Intelligence, woven in.

URDNAI helps business owners, executives, professionals, and real estate operators turn AI from scattered tools into structured, governed, useful capability.

Signal - Intelligence, woven in.

URDNAI helps businesses turn AI from scattered tools into structured capability.

Understand - Map the work first.

We identify workflows, risks, knowledge, and high-value opportunities before choosing tools.

Design - Agents need structure.

We design the roles, data access, controls, and handoffs that make AI useful in real operations.

Build - Capability becomes software.

We build agents, integrations, and custom intelligent systems around the way your business works.

Enable - People stay in control.

Training and enablement help teams use AI confidently, consistently, and safely.

Secure - Trust is designed in.

Security, compliance, and governance are built into the system rather than added after launch.

Evolve - A system that compounds.

Local LLMs, private knowledge systems, and measured improvement let capability grow over time.

AI services

URDNAI provides AI strategy, autonomous agent development, AI implementation, staff training, governance, compliance, security audit, custom intelligent software, and local LLM systems.

Governance, security, and compliance

AI systems need controls, review paths, data boundaries, access rules, audit trails, and operating policies before they are trusted inside a real business.

Custom intelligent software

URDNAI builds AI capability into workflows, CRMs, documents, knowledge bases, intake systems, reporting loops, and private business software.

First engagement - AI systems audit

The first engagement is a one-week AI systems audit, priced by proposal, that maps current workflows, AI usage, risk exposure, data access, automation opportunities, and practical next steps before a business commits to deeper implementation.

Audit deliverables

Clients leave with the right delivery format for the engagement: a report, workshop, implementation roadmap, Loom or video walkthrough, live strategy call, or a combination of those formats.

Founder - Luke Soanes

URDNAI is led by Luke Soanes, founder, with 10 years of experience building automations and AI systems, advanced AI development and deployment experience, AI security accreditation through the Global AI Certification Council, and private client work across agentic infrastructure, real estate AI deployments, AI software projects, training, and consulting.

Systems in practice - Real estate

A representative real estate system pattern includes systems and process automation, data capture, tailored agentic outreach, sales and marketing workflow streamlining, CRM integration with AI agents, and custom software to reduce human error and wait times.

Lead intake fields

Before a call, useful lead context includes the business type, the contact role, the workflow or process under pressure, systems involved, data or compliance constraints, existing AI usage, and the outcome the business wants in the next 90 days.

Frequently asked questions

URDNAI helps businesses decide when to use existing AI tools, when to build custom agents or software, how to govern access safely, and whether local or private AI systems are appropriate.

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Services

AI capability for real operations.

URDNAI helps businesses move from scattered AI experiments to governed systems, trained teams, and software that fits the way work actually happens.

01

AI strategy and implementation

Map workflows, identify valuable use cases, choose the right architecture, and implement AI without losing control of the business context.

Explore AI implementation
02

Autonomous agents for business

Design and build agents with clear roles, data access, escalation rules, memory, evaluation, and human handoff points.

Explore agent systems
03

Governance, security, and compliance

Audit AI systems, define controls, manage risk, document usage, and build compliance into the operating model from the start.

Explore governance
04

Custom intelligent software

Integrate AI into CRMs, intake flows, reporting systems, knowledge bases, documents, and internal tools that need intelligence built in.

Explore custom software
05

Staff training and enablement

Train teams to use AI safely and practically, with playbooks, workshops, operating rules, and repeatable workflows.

Explore staff training
06

Local LLM and private AI systems

Plan private knowledge systems, local model workflows, secure retrieval, and business-owned intelligence that can compound over time.

Explore private AI
First engagement

Start with an AI systems audit.

A focused first engagement gives leadership a clear picture of where AI can help, where it can create risk, and what should be built first.

Founder

Built by an AI systems operator.

Luke Soanes Founder, URDNAI

Luke works across advanced AI development and deployment, with a focus on agentic infrastructure, practical business automation, and secure implementation inside real operating environments.

  • 10 years

    Experience building automations, AI systems, and business software that turns repeatable work into governed capability.

  • Security

    AI security accreditation through the Global AI Certification Council, with governance and risk treated as part of the build.

  • Deployment

    Private client work across agentic infrastructure, real estate AI deployments, AI software projects, training, and consulting.

Read about Luke
Systems in practice

Anonymised example: real estate operations.

Client names are withheld, but the system pattern is clear: remove manual drag, connect the data, and give teams faster, more reliable operating loops.

Real Estate

AI agents and custom software integrated into the sales, marketing, CRM, and client-response workflow.

  1. Capture

    Structured intake and data capture so enquiries, client signals, property context, and follow-up details are not lost in manual handoffs.

  2. Outreach

    Tailored agentic outreach that can prepare next actions, draft communication, and help teams respond with better context.

  3. CRM

    CRM-connected AI agents that support cleaner records, workflow triggers, sales visibility, and consistent follow-through.

  4. Outcome

    Reduced human error, shorter wait times, and smoother sales and marketing processes with intelligence integrated into the operating system.

Explore real estate AI View systems in practice
Credibility

Evidence before implementation.

Deliverable 01

Opportunity register

A ranked view of the workflows, decisions, documents, and customer moments where AI could create measurable leverage.

Deliverable 02

Risk and governance notes

A practical record of data boundaries, approval requirements, compliance exposure, security concerns, and staff-use controls.

Deliverable 03

System architecture direction

Recommended agents, integrations, knowledge sources, review loops, and human handoff points for the first useful system.

Deliverable 04

Implementation sequence

A staged path that separates quick wins from deeper software work, so the business can move without guessing.

Operating principles

  • Do not automate work that has not been understood.
  • Keep people in control where decisions carry risk.
  • Design data access before agent capability.
  • Measure outputs before trusting scale.
Method

Structured before automated.

  1. Understand

    Map the work, data, risk, people, and decision points before choosing tools.

  2. Design

    Define the agent roles, controls, knowledge access, integrations, and review loops.

  3. Build

    Implement software and AI systems around the existing business rhythm.

  4. Govern

    Measure quality, document use, train staff, and keep the system accountable.

Governance

Trust is an operating layer.

AI adoption creates new questions for leadership: what data can systems access, when should people intervene, how are outputs reviewed, and what evidence exists when something matters.

URDNAI treats governance, security, and compliance as part of the design brief. The goal is not just smarter software, but systems a business can explain, monitor, and improve.

Read governance approach
Questions

Useful answers before the first call.

Do we need custom AI software or can we use existing tools?

That depends on the workflow. Some businesses need better tool use and staff practices; others need custom agents, integrations, private knowledge systems, or software that carries business context safely.

Can autonomous agents safely access business systems?

Yes, but only with designed boundaries: permission scopes, logs, review paths, escalation rules, and human handoff points. An agent should not get broad access just because it can be useful.

How do you handle AI governance and compliance?

Governance is built into the operating model. We review data boundaries, approvals, staff usage, audit trails, risk exposure, and the rules that decide when AI assists versus when people decide.

When should we consider local LLMs or private AI?

Local or private AI systems become relevant when data control, privacy, latency, ownership, or internal knowledge access matters more than using general public tools.

What is the best first step?

Start with an AI systems audit. It gives the business a practical view of opportunities, risks, governance gaps, and what should be implemented first.

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Start with context, not a sales script.

The first conversation is for understanding the business, the pressure points, and whether an AI systems audit is the right first move.

Useful context

Send the shape of the work first. The call is sharper when the pressure, systems, and risk are visible.

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Start

Bring the work. We will map the intelligence.

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