Fuelling Business Transformation With Intelligent

We build AI systems that extract, preserve, and operationalise the institutional knowledge trapped in your legacy infrastructure - so your teams act on it in seconds, not lose it when someone leaves.
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Whether building on our platform or engineering custom solutions, we design AI systems with governance built in - not bolted on later.

70%

Unlock up to 70% of your IT budget

12 months

Guaranteed return on investment within 12 months

Our Services

THE DEVELOPMENT BOTTLENECK

Are you trapped in the POC-to-Production death valley?

Every CTO and IT leader faces these challenges - most accept them as inevitable

The Frankenstack nightmare

Six Brittle Connections. One Intelligent Orchestration Layer.


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Traditional Frankenstack requires managing at least six different APIs, data formats, authentication methods, and error handling approaches. When one system changes its API or goes down, it can break the entire chain. Uranion designs and engineers a unified orchestration layer that handles the integration complexity - built around your existing stack.

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The POC-to-Production gap

Your Demo Works. Your Production System Doesn't Exist.

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You built a proof-of-concept and the demo went great. But add authentication, enterprise data volumes, role-based permissions and your existing database - and the architecture falls apart. We build production-grade systems from the ground up, so nothing becomes a throwaway.

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The AI talent crisis

You Can't Afford the Talent. You Can't Lose the Talent You Have.

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AI specialists command $150K-$200K salaries, when you can even find them. Recruiting takes 6-9 months. When you finally hire someone, they become a single point of failure. If they leave, your entire AI strategy walks out the door with them. Your company can't justify $800K annually for a five-person development team just to stay competitive.
This is exactly the kind of risk our engineering team removes.

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Sound familiar? You're not alone

we've built solutions for this, in two ways

CUSTOM ENGINEERING
For organizations that need fully engineered solutions.

We design and build production AI systems from the ground up: legacy extraction, visual search, compliance automation, cloud infrastructure.

Schedule a Consultation

PLATFORM
For teams who want to build and own their AI workflows.

Use our low-code builder with visual nodes, state preservation, and automatic documentation. Deploy in weeks without DevOps overhead.

See How It Works

SOLVES: The POC-to-Production gap

From idea to production in mere weeks

Guided Creation with AI

Describe what you want to build in natural language or choose from templates (CRM, RAG system, data pipeline). The AI suggests data models, nodes, and relationships. No blank-page anxiety—intelligent starting point in minutes.

Build with Visual Nodes

Connect nodes representing your data pipeline—API inputs, AI processing, transformations, outputs. As data flows through, Uranion performs stateful historization, preserving state at every step. This creates your audit trail automatically.

Configure, Don't Code

Set parameters through visual controls. Describe AI operations in natural language. Platform handles authentication, error handling, cost optimization, infrastructure. Write custom code when needed in dedicated nodes—low-code, not no-code.

Deploy Production-Ready

Test with real data. See intermediate states at every node. Deploy with built-in infrastructure—no CI/CD configuration, no DevOps headaches. The result: complete application with workflows, AI, interfaces, integrations, and automatic documentation.

SUCCESS STORIES

A glimpse of our solutions

AI Native Project Generation

Beyond the demo, in production.

A live multi-agent AI sales engine that turns a passive catalog into a personalized client journey - not a proof-of-concept.

Ask the Archive

30 years of sales

knowledge locked in one person's email archive. Junior team had no access, creating dangerous dependency.

AI-Scored Supplier Selection

Traditional keyword-based

Product catalog couldn't handle extensive trade show booth inventory.

More SUCCESS STORIES

Real Poblems. engineered Outcomes.

The same challenges above - solved for real companies across EMEA.

View Case Studies

SUCCESS STORIES

Real Poblems. ered Outcomes.

The same challenges above - solved for real companies across EMEA.

C&C Group

The talent crisis, solved.

200,000 emails turned into one queryable knowledge base - critical know-how no longer locked to a single senior resource.
Salon Image

Arrital

Legacy complexity, solved.

70% on subsequent migration costs, with decades of business rules turned into a reusable, AI-ready asset.
Face Makeup

Henoto

Beyond the demo, in production.

