INTERNAL LABS & PROVING GROUNDS

We Build Products for Ourselves Before Anyone Else. This is where our ideas take shape.

rd_snapshot.sys — futura:labs
3
tools in development
~40
internal hours saved monthly
0
legacy systems tolerated

LAB-TO-LIVE PIPELINE

01

We Build to Solve Our Own Pain Points

02

If It Proves Useful Internally, It Graduates

03

Partners Start Ahead, Not From Zero

FEATURED VENTURE

FUTURA TIMESYNC

IN PROGRESS — INTERNAL ALPHA
The Problem:

Scheduling across distributed teams is chaos. Calendar fragmentation, timezone juggling, and async coordination gaps kill productivity and momentum.

The Solution:

An AI-powered scheduling protocol that learns team patterns, respects focus time, and automatically optimizes meeting windows. The generative scheduling layer for distributed teams.

Current Tech Stack (Draft):
[Next.js][Postgres][OpenAI GPT-4o][Google Calendar API][Vercel]
Milestones:

Internal dogfooding complete. Core scheduling engine validated. Beta invite system in development. External launch targeting Q1.

ACTIVE PORTFOLIO

AuroraSpec

EXPERIMENTAL

AI-powered specification writer. Feed it a product idea, get back structured PRDs, user stories, and technical requirements. Built to eliminate the "blank page" problem in product planning.

Stack:

OpenAI GPT-4o, Next.js, Markdown, Vercel AI SDK

Pulseboard

IN PROGRESS

Real-time health dashboard for distributed systems. Aggregates logs, metrics, and uptime across all our internal tools into a single command-center view. No more tab-switching to check if things are on fire.

Stack:

Next.js, WebSockets, Postgres, Grafana APIs

LaunchPad Kit

INITIAL SETUP

Our internal starter template for spinning up new ventures fast. Pre-configured auth, database, deployment pipelines, and design system. The foundation we use for every new project, packaged for reuse.

Stack:

Next.js, Tailwind, Prisma, Clerk, Vercel

THE GRAVEYARD

X

GhostAgent

DEPRECATED
Why It Died:

Attempted to build an autonomous AI agent for client communication and task management. Hallucination rates were unacceptable for production use—clients received inaccurate project updates and incorrect scheduling information. The trust cost was too high.

What We Learned:

AI agents need human-in-the-loop for any client-facing communication. We now use AI for drafting and suggestions, but humans review and send. This lesson directly influenced how we built TimeSync—AI assists, humans decide.

TECH TRANSFER MODE

Partners who work with us don't start from scratch. They inherit battle-tested infrastructure from our internal ventures.

Pre-tested Auth

Authentication patterns validated across multiple projects. Clerk, NextAuth, custom JWT—we've shipped them all.

AI Patterns

Prompt engineering, RAG pipelines, agent architectures. Patterns refined through real production use.

Deployment Blueprints

CI/CD, preview environments, monitoring. Infrastructure-as-code templates ready to clone.