Luppa AI

Discovery and scoping

Objectives definition: Identify the business problem to solve (e.g., customer support automation, predictive maintenance, sales enablement).

Stakeholders: Align product owners, IT, data science, security, and legal.

Success metrics: Define KPIs (e.g., response time reduction, model accuracy, ROI).

Data assessment: Inventory data sources, data quality, and access requirements.

Non-functional requirements: Security, compliance (GDPR/CCPA), latency, uptime, and reliability.

High-level plan: Estimated timelines, milestones, and success criteria

Data readiness and integration design

Data access: Establish data pipelines, connectors, APIs, or data lake access.

Data governance: Provenance, lineage, retention, and privacy considerations.

Data formatting: Schema mapping, feature definitions, and labeling requirements.

Security & compliance: Role-based access, encryption at rest/in transit, key management.

Architecture and deployment planning

Deployment model: SaaS, on-premises, or hybrid; cloud provider alignment.

Inference endpoints: Real-time (low latency) vs. batch inference.

Monitoring: Logging, telemetry, drift detection, and alerting.

Scalability: Auto-scaling plans for varying load.

Integration points: CRM, ERP, helpdesk, collaboration tools, or data warehouses.

Model/solution desig

Model type: Retrieval, generation, classification, anomaly detection, or a mix.

Customization strategy:

• Fine-tuning on domain data

• Prompt engineering or policy controls

• Tooling for in-context learning

Evaluation framework: Holdout sets, A/B testing, safety checks, bias checks.

Governance: Documented model cards, risk assessment, and compliance alignment.

Domain adaptation: Fine-tuning on organization-specific data.

Prompt/policy customization: Industry-specific prompts, guardrails, and content controls.

Workflow automation: Custom business rules, routing, and integration with internal tools.

Extensibility: REST APIs, SDKs, webhooks, and custom adapters.

User interface: White-labeling, custom dashboards, and role-based views.

Data model customization: Custom feature engineering, schemas, and data connectors.

Security/compliance: Custom authentication, data residency, encryption schemes, and audit logging.

Analytics and reporting: Custom KPIs, dashboards, and scheduled reports.

Training data control: Data provisioning, labeling pipelines, and review processes.

Initial setup/implementation fee: Onboarding, discovery workshops, one-time configuration.

Subscription/license costs: Per-user, per-seat, or tiered pricing (starter, standard, enterprise).

Usage-based costs: Per API call, per thousand tokens, or per compute hour for inference.

Data/storage fees: Costs for data ingestion, storage, backups, and archival.

Custom development: Fees for tailoring features, connectors, or specialized models.

Training and onboarding: Administrator or user training sessions.

Maintenance and support:

• Standard support: business hours, response times

• Premium/enterprise support: 24/7, faster SLAs

• System updates and patching

Security/compliance: Additional charges for audit-ready controls, certifications, or data residency options.

Change requests and scope creep: Rates for scope changes during the project.

Exit/transition charges: Data export, knowledge transfer, or decommissioning costs.

Onboarding program: guided setup, success workshops, and a staged kickoff.

Admin and end-user training: live workshops, recorded sessions, and self-serve training materials.

Documentation: product docs, API references, playbooks, and usage best practices.

Learning paths: role-based curricula for admins, data stewards, and business users.

Proof-of-Concept (POC) support: assistance in designing and evaluating a small-scale pilot.

Customer success management (CSM): assigned CSM to monitor adoption, alignment with goals, and value realization.

Knowledge base and community: searchable articles, FAQs, and user forums.

In-product guidance: tutorials, tooltips, and sample workflows within the UI.

Training for data governance: labeling guidelines, data quality checks, and privacy controls.

Support channels: responsive options (email, chat, phone), with escalation paths.

SLA-linked response times: targets for critical issues versus general inquiries.

Train-the-trainer programs: enable your internal power users to sustain knowledge.

