At Enlyft, our commitment to transforming customer acquisition and intelligence for B2B companies is driven by a powerful, innovative engineering team. Today, we're taking a detailed look under the hood at the technology and processes that power the Enlyft platform.
The Core: Building the Enlyft Company Graph
The foundation of Enlyft is our proprietary data platform, the Enlyft Company Graph. This graph isn't just a static database; it's a dynamic, real-time information source containing a wealth of B2B intelligence, including company firmographics, technology usage, buying intent signals, and hundreds of additional account attributes.
Building and maintaining this comprehensive dataset is a continuous and complex undertaking. Our process begins with crunching billions of public documents as well as vetted third-party data daily to extract relevant company information. This massive influx of data is then processed by our proprietary AI models. These models play a crucial role in validating, classifying, and refining the data – helping detect anomalies or critical events.
Data Quality is paramount in our data operations. We prioritize investment in contact data quality and the detection of irrelevant/fake profiles. Our dedicated team of data scientists monitor and continuously improve our AI models to accommodate the ever-growing data set and stricter quality requirements. This ensures that our users have access to the most accurate and reliable B2B intelligence available.
The scale of the Enlyft Company Graph is significant, providing insights on 40+ Million companies worldwide, tracking 20+ Thousand technologies, and covering over 300+ Million contacts worldwide. Furthermore, we process billions of news, events, hirings, jobs, etc. to identify crucial Buying Signals. This vast amount of data is analyzed using sophisticated Machine Learning and AI to generate key insights such as the AI Core Buying Signal Score, Cloud/SaaS Adoption, Org Types (ISV, MSP, B2B, Growth Index), and Enlyft Buying Signals.
Engineering Culture: Driving Innovation and Scalability
Our engineering efforts are guided by a set of core cultural principles that foster innovation and ensure the scalability and reliability of our platform.
- Start Simple, Then Scale: We believe in building solutions that deliver immediate value while keeping future growth in mind. This approach allows us to iterate quickly and validate our ideas before committing to large-scale implementations.
- Iterate Fast, Learn Faster: Rapid iteration is crucial to our development process. We focus on quickly testing ideas, gathering feedback, and making continuous improvements. This agile approach ensures that we are constantly delivering value to our customers.
- Cut technical debt to maintain speed and scalability: We proactively address technical debt to ensure our platform remains performant and adaptable as it evolves.
- Focus on delivering real value to customers with every iteration: Our primary focus is always on how our engineering efforts translate into tangible benefits for our users.
- Own the Outcome: Our POD-based functional ownership teams take full responsibility for the results, quality, and timelines of their work. This fosters a sense of ownership and accountability throughout the engineering organization.
- Align goals across product and engineering for cohesive execution: We ensure that product and engineering teams are working in lockstep towards common goals, leading to more efficient and effective development.
Enlyft Platform: A Complete AI-Native Platform
The Enlyft Platform is designed to be complete, AI-Native, and Easy-to-Activate. It comprises two key layers: the Data Platform and the Application layer.
Data Platform:
Our focus here is on building a world-class B2B Sales and Marketing data platform. Key objectives include:
- Establish Single Source of Truth: Ensuring data consistency and accuracy across the entire platform.
- Data Trust - Beyond Data Profiling: Implementing rigorous measures to guarantee the reliability and integrity of our data.
- AI / ML - Data Pipelines / Feature Engineering: Utilizing advanced AI and machine learning techniques to process and enrich our vast datasets.
- Self-Serviceability: Context (RAG, Vector / Embeddings): Empowering users with the ability to access and understand data in a contextualized and intuitive manner.
- Data Research - QA / Precision / Recall: Continuously monitoring and improving the quality, accuracy, and completeness of our data by utilizing agentic workflows
Application Layer:
Built on top of our robust data platform, this layer provides an Enterprise SaaS Platform and APIs for critical sales and marketing workflows, including Prospecting – Discover, Prioritization, Qualify Accounts, Buyer Persona & Contacts, Outreach. Core features include Account Fit, Buying Signals, and Visitor Tracking. We also offer Public APIs / Integrations (CRM) to seamlessly integrate Enlyft into existing technology stacks.
At Enlyft, we believe that the combination of a powerful data platform, innovative AI/ML solutions, and a strong engineering culture positions us at the forefront of B2B sales and marketing intelligence. We are excited to continue pushing the boundaries of what's possible and delivering transformative solutions to our customers.
Looking to the Future: AI Native Agentic Workflows
We are continuously exploring and implementing cutting-edge technologies to further enhance the Enlyft platform. This includes leveraging Agentic features such as Intel Snapshots, AI Data Points, and Revenue Predictions to provide even deeper insights. We are also actively developing Co-Pilots and Agentic Automation capabilities to streamline workflows and empower our users with intelligent assistance.
At Enlyft, we believe that the combination of a powerful data platform, innovative AI/ML solutions, and a strong engineering culture positions us at the forefront of B2B sales and marketing intelligence. We are excited to continue pushing the boundaries of what's possible and delivering transformative solutions to our customers.