S
Back to Index
Systems

Activearchitecturesandclassifiedblueprints

Production-grade engines built to scale beyond their category. Each system is a self-contained architecture with its own schema, lifecycle, and purpose.

0

Active Systems

0+

Modules Shipped

0

In Production

0%

Top Completion

01

Active Systems

The engine room

Five independent systems, each solving a different category of problems. Built to compose, not couple.

01
Active

Fyboard

The productivity command center

A full-stack SaaS platform combining task management, note-taking, and workflow orchestration into a single unified system. Built with a modular engine architecture where every feature is an independent, composable unit.

SaaSProductivityFull-Stack
Completion92%
Next.jsTypeScriptPrismaPostgreSQL

Activity

8+

Modules

Modular

Architecture

02
Active

Memory Engine

Persistent context across sessions

A context management system that enables AI models to remember, retrieve, and reason across conversations. Uses vector embeddings, semantic search, and structured memory graphs to maintain continuity.

AIContextEmbeddings
Completion78%
PythonLangChainPineconeFastAPI

Activity

< 50ms

Retrieval

Infinite

Context

03
Active

Permission Architecture

Fine-grained access at scale

A role-based and attribute-based access control system that handles complex permission hierarchies. Supports team-level, resource-level, and field-level granularity with real-time enforcement.

SecurityRBACABAC
Completion85%
TypeScriptPostgreSQLRedisGraphQL

Activity

Field

Granularity

< 5ms

Latency

04
In Development

Workflow Orchestrator

Automation graphs that run themselves

A state machine and pipeline execution engine that models complex business workflows as directed graphs. Supports conditional branching, parallel execution, retry policies, and event-driven triggers.

AutomationState MachinesPipelines
Completion45%
Node.jsTypeScriptRedisBullMQ

Activity

24+

Nodes

Async

Execution

05
Research

AI Reasoning Framework

Structured thinking for models

A framework that transforms raw LLM outputs into reliable, structured decisions. Uses chain-of-thought decomposition, self-critique loops, and confidence scoring to make AI reasoning transparent and auditable.

AIReasoningLLM
Completion28%
PythonOpenAIPydanticFastAPI

Activity

+34%

Accuracy

Multi

Chains

02

Engineering Principles

How I build

01

Schema First

Every system starts with a data model. The schema defines the boundaries, the relationships, and the contracts.

02

Modular by Default

Every feature is an independent engine. Systems compose, they don't couple. Swap any part without breaking the whole.

03

Scale > Features

I'd rather ship one thing that handles 10x load than ten things that break at 2x. Infrastructure over decoration.

04

Observability Built-In

Every system has logging, metrics, and traceability baked in from day one. If you can't see it, you can't fix it.

05

Zero Magic

No hidden state, no implicit behavior. Every decision is explicit, every side effect is declared, every path is traceable.

06

Ship & Iterate

Perfect is the enemy of production. Get the core right, ship it, then refine based on real usage patterns.

I don't build features. I build engines that generate features.