How Hazzel Automated Sales and Machine Rental Operations

!The Challenge

Customer data sat in spreadsheets and contact history, with duplicates. Rental enquiries came in by phone and email, and every offer needed the site location, dates, rental period, type of work and equipment specifications. Offers, contracts, handover and return protocols and service requests were prepared by hand.

⚡Our Solution

We implemented four automation areas in which AI prepares data and drafts, and Hazzel's team makes the decisions:

• ✓Customer base and sales support: merging data from spreadsheets and contact history, removing duplicates and categorising customers (construction contractors, installation companies, industry). New companies are found from the customer profile, and AI prepares summaries and possible use cases for the sales rep to verify.
• ✓Rental enquiries by phone and email: an AI assistant collects the site location, dates, rental period, type of work and equipment specifications. The sales rep gets a summary, a list of missing details and a task to prepare the offer in one register; machine selection and availability stay with them.
• ✓Offers and rental documents: a draft offer with customer details, period, rate, attachments and transport, and after acceptance, draft contracts and handover and return protocols from the company's templates, checked by a team member. Reminders about missing items and the end of a rental, with a task to collect or extend.
• ✓Service requests and machine records: a shared register linked to machine records. The assistant collects the machine identifier, location, description and contact details, and urgent requests go to a team member for immediate assessment. Dates from documentation are verified before reminders are set; AI does not replace diagnosis or the decision to release equipment.

📈Implementation summary

For Hazzel we implemented sales support, a single register for rental enquiries, draft offers and rental documents, and a shared service-request register linked to machine records. AI collects data and prepares summaries, drafts and reminders; the sales and service teams check them and make the decisions.