs them — built and operated end to end by one engineer.
Everything below is deployed, monitored, and serving real users right now. Case studies, live demos, and engagement details live at ark.chakrakali.com.
The estate front door — ark.chakrakali.com. Case studies, live demos, engagement details.
| Metric | Value | How it's kept honest |
|---|---|---|
| Production services | 120+ | live estate inventory, refreshed every 6h |
| Fleet liveness | evidence-based | health asserted by real checks + streamed to an ops cockpit — never timeout guesswork |
| Stall recovery | automatic | supervised recovery on failure, no human in the loop |
| AI memory store | 180,000+ memories | 100% embedding coverage, 60K+ knowledge-graph edges, 1024-dim |
| Frontend quality gate | Lighthouse 100 | deploys are blocked below it — a ratchet, not a goal |
Every number is something a machine already does, every day, in production — not a roadmap. The estate self-heals, self-tests, and refuses to deploy anything that would lower the bar, because that discipline is wired into the pipeline rather than left to whoever's paying attention that day.
| Product | What it does |
|---|---|
| DocForge | Pixel-accurate PDF & PowerPoint generation as an API |
| DeepLens | Federated search across 40+ sources |
| Warden | Smart scheduling with embeddable booking flows |
| StudyMagic | AI-driven spaced-repetition learning platform |
| Isekai Engine | AI narrative RPG with persistent world state |
| HTML deck generator | Animated presentation decks from a prompt |
| Programmatic video | Scripted, voiced, rendered video — fan-out cloud rendering |
| Workflow Master | AI business-workflow engine |
Full catalog with case studies → ark.chakrakali.com
graph TD
U[Users] --> E[Edge: CDN + tunnel ingress]
E --> R[Subdomain router]
R --> P[Product services]
R --> G[Generation pipelines<br/>PDF · decks · slides · video]
O[Orchestration runtime<br/>→ see nexus] -->|supervises| P
O -->|supervises| G
O --> A[AI agent fleet]
A --> M[Memory engine<br/>→ see mindvault]
A --> S[Search / retrieval<br/>→ see scour]
D[Deploy system<br/>build → verify bytes → health → E2E → visual gate] --> P
D --> G
T[Telemetry + audit<br/>→ see aegis methodology] --> O
Agents do the labor, architecture does the discipline. A supervised AI agent fleet handles implementation under hard gates — tests, byte-verified deploys, Lighthouse ratchets, visual verification against production. The interesting engineering is the gates, not the agents.
- The diff is the proof. Claims ship with benchmarks and before/after numbers, or they're marked pending.
- Debug the root cause. A workaround just moves the bug somewhere you'll find it later.
- No silent failures. Detect → confirm → recover → escalate loudly. Liveness by evidence, never by timeout.
- Quality is a ratchet. Gates only tighten. A deploy that would lower the bar doesn't deploy.