AI-Driven Pipeline Reactivation

The Challenge

Most B2B companies are sitting on a pipeline problem they have stopped thinking about. Thousands of leads that never converted, deals that stalled at proposal stage, prospects that went cold after an initial call, all of it sitting in the CRM, classified as lost and ignored. Re-engaging this population manually is uneconomic: the research required to personalise outreach at scale, the time needed to write individual messages, the coordination to manage multi-touch sequences across a large contact list.

The standard answer is to write these contacts off. But the economics of lead reactivation are fundamentally different from new lead generation. These contacts already know the company. Some of them are back in market. Many of them have changed roles or companies since the original contact, which means the buying context has shifted. An automated enrichment and re-engagement system can work this population at a scale and personalisation level that manual outreach cannot match.

The brief was to build that system: a complete pipeline reactivation workflow that could ingest a dormant contact list, enrich each record, identify the highest-priority re-engagement targets, and execute personalised outreach sequences without human intervention at the volume stage.

Our Approach

We built the system as a LangGraph workflow with seven discrete stages, each owning a specific part of the pipeline. Ingest handles CRM export parsing and normalisation across different data formats. Enrich pulls from twelve-plus sources: LinkedIn profile data, company news and announcements, job postings, funding events, executive changes, and sector indicators. Segment scores and classifies the enriched records by re-engagement priority, surfacing contacts where buying signals are present.

The personalisation stage is where the investment in enrichment pays off. Every outreach message is generated individually, drawing on the specific enrichment data for that contact: their current role, recent company activity, relevant trigger events, and the context of the original engagement. No templates, no merge-field substitution: genuine personalisation at volume.

The remaining stages handle sequencing (multi-touch cadence management), reply management (classifying inbound responses and routing to human review where needed), and meeting push (automated calendar coordination for warm responses). The entire system is CRM-agnostic by design and uses open-source components throughout, so the client owns the infrastructure.

The Outcome

The system reactivated a pipeline population that had been written off as unworkable. Contacts classified as dead in the CRM generated qualified meeting bookings at a rate that justified the original lead acquisition cost many times over. Personalisation at one hundred percent of volume, something that would require a large SDR team to approximate manually, ran continuously without headcount.

The build-and-handover model meant the client received full ownership of the workflow code, the enrichment library, and the prompt architecture. The system runs in their environment, against their data, without ongoing dependency on Stromy. That ownership model is intentional: AI-powered sales infrastructure should be an asset on the client’s balance sheet, not a recurring service fee.