AI DEPLOYMENT / R&D · DIIA.CITY RESIDENT

EONYX is an R&D studiohelping you integrate AI into your business processes.

Applied AI deployment for real-sector B2B. We turn operational expertise into workflows, and deploy controlled AI systems on real data.

11.12.25
founded
27.02.26
Diia.City resident
3
paid deployments
1
live business polygon: ALTACO
Who it's for

Where data complexity
hits the money.

Segments
  • 01Distributors of building & finishing materials
  • 02Companies with complex product catalogs
  • 03Importers and exporters
  • 04Manufacturers and suppliers with B2B sales
  • 05Teams where managers build quotes and reconcile stock by hand
Pain we remove
  • Manual preparation of commercial offers
  • Errors in prices, stock levels and product naming
  • Chaos in supplier data: many formats, no structure
  • Loss of expertise when managers leave
  • Long staff onboarding
  • No single source of truth

The value is moving,
from models to deployment.

The global AI market is at an inflection point: value is shifting from the models themselves to embedding them in real business processes. This isn't our interpretation. It's the move of the industry's largest players.

OPENAI · 11.05.2026

Launched a separate AI-deployment company with over $4B in investment, embedding engineers directly inside client organizations to redesign their processes around AI.

ANTHROPIC · 04.05.2026

With Blackstone, Hellman & Friedman and Goldman Sachs, announced an enterprise-services firm where applied engineers work side by side with the client's team.

88%
use AI in at least one function
23%
scale agentic systems
McKinsey · State of AI 2025

When leaders with billion-dollar budgets move exactly this way, it confirms the direction. The market's core problem isn't model capability. It's the gap between what a system can do in an experiment and what a business can safely put into production. That gap is what EONYX addresses.

We deliberately don’t promise an “autonomous system that replaces people”. Our principle is supervised autonomy: AI acts, but through rules, logs, access rights, result checks and human sign-off at the critical points. An AI that names a price or a stock level “roughly right” isn’t innovation. In real business it’s a source of losses. Removing that risk is our value.

A.
Supervised autonomy
AI acts, but through rules, logs, access rights and human confirmation at critical points. Not a weakness; the maturity most deployments still lack.
B.
A real polygon, not a vacuum
Every hypothesis is tested on ALTACO: real goods, clients, stock levels, prices and documents, not on demo data. Most AI startups don't have this environment. We do.
C.
The AI leaves a trail
Where it took the data, what it proposed, who confirmed. It never builds an offer without a source, and never acts with a financial or legal consequence without permission.
How we work

We preserve expertise.
We build agents.
We build them into your process.

01

Audit

TrackAdvisory

First, diagnosis. A map of the processes, priority scenarios, a data and risk assessment, and an MVP plan. You learn where AI actually moves money, before a single line of code.

  • Process & bottleneck map
  • Priority scenarios
  • Data & risk assessment

Ways to start
right now.

Not a finished product line with a price list. These are ways to get into the work, each building on what we've already done.

01

AI Audit & Deployment Roadmap

ForCompanies that want to see where AI actually pays off.
ResultA process map, priority scenarios, a data assessment, risks and an MVP plan.
02

Custom AI Agent MVP

ForCompanies with one clear process worth automating first.
ResultOne applied agent: price monitoring, buyer search, data collection and normalization.
03

B2B Knowledge Base + Assistant

ForCompanies where knowledge is scattered across people, files and chats.
ResultA structured knowledge base plus an AI assistant for managers and onboarding.
04

Market / Price Intelligence

ForCompanies that need a regular view of the market and competitor prices.
ResultRecurring collection, comparison and structuring of external data: prices, SKUs, competitors.

First nodes.
Not yet orchestrated.

What already runs isn’t a random portfolio. It’s the first nodes of the target architecture. Three of them are variations of one class of task: autonomous collection, normalization and structuring of external data.

eonyx · agent-trace · price
intentCollect and normalize building-material prices; structure them for comparison.
01FETCHsources(suppliers=12, fmt=[xlsx, pdf, html])
02PARSE→ price · sku · unit · supplier · date
03NORMALIZEunits(м², шт, т, м³) → canonical
04DEDUP3,418 rows → 2,902 sku · 0 collisions
05COMPAREvs last_run → 184 changed · 9 new
06DONEstructured · 0 unverified prices
01
EONYX · first system nodesRUNNING

Shipped. Paid.

REAL ESTATE

AI advisory for a private entrepreneur

Assessment of where AI applies in the business. A paid engagement, not a demo.

CONSTRUCTION

Price-monitoring system for a building company

Regular collection and structuring of competitors' building-material prices.

STONE EXPORT

Buyer-search system for an exporter

Autonomous search for potential B2B buyers of stone products.

ALTACO · CUSTOMER ZERO

Domain knowledge base + content pipeline

Supplier photos, texts and technical data; LLM-driven content for ALTACO comms.

Start

Let's start
with an audit.

Tell us about the process you need to fix. If there's an agent worth building, the audit will show it, and you can stop at any step. Each format below builds on work we've already done.

  • AI Audit & Deployment Roadmap
  • Custom AI Agent MVP
  • B2B Knowledge Base + Price Intelligence
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