Case Study: AI SDR

This initiative originated from one of our Automation Audits conducted at [redacted], a company with €89M in annual revenue operating in the packaging production and supply sector. The analysis revealed the need to reduce the time spent by the sales and marketing teams on repetitive, low-value tasks related to prospecting, such as building lists based on contact history and ICP, enriching decision-maker data, sending hyper-personalized emails, and managing follow-ups. The goal was to eliminate time waste, improve contact quality, maximize response rates, and maintain strict control over email domain compliance and reputation.

The chatbot operates during the segmentation and outreach phases. Based on the salesperson’s input (industry, company size, materials, sustainability focus, geographic area), it generates automatic lists filtered by ICP and cleansed of already-contacted prospects or active clients. It is also valuable in the nurturing stage, allowing users to quickly query the system for insights about target companies, decision-makers, and the personalization hooks to use in emails. The main users are SDRs, account executives, and marketing teams. Target markets: Italy and Germany.

The system’s knowledge base relies on CRM data, email conversation history, sales notes, public sources, and authorized enrichment databases such as company websites, public profiles, and business registries, in addition to internal taxonomies of ICPs and buyer personas. The knowledge regarding emails and company context can be customized at any time. Sources are updated weekly, and access is restricted to the corporate network and accounts, with role-based permissions and audit trails for all activities.

Automation Description

The system uses a Retrieval Augmented Generation (RAG) approach, allowing the AI to process far more data than a simple chat-based solution with PDFs. Based on the salesperson’s criteria, the model retrieves the most relevant content, analyzes it, and produces:

• a deduplicated prospect list aligned with the ICP • enriched decision-maker profiles with key data and hypothesized pain points • hyper-personalized email drafts with contextual hooks and a coherent follow-up sequence

Guidelines are applied to prevent the sending or generation of messages outside the defined perimeter, ensuring consistency, compliance, and operational security.

The current process includes:

• defining ICP and buyer personas (target roles, company size, markets, exclusion criteria) • automatically generating lists for target segments while excluding previously managed contacts • enriching profiles through CRM and authorized data sources • creating dynamic templates for initial outreach and follow-ups

The system integrates automatic domain and inbox warm-up management, enforces daily volume limits to preserve deliverability, and staggers personalized email delivery. The follow-up cadence can be configured in days and is automatically triggered when no response is received. Conversations are monitored to classify replies and update the CRM automatically. Emails are sent through direct integration with Google Workspace.

The interface is browser-based and accessible within the organization on both mobile and desktop devices. This ensures fast adoption by sales and marketing teams and can be used by non-technical personnel without relying on unauthorized external tools. Opt-out links can be included to manage recipient preferences.

The system is installed either on-premise or on our dedicated servers, depending on the client’s choice. Data never leaves the controlled infrastructure and is not shared with non-compliant third parties.

Results

The automation has significantly reduced the time required per lead, saving approximately two hours per lead across research, nurturing, validation, outreach, and follow-up, with an average of 150 qualified leads contacted per week. The saved time has been reallocated to higher-value tasks such as discovery calls and negotiations, improving open and reply rates through relevant motivations, personalized hooks, and targeted follow-ups. Human intervention remains in place for reviewing key messages and managing negotiations, ensuring commercial control without sacrificing the efficiency of AI support.

One of the next steps is implementing a scoring pipeline that combines intent signals and domain technical fingerprints to prioritize prospects and optimize the sending order. This enables the team to feed the AI with increasingly useful data and obtain even more precise personalization suggestions.

In parallel, metrics and monitoring dashboards will be established to track open rate, reply rate, booked meetings, response time, sentiment analysis, the roles most likely to schedule meetings, and continuous refinement of the ICP, ensuring long-term quality and performance governance.