DEV Community: SCORVIA STUDIO

Your RAG is blindly trusting your vector database. Here is how we fix it.

SCORVIA STUDIO — Thu, 10 Sep 2026 10:39:46 +0000

If you’ve built a basic RAG pipeline, you know the ugly truth: you ask for the nearest documents, the system finds them, and hands them to the model as fact. The problem? "Nearest" is not the same as "right". A document can share half its vocabulary with your query, rank at the top, and answer a completely different question.

At Scorvia Studio, we build robust AI pipelines for businesses that simply cannot afford hallucinations. We just put together an interactive demo showing how we implement Corrective RAG (CRAG) to solve this exact issue: https://scorvia.studio/demos/crag/

The idea (originating from the 2024 paper by Yan et al.) is straightforward: we put a grader between the retrieval and the generator. Nothing reaches the generator unread.

Here is what the flow looks like in our implementation:

The Grader
Instead of a standard LLM prompt, we use a fine-tuned T5-large model as a grader. It assigns a relevance score from -1 to +1 to the retrieved documents. It’s not a simple binary yes/no.
The Thresholds
We set upper and lower thresholds that are tuned specifically per dataset (a demo claiming a universal "0.7 threshold" is misreading the paper).

Correct (above upper): At least one document is good. We use the local corpus.

Incorrect (below lower): The corpus is useless. We rewrite the query and do a web search.

Ambiguous (in the middle): We combine both corpus and web.

Decompose, then Recompose This is where the magic happens. Even a document that passes the threshold is still mostly noise. We cut it into strips, score each strip individually, drop the weak ones, and join the survivors back together in their original order. The generator only reads the exact sentences that actually earned their place.

The honest tradeoff:
Precision isn't free. Latency goes from roughly 400 ms to about 750 ms, and a miss adds a search round-trip on top of that. You are now making two LLM calls per query instead of one. Widening the retrieval also widens your grading bill, because the grader has to run once per document.

What you buy for that price:
Hallucinations drop to near zero, and your answers can confidently include facts published long after your index was built. What keeps CRAG in production for our clients is that it bolts seamlessly onto an existing pipeline without needing to touch or fine-tune the generator model (unlike Self-RAG).

For serious, high-stakes products, this trade is 100% worth making.

If you are a founder or an enterprise looking to build a premium AI product where accuracy isn't optional, stop settling for toy AI wrappers. At Scorvia Studio, we build retrieval systems that actually check themselves. Reach out to us, and let's get your pipeline ready for production.

Building high-performance software and AI architectures for B2B products. Here is how we operate at Scorvia Studio.

SCORVIA STUDIO — Tue, 08 Sep 2026 14:50:16 +0000

Most development work looks average because someone stopped as soon as the outcome was barely acceptable. We are Scorvia Studio, a development team focused on engineering reliable AI systems, web applications, and desktop software that goes beyond the initial brief.

We are opening our bandwidth for new B2B clients and founders who need robust architecture and zero friction. Here is exactly what we do and the technical standards we maintain.

Core Capabilities

Applied AI and Private Local Models
We build AI pipelines that minimize hallucinations. Our implementations include Corrective RAG systems that grade their own retrieved documents, cut out weak facts, and verify answers before reaching the generator. If your data is highly sensitive, we deploy private AI models running locally on your own hardware, ensuring your proprietary data never hits an external API.

Fast MVPs and Production-Ready Software
We launch functional software on aggressive, agreed-upon timelines. Our standard stack relies on TypeScript, React, Node, and Python. For native environments, we build mobile applications using React Native and lightweight desktop software utilizing Tauri and Rust, ensuring your programs run efficiently and remain operational even when the network drops.

Real-Time WebGL and High-Performance Interfaces
We build web products that act as precision instruments rather than simple brochures. We use WebGL and edge computing to render real-time graphics directly in the browser. Every interaction and motion is engineered to serve a structural purpose, and nothing moves just to fill the silence.

Custom Internal Tooling
We build internal software that consolidates operations. If your team is losing time jumping between disparate SaaS subscriptions, we engineer single, custom interfaces connected to your existing SQL databases to give you total ownership and control over your data workflow.

How We Work

Direct Engineering Access
You speak directly to the developers writing the code. There is no account management layer slowing down the communication process. A technical decision made on Tuesday is committed to the build on Tuesday.

Fixed Scope and Transparent Economics
We operate on fixed fees tied to specific project milestones mapped against a written scope. We do not bill by the hour, and we do not add surprise line items to the invoice.

Complete Code Ownership
When the project is finished, you own the source code, assets, and licenses outright. Your infrastructure and product belong to you. We do not hold anything hostage on our own servers.

We take on a strictly limited number of projects to ensure every build is pushed as far as technically possible. If you are a founder, operator, or B2B business looking for a technical partner capable of executing complex web, mobile, and AI systems, let us know what you are building. We are ready when you are.

I built a content tool where the AI runs 100% in the browser — no servers, no API keys

SCORVIA STUDIO — Fri, 05 Jun 2026 14:51:07 +0000

Most "AI content" tools send everything you write to a server. I didn't want that — so I built 24U to run the model entirely in the browser.

The idea

Paste one article, note, or voice memo → get 11 platform-native posts (LinkedIn, Instagram, TikTok, X, Reddit, YouTube, Threads, Pinterest, Telegram, WhatsApp, Facebook). Each written for that platform's style, not copy-paste.

The twist: nothing you write ever leaves your device.

How it runs locally

WebLLM + WebGPU load a quantized LLM into the browser. After a one-time ~2GB download it runs offline.
No backend inference, no API keys, no per-token cost — the "server" is the user's own GPU.
COOP/COEP headers enable SharedArrayBuffer (required for the WASM/WebGPU runtime).
ONNX/native deps are kept out of the server bundle so the Next.js function stays tiny.

Tradeoffs (honest)

Needs a WebGPU browser — Chrome/Edge today, Safari/Firefox are WIP.
That one-time 2GB download is the price of full privacy + offline use.
A small in-browser model isn't GPT-4 — but for "rewrite this for platform X" it's more than enough, and it never sees a server.

Why one-time pricing

No server compute to pay for → no subscription. You own it once.

Building 24U solo and in public — privacy-first AI that runs 100% in your browser (competitive analysis, SWOT & content from your data, nothing uploaded). I'm all-in and running lean. If it resonates, support means the world — backers get a free lifetime license 🙏 ko-fi.com/lukaszdev

Curious what the dev crowd thinks of in-browser inference as a privacy pattern — would you trust "runs locally" over "we promise we don't read your data"?

Try it: https://24-u.vercel.app/landing