For years we championed a way of thinking about pricing that was, in many ways, ahead of its time. Instead of focusing on price elasticity and manual rules, the goal was to understand what truly drives a sale: how seasonality, customer behavior, competitive dynamics, inventory, and many other factors influence buying decisions at any given point of sale. That philosophy became a retail-native contextual AI engine, and today companies in more than 20 countries use it.
But we also learned something important along the way.
That kind of price optimization requires deep historical data and a high level of organizational maturity: a readiness to map years of transactional history, retrain teams, and let go of manual control. Many retailers are simply not there yet, and not because they lack ambition, but because the infrastructure is not in place.
The most common feedback we heard from prospective clients was direct:
“Your solution is the most progressive we have seen on the market, but we are not ready for it yet."
That insight shaped what we are announcing today.
Competera’s flagship products, Pricing Platform and Competitive Data, are now available in new editions, each designed to meet a retailer where their pricing function is today and grow with them as it matures.
Pricing Platform ships in two editions: Adaptive AI, for retailers who want rule-governed repricing workflows, and Contextual AI, for those ready for demand-led optimization. Competitive Data follows the same principle with two packages: Starter, for retailers who need high-quality competitive data with full context around the price and pay-per-use billing, and Enterprise, for those who need multi-country SLA-controlled market intelligence.
Competitive Data works both ways. Many retailers run it on its own, feeding market intelligence into pricing they manage themselves or into systems they already have. For others, it is the first step toward automation. The two packages are designed to match the scale of market intelligence a retailer requires, from flexible pay-per-use coverage to a full SLA-controlled dataset across markets and banners. The data a retailer collects from day one becomes the foundation their pricing engine will run on when the time comes.
When a retailer is ready to automate pricing but is not yet in a position to hand decisions to the models, Adaptive AI is the right starting point. We thought long and hard about this edition, searching for the right balance between preserving our vision and meeting the real needs of the market. Three things mattered most:
What we built:
Contextual AI remains the most advanced pricing engine we have ever built. It is a neural network that reads more than 20 demand-driving factors, from elasticity of demand and competitive dynamics to inventory health and product lifecycle stage, to recommend the optimal price for every product, at every store, every day. It requires 24 months of transaction history and comes with an 85% forecast-accuracy commitment.
The path from Adaptive AI to Contextual AI is an upgrade, not a migration. A retailer does not rebuild their data architecture and does not lose what they have built. The segmentation, the pricing rules, the campaign configurations- all of it carries forward.
Both products share one workspace and one data foundation. Competitive Data feeds directly into both Pricing Platform editions as the external data layer, so the market intelligence a retailer collects is the same market intelligence their pricing engine runs on. Every move forward within Competera’s product portfolio builds on what came before instead of resetting it.
We did not design this structure to sell more products. We designed it because a retailer’s pricing function does not stand still. It matures. And retailers should be able to grow on the same platform, not migrate to a new one every time their needs evolve. Competera is now ready to be that partner, at every stage and through every transition between them.
Alex Halkin
CEO and founder
Competera