Dynamic & Continuous Pricing
Pricing is no longer a batch file. We run rules, optimizers, and ML side-by-side to generate fares and ancillaries continuously while keeping revenue management firmly in control. Every decision is auditable, replayable, and tied to business guardrails.
Signal rich
Demand, supply, loyalty, and competitor data feed pricing decisions instantly.
Control friendly
Floors, fairness, and operational guardrails baked in with streaming monitoring.
Bundling aware
Ancillary and bundle pricing align with Offer Management so upsells stay coherent.
Signals to decisions
Dynamic pricing
Dynamic pricing
We ingest demand, supply, loyalty, and competitive signals to produce explainable prices. Floors, ceilings, and fairness rules are part of the core design.
- Dynamic floor pricing based on flight, customer, or any dimension (time to departure, inventory, channel, persona).
- Predictive AI connectivity so demand-supply models feed prices in real time.
- Rule-based overrides and guardrails ensure pricing stays compliant and brand-safe.
Blended intelligence
Continuous pricing
Continuous pricing
Mix floors, dynamic rules, and AI to deliver continuous price points. Switch models by market or cohort without rewriting core code.
- Blend floor, dynamic, and AI pricing engines to produce the ultimate price per request.
- Explainability on every decision—feature contributions, model versions, and triggered guardrails.
- Scenario sandboxes for revenue management to test new strategies safely.
Attach value in realtime
Dynamic bundling
Dynamic bundling
Bundles can be reconfigured on the fly. Seat + lounge + insurance combos or ancillary packs change based on rules, inventory, and customer attributes.
- Dynamic bundling of products based on any rule or persona.
- Control stocking, eligibility, and upsell sequencing through shared policies.
- Expose bundle options with contextual messaging in Offer Management and distribution channels.
FAQs
Can we start with rules only?
Yes. Begin with rule-based pricing, then introduce ML/optimizers gradually per market or channel.
Is it explainable?
Every decision carries feature contributions, model versions, and the guardrails hit, so pricing teams trust the output.
How do you prevent runaway models?
Floors, ceilings, fairness checks, and anomaly detection run continuously. You can enforce approvals before promoting new models.
Do you support ancillaries and bundles?
Fully. Ancillary pricing and bundles run through the same decision engine so offers stay consistent.