Solutions
Business-ready use cases
Agents for observability and security
Document Intelligence
Structure and validate documents with AI.
Data + AI Operations
Data + AI Management · Real time data processing
Enterprise AI with full control and privacy
We help technology architects and innovation leaders deploy private AI and data solutions quickly, securely and reliably.
- ISO 27001Certified information security
- +15 yearsDeploying enterprise platforms
- 24x7 opsContinuous support and monitoring
Clients and partners who trust us
The challenge
Real challenges in adopting enterprise AI
Insufficient, dirty and disconnected data
Pressure to show fast results without failing
Protecting sensitive information and regulatory compliance
Unstructured data and documents. Manual processes.
Legacy architectures that slow new initiatives
Stack and vendors without private-AI expertise
Adopting AI should never put your information at risk
At Urdaten Richit we understand these challenges and know how to help.
Track record
Our numbers speak for themselves
- +15 years of experience
- +1T records processed
- ISO 27001
- 24x7 ops
Our portfolio
Platforms
Engines that run the AI
Search, RAG, agents, observability and security with AI
LLM, private agents
Data flows
Real-time data ingestion and enrichment
Infrastructure
The compute foundation
AI infrastructure
AI Factory & AI Data Platform
Private AI, local AI
Agent platform
Cloud infrastructure
The path forward
Deploying enterprise AI doesn’t have to be complicated
Three steps to go from vision to operation.
- STEP 1
Book a discovery session
We review your goals, data, infrastructure and risks.
- STEP 2
We design a reliable solution
We build a secure, scalable architecture aligned to your business.
- STEP 3
We deploy and support you
We deploy, optimize, train your team and provide ongoing support.
What our clients say
Read what organizations that trusted Urdaten Richit with their data and private-AI projects have to say.
You have the vision. We have the roadmap.
Leave us your details and book a discovery session. We review your goals, data and infrastructure, and propose the safest path forward.
- Response in under 24 hours
- No commitment
- Talk directly with specialists
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Frequently asked questions
What companies ask us
Which regions do you operate in?
The United States from our offices in Austin, Texas, and Latin America from our offices in Mexico City.
How can a company deploy AI without exposing its confidential information?
The key is on-premises or hybrid deployments. AI runs inside the organization’s own infrastructure, without sending data to public clouds. This requires an architecture designed from the start with privacy as a priority, not as an add-on layer. At Urdaten Richit we design and deploy private-AI platforms using technologies like NVIDIA, Elastic and Dell, ensuring sensitive data never leaves the organization’s perimeter.
What is private AI and why does it matter for enterprises?
Private AI operates entirely within the company’s own infrastructure, without relying on external APIs or public-cloud models. For enterprise organizations, especially in sectors like finance or government, this is critical: it guarantees full control over data, regulatory compliance and the elimination of sensitive-information risk.
What certifications should an enterprise AI provider have to guarantee security?
The minimum standard for projects with sensitive data is ISO 27001 certification, which validates the provider’s information-security controls. It’s also worth verifying that the team has certified engineers on the platforms they will deploy (Elastic, NVIDIA) or the project’s specific technologies. Urdaten Richit holds ISO 27001 certification and has certified engineers on the leading enterprise AI and data platforms.
What is the difference between a cloud AI deployment and an on-premise one?
A public-cloud deployment is faster to start but means data leaves the organization’s perimeter and is subject to the cloud provider’s policies. An on-premise deployment keeps everything within your own infrastructure, which means greater control, greater privacy and generally lower operating cost over the long term. Hybrid deployments combine both, depending on the data type and workload.
How do platforms like Elastic and NVIDIA fit into an enterprise AI architecture?
Elastic is used mainly for semantic search, unstructured-data analysis and observability. It’s the engine that lets AI find and process relevant information within the organization’s systems. NVIDIA provides the accelerated-compute infrastructure needed to run language and vision models efficiently on-premise. Together they form the technical foundation of most enterprise private-AI projects.