Kinetik provides revenue intelligence SaaS solutions and services that help enterprise B2B tech companies improve their marketing ROI, increase sales productivity, and optimize go-to-market performance through AI. Their platform equips RevOps, sales, and marketing teams with deep funnel analytics and actionable insights for complex B2B buying journeys.  

To expand their platform’s sales intelligence capabilities, Kinetik partnered with GoML to build a new layer of AI-driven features. These features were designed to enrich contact data, infer buyer roles, and detect high-intent buying signals, all using Gen AI and cloud-native infrastructure.

The problem: incomplete data, unclear roles, and missed buying signals

Kinetik’s platform already captured performance data across accounts, but sales teams still struggled with incomplete contact records and unclear buyer roles. Many contacts lacked crucial metadata such as job title, persona, or seniority, making it difficult to distinguish between end-users, influencers, and decision-makers.

Even when engagement signals existed, like content interactions or product interest, they were fragmented across tools and hard to prioritize. To make their insights more actionable for users, Kinetik needed a Gen AI-driven intelligence layer that could turn raw contact data into high-confidence, role-aware buying signals.

The solution: an AI sales platform built on AWS Bedrock

GoML partnered with Kinetik to develop modular, AI-powered features for their platform leveraging Amazon Bedrock and Claude. These features infer buyer roles, enrich contact data, and track high-intent signals across accounts.

Data ingestion and enrichment

• Contact data is sourced from AWS S3 and stored in PostgreSQL

• Missing metadata like job level, persona, and title is enriched via APIs such as Apollo, PDL, and SerpAPI

• This creates a structured, intelligence-ready dataset for AI processing

Role inference with Claude models

• Using dynamic prompt templates stored in S3, Claude LLMs classify each contact as a “Decision Maker,” “Influencer,” or “User”

• Templates are modular and version-controlled, allowing updates without backend code changes

Buying group analysis and signal detection

The AI layer synthesizes contact-level intelligence into account-level views:

• Role-persona combinations by company

• Top 5 real-time buying signals (e.g., demo requests, content views, opt-ins)

• Which roles are engaging with which products

• An auto-generated company summary for sales reps

• All results are cached for fast retrieval and reuse across the GTM engine

The impact: smarter AI sales platform for more accurate signals

With GoML’s AI features integrated into its platform, Kinetik achieved meaningful improvements across its sales and marketing workflows:

• 60% improvement in signal classification accuracy, enabling sales reps to focus on the right contacts and reduce false positives.

• 100% enrichment of contact records in test datasets using API-based augmentation, improving targeting precision.

• 70% reduction in manual research and analysis time, allowing teams to spend more time selling and less time cleaning data.  

Lessons for B2B GTM and sales teams

Common pitfalls to avoid

• Assuming CRM data is complete or reliable

• Treating all contacts the same without understanding influence or role

• Ignoring high-intent signals spread across channels

Tips for product and growth teams

• Use LLMs to dynamically infer buyer roles based on enriched inputs

• Connect to enrichment APIs (Apollo, PDL, SerpAPI) to fill missing fields

• Prioritize accounts and contacts with the strongest intent signals

• Design your AI features to be modular, updateable, and API-driven

GoML can help your RevOps or SaaS platform integrate advanced AI sales intelligence features, from role inference to real-time signal detection. Whether you’re enriching your product or scaling GTM efforts, we’ll help you embed Gen AI where it matters most.

Ready to upgrade your sales intelligence?