Project Description
I need an end-to-end, self-running engine that continuously finds and nurtures B2B prospects for office-space leasing in Gurgaon, focusing strictly on established companies. The platform has to do more than scrape names—it must predict which firms are most likely to relocate or expand, reach out to them with human-sounding messages, and keep the conversation flowing until my sales team steps in.
Core workflow
• Data sourcing & enrichment: pull fresh company information from publicly available registries, LinkedIn, news feeds, and any other ethical sources, then append decision-maker contacts automatically.
• Predictive analytics: score every prospect on its likelihood to lease new space within the next 3–6 months.
• NLP-driven email outreach: craft and schedule personalized sequences that adjust tone, timing, and content based on real-time engagement signals.
• AI chatbots: engage inbound visitors on our site or landing pages, qualify them, and feed the CRM.
• Hands-free nurturing: route qualified leads to the sales inbox or directly into our CRM with complete contact history.
Acceptance criteria
1. At least 50 net-new, qualified Gurgaon occupier leads delivered in the first 30 days of live operation.
2. Predictive scores showing ≥70 % precision when back-tested against historical deals we provide.
3. Response and meeting-booking metrics visible on a live dashboard.
4. All components must run without manual intervention beyond initial configuration.
Tech flexibility is welcome—whether you prefer Python with scikit-learn, BigQuery pipelines, HubSpot/Zoho integrations, or proprietary tools, just keep the stack transparent and maintainable.
Once the machine proves itself, we can discuss scaling it to other Indian cities; right now, Delhi NCR mainly Gurgaon is the only geography that matters.