Opportunities
Apex Pricing Core
Connected through 6 “incumbent in” links and 2 “embodies” links.
Structure
Opportunities
Opportunities
Connected through 6 “incumbent in” links and 2 “embodies” links.
Structure
Build difficulty
Hardest Part
Normalizing deeply fragmented transaction histories and complex discount hierarchies from legacy ERPs to accurately model price elasticity without generating margin-destroying recommendations.
Min Viable Scope
Deliver a price optimization engine strictly for net-new sales quotes in mid-market wholesale distribution. Deliberately leave out billing execution, automated quote generation, subscription management, and retroactive rebating.
Cold Start Problem
The engine requires historical transaction data to train elasticity models, but companies refuse live pipeline integrations without proven ROI. Break this by offering an offline margin leakage audit using static CSV exports.
Time To First Value
2 to 4 weeks of historical data ingestion and model backtesting.
Data Moat Available
true
Technical Difficulty
High
Build profile
The gap
Wedge
Target usage-based infrastructure SaaS companies where tier upgrades and overage negotiations require constant and complex mathematical modeling. This niche experiences acute pain because manual quoting delays renewals and degrades margin. Expansion moves from optimizing renewal pricing for usage-based SaaS to generating net-new deal structures, eventually capturing traditional seat-based software companies.
Timing
Large language models process unstructured win-loss transcripts and messy historical CRM data to map price elasticity without requiring perfectly structured data pipelines.
Why This ICP
Mid-market SaaS companies between $20M and $100M ARR possess sufficient deal volume to generate pricing signals but lack the dedicated internal data science teams found at large enterprises.
Size Of Prize
There are approximately 35,000 mid-market and enterprise B2B SaaS companies in the US and Europe. At an average annual spend of $40,000 per company for CPQ administration and external pricing strategy consultants, the addressable economic value is $1.4B.
Gap Narrative
B2B SaaS companies lose margin by relying on static pricing grids and manual spreadsheet quoting that ignores live customer usage and market elasticity. RevOps teams require a dynamic engine that executes real-time adjustments and generates optimized deal structures instantly. Current CPQ tools enforce business rules but do not optimize the actual price point based on historical win rates.
Defensibility
The system compounds value through workflow lock-in and a proprietary data moat. As the engine prices more deals, it trains a unique company-specific elasticity model that maps conversion rates against specific price points. Removing the software forces the customer to abandon years of optimized elasticity data and return to baseline guesswork.
Why This Thesis
Service-as-Software fits this problem because pricing strategy traditionally requires expensive consulting engagements. The platform absorbs the analytical labor of a pricing analyst and embeds the output directly into the sales quoting workflow.
Overview
Sized prize
IllustrativeIllustrative targets and order-of-magnitude estimates — not an achieved track record. This Thing is concept-stage; real figures come from live data once operating.
SAM
~$800M-1.2B targeting the ~10k-15k mid-market and enterprise SaaS firms with complex monetization
SOM
~$15-30M realistic 3-year capture based on direct sales capacity to enterprise software vendors
TAM
~50k global software and SaaS companies × ~$80k/yr average spend on pricing infrastructure ≈ ~$4B
Growth Rate
~18-24%/yr, driven by the enterprise shift from flat-rate subscriptions to complex hybrid and usage-based billing models
Paid Comparable Spend
~$150k-300k/yr spent on dedicated billing engineering teams, legacy CPQ software, and pricing strategy consultants
Market sizing
How you know
Kill Thresholds
Leading Metrics
What Proves Right
Engineering teams replace custom billing logic with Apex Pricing Core within 30 days of implementation. Customers successfully route at least $1M in monthly usage revenue through the pricing engine within the first quarter of deployment. Cohorts signing annual contracts at the $80k price point renew at >90% gross retention due to direct savings on internal billing engineering headcounts.
What Proves Wrong
Implementation cycles drag beyond 90 days as engineering teams struggle to map legacy ERP data models into the engine. Sales cycles stall when pricing strategy teams refuse to migrate away from existing Excel models or custom CPQ configurations. Customers default back to flat-rate subscriptions because configuring complex hybrid tiering in the platform requires heavy developer intervention.
Win conditions