Pricing & engagement models
Turn a business question into a prioritized AI pilot
We structure engagements around concrete use cases and short pilots. Each engagement delivers artifacts you can reuse: data mapping, integration scripts, evaluation metrics and a deployment checklist.
Real-case pilots
Operational handover
Flexible pricing aligned with use cases
Plans grounded in scenario scope and operational readiness
Discovery Session
- Scoping workshop and use-case map
- High-level technical feasibility review
- Example-driven success criteria
- Proposal and phased roadmap
Production Accelerator
- Full pipeline build and model validation
- Staged deployment with rollback and testing
- Monitoring dashboards tied to KPIs
- Operational runbooks and team handover
- Priority implementation support with biweekly milestone reviews, tailored integration scripts for legacy systems, and on-demand architectural consultations during the first 90 days of deployment.
Basic Implementation
- Discovery workshop and implementation roadmap focused on one production use case.
- Standard AI model configuration and deployment using hosted infrastructure.
- Integration adapters for one external data source and one output channel.
- 30 days post-deployment monitoring and tuning with a single performance review.
- Access to online documentation, implementation playbooks, and one technical Q&A session.
Implementation case studies, playbooks and scenarios
AIHubWorks focuses on practical, scenario-based AI implementation. We present concrete cases to show how an AI model moves from prototype to production in real business settings. Examples include an inventory demand forecasting playbook for a mid-sized retailer, an automated quality-inspection pipeline for a manufacturing line, and a customer-support triage flow for a regional service provider. Each case outlines objectives, data preparation steps, integration points, testing scenarios, rollback procedures and measurable KPIs used during rollout. The text emphasizes reproducible steps, risk mitigation, and how teams can reuse artifacts such as data schemas, test suites and monitoring dashboards when scaling to other processes.
We structure implementations as repeatable scenarios: define the decision point, prepare the data, integrate incrementally, and validate results with live case tests.
