After-Sales Capabilities
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ServiceCPQ follows a pragmatic, iterative implementation methodology — starting with what works out of the box, then collaboratively closing gaps, training models, and deploying with confidence.
Each phase builds on the last. We start by proving value with the standard platform, then close gaps systematically — so you know exactly what you're getting before customisation begins.
We start by mapping your current state — how opportunities are found, how quotes are built, how contracts are priced, and what data exists in your ERP and CRM. This gives us a shared baseline before any configuration begins.
We configure the standard ServiceCPQ platform using your real data — equipment models, parts catalogues, job libraries, pricing rules, and contract templates. The goal is to show you exactly what works out of the box before any customisation is discussed.
After seeing the base model, every client identifies requirements that aren't yet met out of the box. We run a structured prioritisation workshop to classify each gap — deciding what gets built, what gets added to our product roadmap, and what can wait.
Must Have gaps are built in focused sprints — custom workflows, ERP adapter extensions, bespoke pricing rules, and any industry-specific configurations. Each customisation is documented, tested, and peer-reviewed before integration into the platform.
With the platform configured and customised, we load your full production dataset and train the AI models against your real equipment, customer, and service history data. Infrastructure is provisioned on AWS, client cloud, or hybrid — according to your preference and security requirements.
Before any production deployment, the fully configured and trained platform runs through structured testing in a dev environment — covering integration flows, UI workflows, model accuracy, and edge cases. Client teams conduct formal User Acceptance Testing (UAT) against agreed test scripts.
Production deployment is a managed cutover with hypercare support. Once live, we enter a continuous improvement cycle — monitoring model performance, retraining on new data, delivering Should Have features, and iterating based on real user feedback.
Not all gaps are equal. Our prioritisation framework ensures the right requirements are addressed at the right time — without over-engineering or scope creep.
Requirements without which the platform cannot meet core business needs. These block go-live and must be resolved in Phase 4 before production deployment.
Valuable requirements that don't block go-live but significantly improve the experience. We assess whether adding them to our product benefits all clients — if yes, they ship as standard features.
Useful but non-critical features that would improve efficiency or convenience without affecting core outcomes. Deferred to post-go-live iteration cycles once the platform is live and stable.
ServiceCPQ deploys on the infrastructure that meets your security, data residency, and cost requirements.
Managed by our team on AWS. Fastest to deploy. Multi-tenant with full data isolation. SLA-backed uptime.
Deployed into your own AWS account. You own the infrastructure. Ideal for data residency or compliance requirements.
Container-based deployment on Azure AKS or Google GKE. For clients with existing Azure or GCP enterprise agreements.
On-premise or hybrid deployment for air-gapped environments. API-compatible with ServiceCPQ cloud for sync scenarios.
Most clients are running against their real data within two weeks of kick-off. Let's start with discovery and show you what the base platform delivers out of the box.
©2023 All Rights Reserved. Auxentios Technology Solutions
