About OnDeck Partners
OnDeck Partners is a US-based private equity firm that acquires and operates Minor League Baseball franchises. Their portfolio includes the Montgomery Biscuits (AA affiliate of the Tampa Bay Rays) and the Visalia Rawhides (A affiliate), alongside the OnDeck Partners investment entity itself. With three distinct organizations sharing common ownership but operating independently, OnDeck needed a CRM infrastructure that could serve each entity's specific commercial workflows while giving the parent company a unified view across the portfolio.
The Challenge
Each baseball team was running its sales operation on Zoho CRM, independently configured and maintained. The problems were structural:
Three separate entities, no shared infrastructure. OnDeck Partners, the Montgomery Biscuits, and the Visalia Rawhides each had different workflows, different team sizes, and different commercial priorities. A single shared workspace would create noise; three completely separate systems would prevent visibility at the portfolio level.
Legacy Zoho setups built for workarounds. The sales teams had been tracking deals by manually adding line items for everything - including taxes, service charges, and individual game inventory - creating data quality problems that made commission tracking and reporting unreliable. No rate card structure existed: sales reps were creating new line item types for each deal.
No inventory management. Game-day suites, sponsorship assets, and season ticket packages were tracked across Google Sheets. There was no way to prevent the same inventory being sold twice, and no connection between CRM deals and the products being sold.
No integration between systems. Phone calls (via Ringover), quoting and contracts (PandaDoc), and analytics (BigQuery via Hightouch) were all disconnected from the CRM. Sales activity was manually logged, or not logged at all.
Our Approach
Novlini delivered the project over several months, with Will Stenzel embedded on-site at both clubs during the initial rollout.
Data Model Architecture
Three Attio workspaces configured: one for each baseball club and one for OnDeck Partners. The data model for the clubs was built around a single Deals object and Lists - reflecting how Minor League Baseball sales actually work, with ticket packages, group events, and sponsorship assets as the primary commercial objects.
A Figma data model was built before any configuration began, mapping the full object structure, field requirements, and integration touchpoints. Discovery interviews were conducted with key stakeholders at both clubs to validate the model before implementation.
Migration from Zoho
Historical deal and contact data migrated from both Zoho instances. Data cleaned before import - line item types consolidated, commission-relevant fields separated from tax and service charge fields that had been incorrectly tracked as deal components.
Sample migration validated with each club's operations lead before the full import was triggered. Post-migration, adoption dashboards were built to track deal creation by rep across both clubs, giving the OnDeck team visibility on rollout progress in real time.
Line Item and Rate Card Structure
A structured line item architecture designed to replace the ad hoc Zoho approach. Line item types defined for the main commercial categories - season tickets, group packages, sponsorship assets, game-day suites - with templates pre-built so reps select from existing options rather than creating new types per deal. Foundation laid for the PandaDoc integration: when a deal is ready for quoting, line item data flows directly into the quote template without manual re-entry.
Integrations
Ringover connected to both club workspaces: inbound and outbound calls logged automatically to the relevant contact and deal records, with AI-generated call summaries syncing to Attio post-call. The integration required per-club configuration given different state recording laws (California two-party vs Alabama one-party).
Hightouch configured to push ML-scored lead data from BigQuery into Attio - qualified leads surfaced directly in the CRM for the sales team to action, without requiring a separate tool or report. Ryan Koenig (OnDeck data engineer) owns the data pipeline; Novlini prepared the Attio field structure and documentation to receive the data cleanly.
PandaDoc integration scoped and in progress: deal line items feed directly into quote templates, with tax logic applied automatically based on line item type and club location.
Technology Stack
Attio - three workspaces: OnDeck Partners, Montgomery Biscuits, Visalia Rawhides (replaces Zoho)
Ringover - call and SMS tracking, synced to Attio
Hightouch - ML-scored lead data pushed into Attio from BigQuery
PandaDoc - quoting and contracts, integrated with Attio line items (in progress)
BigQuery - deal and contact data exported from Attio for portfolio-level analytics
The Results
Both clubs went live on Attio within weeks of the initial rollout, with deal creation tracking showing healthy adoption across sales reps at both franchises.
Three Attio workspaces operational - OnDeck Partners, Montgomery Biscuits, and Visalia Rawhides each with purpose-built data models
Zoho migration completed - historical deals and contacts from both clubs imported and cleaned
Adoption visible in real time - portfolio-level dashboards show deal creation by rep across both clubs
Ringover live on both teams - calls and SMS logged automatically to Attio records
Rate card structure in place - line item templates defined, ad hoc creation eliminated
ML lead scoring flowing into CRM - Hightouch pipeline operational, qualified leads surfaced in Attio
Why It Matters
Minor League Baseball operations have a commercial structure that most CRMs are not designed for. You are selling perishable inventory - game-day suites, group packages, sponsorship placements - that expires on a specific date and cannot be resold. You are tracking commissions on deals that include items that are commissionable and items that are not. And you are doing this across a small, high-churn sales team where data quality depends entirely on what reps enter.
Building the right data model from the start - with a structured rate card, clean line item architecture, and direct connections to quoting, phone, and analytics - is what turns a CRM from a reporting burden into an operational backbone for the commercial team.