Technology

Put your data to work across the business and create an AI foundation.

Your sales, product, marketing, customer success, and other teams already generate the data you need. We bring it together and organize it so teams can use it for reporting and analytics today, while creating the foundation for AI applications across the business.

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The Challenge With Implementing AI in Technology Companies

The data exists in ten places. It means something in none of them.

63%

Organizations that don't have, or aren't sure they have, the data management practices required for AI. Even technology companies building AI products and services can face significant gaps in their own data infrastructure.

Sales data may be in the CRM, while product usage, support, marketing, and renewal data are managed in other systems. Different teams can also use different customer records, definitions, and account hierarchies, making it difficult to connect that information across the business.

Those inconsistencies become more important when the data is used for AI. Duplicate records, incomplete customer histories, or conflicting definitions affect the information available to the model and the results it produces.

Establishing consistent definitions, customer hierarchies, and governance gives teams a common foundation for reporting and analytics and provides the data needed to support AI applications.

Technology companies may build products that depend on clean, consistent data while struggling with the same issues internally. AI initiatives expose gaps like inconsistent definitions, fragmented systems, and reporting that depends on manual reconciliation.

COMMON TECHNOLOGY AND ENTERPRISE SOFTWARE APPLICATIONS

What we've shipped in technology and enterprise software

Most AI initiatives fail after the strategy deck is delivered. PivotX stays through execution, adoption, and measurable business outcomes because that's where transformation actually happens.

Yours would look different.

Customer 360 and Master Data Management

Fragmented customer data unified into a single governed view across sales, support, licensing, and revenue operations, with visual hierarchy navigation and self-service stewardship.

AI-Driven Customer Segmentation and Propensity Scoring

ML-based segmentation using CRM and third-party firmographic data. Active deals scored by win propensity. Aging deals and priority renewals get surfaced automatically instead of found by accident during a pipeline review.

Sales and Marketing Data Platform

End-to-end funnel visibility on a unified platform. Sales cycles shortened. Lead targeting improved. Conversion rates increased after fragmented tracking was replaced.

Conversational Data Insights

GenAI-powered natural language query over your own data. A product or operations leader gets an answer the same afternoon instead of putting in a request and waiting.

AI Readiness Assessment and Data Strategy

Architecture and market readiness evaluated against product roadmap and AI ambition, ahead of the investment decision, not after it's already been made.

GTM Strategy and Channel Diversification

Go-to-market transformation: positioning sharpened, offerings defined, channel mix restructured for scalable, diversified revenue growth.

HOW WE WORK

Knowing how to build AI isn't the same as knowing what to do with it.

Technology companies often have the technical resources to build AI internally, but they don't always know what data their sales, customer success, or product teams need or how AI can help them use it. We start by understanding what those and other teams are trying to accomplish. We look at the data available to support them, what needs to be brought together, and where AI can improve an existing process or create a new capability. The first engagement creates a governed foundation your team owns. Additional use cases can build on the same data rather than requiring a new foundation each time.

See our full method

Week 1–4

Explore & Evaluate

We map the business and identify where AI could create measurable value. Working with your team, we evaluate potential use cases and narrow the list to the ones worth pursuing. By the end of week four, we know what we're building, why it matters and how we'll measure success.

Week 5-8

Validate

Solution design and prototyping happen alongside user involvement. The people who will use the solution help shape it as it develops. Working MVPs give the team something real to test and evaluate rather than asking them to react to a finished product.

Week 9-10

Value & Scale

We deliver the working AI solution, a validated 12-month roadmap, and a quarterly execution plan. We also begin the process of putting the solution into use. At this point, your team has experience with the solution and a clear understanding of what comes next.

Proof