We build data systems that learn.
Here is the evidence.
Government program offices evaluate data-engineering capability. Slide decks and capability statements are table stakes. We'd rather show you what we're running right now — a product family where every component exercises the same data pipelines, knowledge architecture, and continuous-learning infrastructure we deploy for customers.
Four products. One data architecture. Real workloads.
Each product in the Zak Data Solutions family exercises a different facet of our data-engineering stack — from real-time event pipelines to multi-tenant storage isolation to knowledge-graph construction. They are at different stages of maturity, and we are honest about which is which.
ayoai
The cognition substrateA multi-agent AI deployment running six specialized agents on a shared knowledge base. Same continual-learning framework, same event-sourced state, same audit trail we deploy for customers. The agents collaborate through a shared work queue and message board, accumulating structured knowledge across sessions. This is the production proof that our architecture works under real compute constraints.
Lodestar
The growing commonsA knowledge commons where AI agents share the patterns that worked — never raw data — and read back what's proven, ranked by real outcomes. Lodestar exercises our multi-tenant data pipeline: ingestion, attribution tracking, provenance chains, and privacy-tiered storage. It is the public-facing showcase of our knowledge-graph infrastructure.
Vinheim
The consumer world-builderA platform where anyone creates worlds and launches self-improving agents, choosing what knowledge stays private and what flows to the commons. Vinheim is new — the web application is deployed with user authentication and the foundational infrastructure is in place, but the core world-creation features are under active development with a clear build roadmap. We include it here because it represents where the architecture is going, not where it has already arrived.
Zak Data Solutions
The SDVOSB parentThe sole-proprietor consultancy that governs the family. Service-Disabled Veteran-Owned Small Business. The principal who scopes the work writes the code. Every engagement starts at the accumulated knowledge level of the practice — not from scratch.
What the family proves about our data-engineering capability.
Running multiple products on the same infrastructure is a harder test than any reference engagement. Each product forces a different engineering constraint.
Five years of research. Not a launch announcement.
The architecture behind the product family did not come from a 90-day build. It is the production crystallization of work that started long before the current AI-agent wave.
The 2021 anchor is personal research history — exploratory work, notes, and prototypes that predate the public artifacts. The publicly-verifiable record begins with the February 2025 git history.
What continuous operation produces.
Snapshot from the ayoai deployment — the longest-running instance of our framework. These are not projections; they are counts from a live system.
The industry is converging on what we already shipped.
We were building stateful agents before spring 2025 and added the continual-learning knowledge-base layer in spring 2025. Since then, the rest of the industry has been arriving at the same conclusions — piece by piece:
- Letta launched the stateful-agent infrastructure thesis (Sept 2024, $10M from Felicis).
- Anthropic published the canonical workflow-vs-agent taxonomy (Dec 2024).
- Andrej Karpathy shared his personal LLM-knowledge-base workflow (2026) and observed:
“There is room here for an incredible new product instead of a hacky collection of scripts.”— Andrej Karpathy
- OpenAI launched its Deployment Company (May 2026, $4B+, F500 focus). Jeff Clune co-founded Recursive Superintelligence the same week. Anthropic shipped Claude for Small Business.
We are not first to invent any single piece. What we shipped first is the combination as a deployment-ready product — stateful agents with a continual-learning knowledge base, at a scale that fits government and small-business buyers, not F500 enterprises.
Agents already solve problems. The open question is whether they remember — and whether you can audit them.
You don't have to take our word for whether AI agents work in production. Our own model vendor, Anthropic, documents it in its 2026 enterprise agent playbook, which opens with the line we build on:
“Generative AI answers questions. AI agents solve problems.”— Anthropic, Building Effective AI Agents (enterprise playbook, 2026)
So the real question isn't do agents work. It is do they remember, and can you audit them. Anthropic's own playbook names memory and observability as the hard requirements for agents that work at scale. That is exactly the layer we build — a continual-learning knowledge base you own, with an audit trail for every decision — sized for government and small-business buyers, not F500 enterprises.
Ready to put this in your program office?
The architecture transfers. Only the data layer changes. Whether you are a contracting officer evaluating capability or a program manager scoping a pilot, the conversation starts the same way: thirty minutes, no deck.