Custom Dynamic Pricing Software Development Services

SaaS pricing platforms get you live in weeks and fit standard B2B and retail use cases. Custom wins when you need proprietary AI logic the vendor won't ship, native integration with a custom ERP, full IP ownership of the algorithm, or pricing across more than 100,000 SKUs where SaaS per-SKU fees stop making financial sense.

If your pricing data is a competitive moat, you want cloud-native pricing software built on your AWS account to keep it intact.

Per industry average, a focused MVP with a rules engine, two-channel integrations, and basic analytics lands between $120,000 and $250,000. A mid-range build with ML elasticity models, CPQ or ERP integration, and a full visual strategy builder runs $250,000 to $600,000. Enterprise price optimization platform programs with Agentic AI, omnichannel sync, and migration from a legacy SaaS run $700,000 and up.

A working MVP ships in 3-4 months, per industry average. A mid-range engine with full ML elasticity and 2-3 system integrations takes 6-9 months. Enterprise rollouts with Agentic AI workflows, multi-region deployment, and SaaS migration run 9-15 months with phased releases.

We deliver a proof of concept within 14 days of project start, so stakeholders see real-time price recalculation on real data before committing to the full build.

Yes, we can integrate with your current systems. We are equipped to build connections for major CPQ platforms like Salesforce, SAP, and Oracle. We can also sync pricing with ERPs such as SAP S/4HANA, NetSuite, and Microsoft Dynamics 365, and handle commerce integrations for Shopify Plus, BigCommerce, Magento (Adobe Commerce), and various headless APIs.

If you rely on bespoke or undocumented systems, we will run a dedicated discovery phase first. This allows us to provide you with a clear, realistic integration scope before we commit to the work.

Pure rules engines lose to competitor automation within 12 months, and pure ML models break trust the first time they propose a price below cost. It is recommended to build a rules layer that owns guardrails (margin floors, MAP compliance, channel parity) and an ML layer that proposes moves within those guardrails using machine-learning pricing models.

The visual strategy builder gives commercial teams full audit visibility on every decision, so the ML layer is never a black box. That's the pattern that survives both finance review and pricing-analyst pushback.

Both, depending on the use case. Third-party feeds (Prisync, Intelligence Node, DataWeave) are faster to plug in and fine for standard mid-market competitor price tracking. Proprietary scrapers win when you need data freshness under 10 minutes, you operate in markets where commodity feeds have poor coverage, or product matching needs custom logic for your category.

We build scrapers on AWS Lambda with rotating proxy infrastructure and ML-powered product matching to defeat anti-bot countermeasures.

Pricing platforms touch margins, costs, and customer data, three of the most sensitive datasets in the business. We build to SOC 2 compliant pricing software control patterns and ISO 27001 software development standards from day one, with AES-256 encryption at rest, TLS 1.2+ in transit, role-based access control, and segmented network environments.

For US and EU operators, we ship GDPR compliant pricing data patterns (data residency, right-to-erasure, consent logs) plus CCPA workflows. MAP enforcement is a feature, not just a compliance checkbox — automated violation detection across global resellers with reporting workflows for brand-protection teams.

We've delivered zero-downtime migrations on adjacent commerce and reporting platforms and apply the same playbook to pricing engines. The standard pattern is data audit, schema mapping, a dual-write window where the new engine and the existing system run in parallel, validation against historical pricing decisions, then cutover during a planned low-traffic window. The legacy system stays as a hot rollback for a defined post-cutover period.

We also handle extracting your pricing rules from the SaaS UI when there's no export API, reconciling rule logic between systems, and rebuilding 12+ months of historical pricing context so your ML models start with real signal.

You own everything: source code, data models, ML weights, infrastructure-as-code, and deployment pipelines. Hosting goes on your AWS, GCP, or Azure account, not ours; we use AWS pricing infrastructure as the default given our AWS Select-Tier partnership, with Kubernetes pricing deployment for engines that need elastic scale on flash sales or seasonal peaks.

You get 1-hour incident response, 24/7 monitoring, model retraining, and platform upgrades. 80% of our clients stay with us 7+ years, so the team that built your engine is the same team supporting it three releases later.

Typical post-launch retainers run $12,000 to $35,000 per month, depending on engine scale, AI feature load, and the number of integrations under SLA.