A live multi-agent AI sales engine that turns a passive catalog into a personalized client journey, not a proof-of-concept.
Make up Foundation

View Case Studies

WHAT YOU CAN BUILD

Proven uses of data-Intensive, AI-Powered applications across industries

Data Pipelines & Integration
Problem: Disconnected systems require fragile ETL scripts and constant maintenance when business rules change.
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Mechanism: Connect disparate sources, transform data, enrich with AI analysis—all with preserved state at every step.
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Outcome: Nightly sales aggregation with AI anomaly detection and automated reporting—changes take minutes, not development sprints..
Business Logic Migration
Problem: Legacy systems hold critical rules hostage; manual extraction takes months and frequently fails.
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Mechanism: Extract complex business rules into AI-ready knowledge bases that preserve intent and accelerate modernization.

Outcome: Manufacturing company migrates 30-year-old pricing engine—achieves proven cost reduction on migrations.
Intelligent Process Automation
Problem: Rule-based automation breaks when content changes; humans still route most exceptions manually.
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Mechanism: Automate with AI decision-making and dynamic routing based on semantic content analysis.
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Outcome: Customer emails automatically categorized, routed, with drafted responses—handles exceptions without manual rules updates..
RAG Systems & Knowledge Bases
Problem: Institutional knowledge trapped in emails, documents, and departing employees' heads.

Mechanism: Transform decades of documents into intelligent, searchable systems with natural language queries.

Outcome: Sales team queries 30 years of proposals and pricing history instantly—knowledge survives turnover.
Custom CRM & ERP Systems
Problem: SaaS platforms force workflows to conform; custom development takes years and costs millions.
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Mechanism: Build bespoke systems tailored to unique processes without SaaS limitations or traditional dev timelines.
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Outcome: Luxury real estate CRM with specialized features for high-net-worth transactions—built in weeks, not years..
Semantic Search & Product Discovery
Problem: Customers can't find products using conceptual queries; keyword search misses intent.
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Mechanism: Enable natural language and conceptual search—not just keyword matches.
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Outcome: Trade show catalog where users search by design style or use case—discovery becomes intuitive..
Data Pipelines & Integration
Problem: Disconnected systems require fragile ETL scripts and constant maintenance when business rules change.
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Mechanism: Connect disparate sources, transform data, enrich with AI analysis—all with preserved state at every step.
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Outcome: Nightly sales aggregation with AI anomaly detection and automated reporting—changes take minutes, not development sprints..
Business Logic Migration
Problem: Legacy systems hold critical rules hostage; manual extraction takes months and frequently fails.
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Mechanism: Extract complex business rules into AI-ready knowledge bases that preserve intent and accelerate modernization.

Outcome: Manufacturing company migrates 30-year-old pricing engine—achieves proven cost reduction on migrations.
Intelligent Process Automation
Problem: Rule-based automation breaks when content changes; humans still route most exceptions manually.
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Mechanism: Automate with AI decision-making and dynamic routing based on semantic content analysis.
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Outcome: Customer emails automatically categorized, routed, with drafted responses—handles exceptions without manual rules updates..
RAG Systems & Knowledge Bases
Problem: Institutional knowledge trapped in emails, documents, and departing employees' heads.

Mechanism: Transform decades of documents into intelligent, searchable systems with natural language queries.

Outcome: Sales team queries 30 years of proposals and pricing history instantly—knowledge survives turnover.
Custom CRM & ERP Systems
Problem: SaaS platforms force workflows to conform; custom development takes years and costs millions.
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Mechanism: Build bespoke systems tailored to unique processes without SaaS limitations or traditional dev timelines.
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Outcome: Luxury real estate CRM with specialized features for high-net-worth transactions—built in weeks, not years..
Semantic Search & Product Discovery
Problem: Customers can't find products using conceptual queries; keyword search misses intent.
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Mechanism: Enable natural language and conceptual search—not just keyword matches.
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Outcome: Trade show catalog where users search by design style or use case—discovery becomes intuitive..

cONTACT Us

Ready to turn your challenges into opportunities?

Whether it's a 30-year-old pricing engine, 200,000 emails of sales knowledge, or a legacy system blocking your roadmap - we've solved each of these before. Let's talk about yours.

