Parte wanted to turn AI ambition into practical, scalable impact across its operations. Together with AE, the company embedded AI capabilities directly into everyday workflows while keeping people in control. The result is a scalable foundation that improves efficiency, supports adoption and enables continuous value creation.
01 - Context
Scaling a knowledge-intensive business through AI-enabled operations
Parte is a leading Belgian property-management platform managing more than 5,000 buildings through over 450 employees across 27 offices. Following Hg Capital's majority investment, AI became a strategic lever for accelerating growth, improving operational leverage and creating a scalable technology-enabled platform.
The operating environment is knowledge intensive. Property managers handle large volumes of emails, requests, documents and follow-up activities, while each office retains local practices and domain expertise. This created a transformation challenge that combined process redesign, platform integration, adoption and measurable value creation.
02 - Challenge
Reducing repetitive work while keeping people in control
Parte wanted to reduce the time skilled property managers spent on repetitive communication and administrative handling without replacing their judgement or disrupting the systems that support daily work. The challenge was to move quickly from opportunity identification to reliable production capabilities while maintaining user control and proving value office by office.
- High volumes of unstructured email communication and requests.
- More than 80 recurring communication and workflow types.
- A brownfield landscape centred on Properly, Parte's custom CRM.
- The need for human oversight, explainability and controlled exception handling.
- The need to measure both adoption and business value as capabilities scaled.
03 - AE's role
Turning AI ambition into measurable operational value
AE acts as Parte's transformation and delivery partner in close co-creation with Parte and with alignment to Hg Capital's value-creation objectives. The engagement combines business discovery, AI architecture, product management, engineering, data work, change management and impact measurement in one multidisciplinary setup.
The work follows a staged model: discovery, rapid prototyping and industrialisation through a dedicated Tiger Team. This creates a direct link between business priorities, technical validation and production adoption.
04 - Scope
Turning AI ambition into measurable operational value
- Discovery workshops with leadership and process owners to identify and prioritise the highest-value communication flows.
- A quantified business case covering recurring value creation, implementation effort and expected return.
- Rapid prototypes for email classification, case matching, data extraction and general question answering using real operational data.
- Buy-versus-build validation and feasibility testing before production investment.
- Industrialisation by an multidisciplinary Tiger Team.
- Office-by-office onboarding, champion activation, training and adoption tracking.
Key technologies: an MCP-based tool and data integration layer, RAG-grounded retrieval (Azure AI Search & Snowflake), a multi-provider LLM abstraction layer (LiteLLM with Azure OpenAI and Anthropic [Sonnet 5, Sonnet 4.6, Opus 4.8, Haiku 4.5]), and built-in observability and guardrails (Langfuse, OpenTelemetry). Claude Cowork is being used to build out features using the aforementioned technologies.
Capability building through delivery
AE works alongside Parte's internal stakeholders rather than delivering a detached AI product. Product decisions, workflow design, implementation and rollout are performed in close collaboration with users and platform owners. This enables operational knowledge to remain embedded in the solution and allows Parte to extend the capability as new use cases are added.
AI capabilities are integrated into the daily workflow with explicit rationales and human-in-the-loop controls. A live AI Impact Cockpit tracks quality, adoption, saved time and business value by office. This provides a shared fact base for product prioritisation, management steering and rollout decisions.
Governance, observability and operating model
The model combines frequent value releases with structured governance: two-weekly delivery increments, monthly steering and continuous user feedback. Change management is treated as part of the product, with tailored onboarding and early offices supporting subsequent rollout waves.
05 - Demonstrated impact
From early automation wins to recurring business value at scale
The AI capabilities are now live across all 27 offices, with growing adoption among property managers and a high volume of interactions handled with a low error rate. Early automation flows are already reducing processing and resolution times, freeing up significant capacity for higher-value work. Just as importantly, Parte now has a scalable foundation for continuous improvement: every new workflow that is validated and brought into production builds on the existing platform and extends the measurable operational impact.
“At Hg, we work with AE on the AI transformation of one of our portfolio companies. Their pragmatic approach with a focus on concrete results appealed to us right away. The team combines technical expertise with a strong understanding of real-world impact. Communication is clear, and the team actively thinks along when priorities shift. Enthusiastic, sharp, and a great partner to work with.”
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