Connect to the line you already have
We connect to existing machines, PLCs, gateways, ERP systems, and edge sensors without forcing a giant replacement project first.
TinyAI Studio
Operational intelligence for messy physical operations.
TinyAI enters a messy physical operation, finds a trapped source of operational leverage, and unlocks it fast, practically, and with very low adoption friction.
We work in the transition gap before traditional industries fully automate. Instead of forcing a giant new system on the client, we connect to what already exists, capture the knowledge already on the floor, and build useful intelligence people can actually use.
Why now
Traditional operations are under pressure to improve throughput, labor efficiency, and visibility now, years before full autonomy catches up.
TinyAI is not
- Not a dashboard shop.
- Not a generic AI chatbot.
Operating principle
Edge where it matters. Cloud where it helps. Delivery where people already work.
The point is not to sell “AI” to a factory. The point is to unlock leverage that was already there, trapped inside machines, routines, blind spots, and tribal knowledge.
What TinyAI Actually Does
We turn overlooked operations into something that can think back.
We are building for environments where the line is real, the machines are already installed, and nobody has time for a six-month software adoption ceremony.
Find the trapped operational leverage
We look for the hidden source of waste, friction, delay, or blindness that is already costing throughput, labor, or decisions.
Replace fragile manual workflows
The goal is not another dashboard tab. The goal is fewer blind handoffs, fewer ad-hoc workarounds, and better floor-level decisions.
Deliver intelligence where people already work
Edge when it matters. Cloud when it helps. Phone, message, alert, workflow, or operational surface, whatever the line can actually adopt.
The Transition Window
This is the five-to-seven-year gap we care about.
The most advanced AI tooling is compounding fast, but many traditional industries still run on old machines, manual coordination, and partial visibility. That gap is where TinyAI operates.
Pressure
Clients still need more throughput, better labor efficiency, and fewer surprises on the existing line.
Constraint
Most of them cannot absorb heavy enterprise implementation, giant IT projects, or behavior change for its own sake.
Opportunity
Modern AI, cheap compute, edge devices, and better tooling make small, high-leverage deployments finally realistic.
How We Work
Not a giant implementation. Not a toy pilot either.
We start with one painful problem, solve it properly, and use that foothold to build something more durable than a one-off fix.
01
Start with a real pain point
We do not begin with a platform pitch. We begin with one concrete operational problem worth solving now.
02
Use what the operation already knows
Machine data matters, but so does tribal knowledge. We capture both so the system understands what the line is actually trying to do.
03
Ship a useful system, not a science project
The standard is practical leverage with very low adoption friction, not an impressive demo that dies after the pilot.
What We Refuse To Be
TinyAI is not trying to become another old category with new words on top.
Not a dashboard shop.
Not a generic AI chatbot.
Not a sensor catalog.
Not ERP-style heavy implementation.
Contact
We are building the studio in public now.
If you run a traditional operation and there is a painful blind spot, bottleneck, or manual workflow you suspect should not still exist, we want to hear about it.
oron@tinyai.studio Based in Israel. Working from the floor outward.
Field Notes
How we think in the open.
How We Deliver Story to a Factory Agent
Retrieval is useful, but operational AI systems need more than a bag of context. In Factory Agent, we separate stable identity, tribal knowledge, bounded snapshots, conversational contracts, and living story objects so the model receives the right shape of truth for the job.
Three Runtime Cross-Fertilization: How Fast Loops, Live Conversations, and Overnight Reasoning Improve Each Other
Most AI systems pick one runtime and call it a product. We built a factory intelligence system where cheap loops, user-facing conversations, and deep offline reasoning continuously teach each other.
Why We Built a Factory MCP Instead of Letting Models Touch Raw Data
Large models are good at language, not at safely navigating factory data. We built a bounded factory MCP layer so agents ask better questions, use the right abstractions, and stop taking dangerous shortcuts.
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