Schedule a Consultation

Email us
info@uranion.ai
Phone number
🇬🇧 UK: +44 7956 947848
🇬🇧 UK:+44 20 4634 0606
🇮🇹 Italy: +39 06 8384 5463
Office address
71-75 Shelton Street, Covent Garden,London, WC2H 9JQ, United Kingdom
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What They Deliver:
Powerful but complex platforms requiring 6-12 month implementations with specialized consultants. Expensive licensing with unpredictable cost scaling. AI capabilities added recently feel like bolt-ons, not native architecture. Customization requires platform-specific expertise. Vendor lock-in through proprietary architectures and data models.
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What Uranion Delivers:
✓ 2-3 week deployment — production-ready without consultant dependency
✓ Natural language configuration — not proprietary scripting languages requiring specialized training
✓ Modern AI-native architecture — not AI retrofitted onto legacy platforms
✓ Transparent pricing — predictable costs, not enterprise licensing negotiation
What They Deliver:
UI builders optimized for interface construction. Data integration added as afterthought—feels like plugins, not architecture. AI capabilities retrofitted onto platforms designed before transformer era. Complex data transformations require workarounds or custom code. Enterprise requirements (granular permissions, SSO, multi-tenancy) are additional modules, not foundation.
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What Uranion Delivers:
✓ Integration-first foundation — data pipelines are architectural core, not add-ons
✓ AI-native from inception — embedding, RAG, semantic search built-in with cost optimization
✓ Enterprise features included — SSO, RBAC, audit logs, multi-tenancy from day one
✓ Stateful data layer — complete transformation history enables debugging impossible in UI-first tools
What They Deliver:
Workflow automation with stateless architecture. Data flows through transformations and disappears—no audit trail, no preserved intermediate states, no time-travel debugging. When workflows break, you have output errors but no visibility into which transformation failed or why. Building anything beyond simple automations requires external databases and custom code.
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What Uranion Delivers:
‍✓ State preservation at every node — complete audit trail without manual logging
✓ Time-travel debugging — inspect data at any transformation point, at any time
✓ Incremental reprocessing — 85-95% cost reduction when business logic changes
✓ Application platform, not just automation — build full apps with dashboards querying workflow state
Workflow A – Customer Analytics

What this workflow does
✓ This workflow takes raw customer data and turns it into clear insights about how valuable each customer is over time.
✓ It starts by pulling in customer records, checks that email addresses are valid, cleans up duplicates so each person has one profile, then calculates customer lifetime value and sends the results to dashboards.
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Why the shared steps matter
✓ Most steps here (data ingestion, validation, deduplication, and scoring) are the same building blocks used in other workflows like Inventory, Finance, and Marketing.
✓ Only the final “Export to dashboards” step is unique, which means improvements to core logic instantly benefit several teams, not just Analytics.
Workflow B – Inventory Management

What this workflow does
✓ This workflow helps keep the right products in stock by tracking items, checking codes, and deciding when to reorder.
✓ It pulls in product and stock data, checks that product IDs are correct, uses a shared calculation engine to decide when stock is low, then sends alerts to suppliers and creates purchase orders when needed.
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Why the shared steps matter
✓ The way data is ingested, validated, and scored is shared with Customer Analytics and Finance, so everyone works from the same “source of truth.”
✓ Only the supplier alerts and purchase order steps are inventory‑specific, which keeps the system flexible but consistent across teams.
Workflow c – Financial

What this workflow does
✓ This workflow turns raw transaction logs into reliable financial results and reports.
✓ It brings in transaction data, checks that amounts look correct, matches each transaction to the right account, calculates profit, then adds tax, regulatory reports, and executive dashboards on top.
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Why the shared steps matter
✓ The first four steps (data ingestion, validation, matching, and profit logic) are shared with Customer Analytics and Inventory, so numbers line up across the business.
✓ Only the tax, regulatory, and executive‑reporting steps are special to Finance, which makes updates easier and reduces the risk of inconsistencies.
Workflow d – Marketing

What this workflow does
✓ This workflow helps Marketing target the right people with the right messages across email, social, and ads.
✓ It starts by checking contact data and consent, merges records so each person has a single profile, scores leads to see who is most likely to convert, then runs email campaigns, social media activity, and retargeting.
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Why the shared steps matter
✓ Validation, merging, and scoring reuse the same logic as Customer Analytics, Inventory, and Finance, so Marketing is working with the same clean, scored profiles as everyone else.
✓ Campaign execution steps (email, social, retargeting) are Marketing‑only, but they sit on top of shared data and scoring, which is what makes the whole system feel coordinated instead of siloed.