AI that worksinside the business.

01

Andrea

An in-house AI initiative built for the real-estate ecosystem, connecting real-estate knowledge, intelligent search, business workflows, automation and customer interaction.

• Conversational property search grounded in live business data
• Contextual retrieval across real-estate knowledge and platform records
• CRM intelligence and lead processing
• Workflow automation triggered from conversation

02

Business-Specific AI

General-purpose assistants answer general questions. The useful version is bound to one business, its data and the way it actually operates.

• Domain knowledge represented for the business it serves
• Grounding constraints tied to information the business can stand behind
• Evaluation built from the organisation’s own cases
• Scope defined by workflow, not by capability

03

Intelligent Search

Enquiries arrive as lifestyle, location and constraint described in natural language. The work is translating that into something the business systems can resolve.

• Intent interpretation across open-ended queries
• Retrieval against domain concepts rather than keywords alone
• Conversational context carried across a session
• Results reflecting current platform state

04

Workflow Automation

An answer is not an outcome. The value sits in what happens next — the record created, the lead routed, the follow-up scheduled.

• Event-driven routines connected to business processes
• Lead capture, routing and follow-up
• Notification and messaging workflows
• Automation designed to be observable

05

CRM Intelligence

Enquiries connected to the records and workflows they belong to, so that conversation and operational data describe the same reality.

• Enquiries bound to CRM records
• Business data available to the interaction layer
• Operational context in customer-facing responses
• Integration through a mediated API surface

06

Conversational Systems

Customer interaction across messaging surfaces, with context maintained so a follow-up message is understood in relation to what came before.

• Multi-turn context handling
• Messaging platform integration
• Consistent behaviour across interaction channels
• Handover paths to people when appropriate

07

LLM Integration

Model capability is a component, not the system. The engineering is in retrieval, grounding, integration and the constraints around generation.

• Retrieval-grounded response composition
• API integration binding models to business systems
• Guardrails around what generation may assert
• Fallback behaviour when confidence is insufficient

08

Enterprise AI

Adopting AI across an organisation means deciding which processes benefit, which are unsuitable, and what evidence would settle the question.

• Process selection based on measurable benefit
• Adoption sequenced by risk and readiness
• Reusable integration patterns across use cases
• People kept in the loop where judgement is required

09

AI Security

The AI layer is treated as an untrusted client of internal systems. That keeps the security model simple as the surface grows.

• Scoped, mediated access to business data
• Input validation before requests reach downstream systems
• Permissions bounded by the acting user’s own access
• Prompts, configuration and business logic kept internal

10

AI Architecture Philosophy

The model is rarely the hard part. Integration, data access and grounding are where the value and the difficulty both sit.

• Integration over model selection
• Grounding as a requirement, not a refinement
• Business-specific evaluation over general benchmarks
• Outcomes measured in workflow, not in output quality alone