PIES Studio

31/07/2026

CIOs don’t say no to AI. They say no to losing control of their data to get it.

PIES Studio’s Private LLM removes that trade-off. Every application built on the platform runs its AI inside your environment, under your governance, nothing leaves to train someone else’s model.

AI built in, not bolted on. The speed your business wants, with the control you’re accountable for.

Let’s grab a coffee and talk it through → www.pies.io

28/07/2026

Your business still has problems software should’ve solved by now.
The issue? Delivery is too slow.

PIES Studio fixes that. 💡
We turn real business problems into working software in days, not months.

Built with private AI, running in your environment.
No data leaks. No training someone else’s model.

You own it. We run it. It keeps delivering.
Enquire now for a demo! 📲

30/06/2026

Two weeks ago, a US directive forced two frontier models offline overnight. They're still dark today, with no restoration date.

If you'd wired production workflows to Fable 5, your engine vanished in an afternoon. The suspension landed June 12. Teams that had second-sourced kept shipping while everyone else scrambled.

I've been writing software long before I was a CTO, and this is the kind of failure mode that's invisible right up until it isn't. We benchmark models obsessively and skip the more basic question: what happens to everything you built if the model goes away?

It's not hypothetical anymore. Production AI agents are being rolled back at high rates, with PII exposure and hallucination leading the causes. The consensus forming this month across an academic institution (MIT Sloan), an infrastructure vendor (Databricks), and a security standards body (OWASP) is the same: enterprises have been treating agentic AI as a software deployment problem when it's a control problem.

That's the wrong layer to be fighting at. From the engineering side, three things this episode reinforced for me:

If you don't own inference, you don't own continuity. A model you run inside your own network can't be switched off by a directive, a price change, or an API deprecation. We built PIES with a private LLM family (3B to 400B) that runs entirely on your hardware. No cloud calls at inference, no data leaving the network.

Governance has to be enforced in the request path, not written in a PDF. In PIES Studio, every AI call (app generation, in-app AI, agent calls) passes through a governance router before it reaches any model. The caller doesn't pick the model; admin-defined policy does, matched on data classification, role, region, and time window, with a per-call audit record. AI as a risk becomes AI as a control.

Portability beats lock-in. You describe the app; the platform produces standard, production-grade source you fully own and can run anywhere: cloud, Kubernetes, or fully air-gapped. No rewrite, no off-switch you don't hold.

The teams that win the next 18 months won't have the flashiest model. They'll be the ones who can't be turned off.

https://pies.io/

Call now to connect with business.

04/02/2026

At PIES, AI isn’t something we’ve added on later — the platform was built for AI from day one, not retrofitted after the fact.
Most platforms didn’t start with AI in mind — they’ve added it later. A chatbot here, a code assistant there, maybe a “generate” button bolted onto an existing workflow.

It matters, because AI is only as powerful as the data and systems it’s connected to. If your workflows, backend, and data pipelines aren’t designed for AI, then AI can only ever operate at the surface level.