The business problem behind the build.
The founding team had a strong vision for a pricing product, but they needed a system that could pull from multiple data sources, forecast demand, optimize for different commercial goals, and feed pricing actions back into the e-commerce platform.
This was not a lightweight experiment. It had to become a reliable operating system for a core commercial decision.
A system designed for real decision-making.
We built an end-to-end MLOps pipeline that ingests commerce signals, estimates demand response, and applies constrained optimization to produce recommended prices aligned with profit, GMV, and growth targets.
The system was deployed in collaboration with Outerbounds, with workflows orchestrated so the client could move from batch data processing to a repeatable production path.
The decisions that made the workflow hold up.
- Demand curves were modeled as distributions rather than single guesses, which made pricing decisions more robust under uncertainty.
- Lagrangian optimization balanced growth and profitability while respecting the tradeoffs the business actually cared about.
- Constraint handling and repricing rules kept final recommendations aligned with business policy and operating reality.
Workflow View
Dynamic Pricing Pipeline
How data, optimization, and business guardrails shape a final pricing action.
Commerce inputs
The ingestion layer unifies product, customer, and historical sales data into a dependable feature base for pricing decisions.
Key signal: Multi-source ingestion
Result
The resulting system generated pricing actions that lifted revenue by 35% and increased sales by 25% while staying inside the guardrails the business needed.
Just as important, the client moved from an ambitious concept to a durable product that could be iterated with confidence.
What this work says about how ControlThrive builds.
Great pricing systems do more than optimize. They translate business strategy into rules and objectives the product can reliably execute.