Data in transit and at rest: encryption standards (e.g., TLS 1.2+/AES-256)

Identity and access management: SSO, MFA, granular RBAC, least privilege

Data residency and sovereignty: where data is stored and processed

Data ownership and usage rights: whether vendor can use your data for training, benchmarks, or other purposes

Data minimization and retention: data minimization practices and configurable retention policies

Audit logging and monitoring: immutable logs, tamper-evident storage, alerting on access

Security certifications: SOC 2, ISO 27001, GDPR/CCPA compliance, HIPAA as applicable

Security testing: regular penetration tests, vulnerability assessments, and responsible disclosure process

Incident response: defined SLA for security incidents, notification timelines, and remediation steps

Data masking and de-identification: options for handling sensitive data (PII/PHI)

Backup and disaster recovery: RPO/RTO targets, data restoration procedures

Third-party risk management: vendor assessments for dependencies (cloud providers, integrators)

Update frequency: security patches, feature releases, bug fixes

Deployment model for updates: automatic/optional vs. manual; maintenance windows

Change management: versioning, release notes, impact assessment, rollback procedures

Testing and governance: pre-production staging, QA gates, canary or blue/green deployments

Downtime impact: expected maintenance windows, accessibility during updates

Backwards compatibility and deprecation: timelines for deprecated features

Communication: advance notice of major releases and security advisories

Customer control: ability to schedule updates or opt into P0 vs P1 fixes

Data ownership statement and any boilerplate language.

Whether data is used to train or improve models; if so, how you can opt out.

Available data export formats and API endpoints; export turnaround times.

Retention policies and default vs. configurable settings.

Deletion procedures, verification of deletion, and purge logs.

Data residency options (where data is stored and processed).

Logs and audit reports related to data access.

Scaling up

• Lead time required to scale (days/weeks) and any minimums.

• Any architectural requirements (additional onboarding, architectural review, compatibility with existing integrations).

• Pricing impact and renewal implications when scaling (tier changes, ramp-up pricing, caps).

Scaling down

• Downgrade process, notice period, and effective date.

• Reconciliation of unused licenses or capacity charges.

• Data retention implications after downscaling (e.g., keep data for a grace period).

Elasticity and auto-scaling

• Whether the platform supports automatic scaling and any associated risks (latency, quota limits).

Financial terms

• Whether scaling is billed on a tiered basis, usage-based, or both.

• Any penalties, minimum commitments, or decommitment fees for scaling events.

Migration considerations

• Dependency checks for decommissioning features or moving.

Pricing impact and renewal implications when scaling (tier changes, ramp-up pricing, caps).

Renewal model and term

• Automatic renewal vs. manual renewal; renewal term length; renewal notice windows.

Pricing and changes at renewal

• How prices can change at renewal; caps on increases; grandfathering terms.

Cancellation rights

• Right to terminate for cause (breach, SLA failure) and cure periods.

• Right to terminate for convenience or regulatory/compliance reasons (if any).

  
  Notice requirements: minimum advance notice before renewal or termination.

Data handoff at end of contract

• Data export rights, formats, timing, and any fees.

• Assistance or transition services post-termination and associated costs.

Return or deletion of data

• Post-termination data deletion timelines and certifications.

Breach and security incidents

• Notification timelines and obligations during the term and in wind-down.

SLA and support continuity

• What happens to support SLAs during transition; any transitional support windows.

Audit rights and compliance

• Customer access to audit reports during renewal discussions; redacted vs. full reports.

Exit fees or penalties

• Any early termination fees or minimum commitment penalties.

Contractual protections for data privacy

• Data processing addendum (DPA) terms, subprocessors, and flow-downs of security controls.

Security certifications

• SOC 2 Type II, ISO 27001, ISO 27701 (privacy), PCI DSS (if payments), etc.

Data protection regulations

• GDPR, CCPA/CPRA, HIPAA (if healthcare), LGPD, APAC equivalents.

Industry-specific standards

• SOX, HITRUST, FedRAMP (for U.S. government workloads), NIST cybersecurity framework alignment.

Data residency and cross-border data transfers

• Where data is stored, processed, and transferred; Standard Contractual Clauses (SCCs) if applicable.

Privacy program

• Data minimization, DPIA/PIA processes, DPIA documentation, data subject rights handling.

Audits and third-party assessments

• Availability of audit reports, frequency of audits, and how findings are remediated.

Security testing

• Penetration testing cadence, external auditor involvement, vulnerability management program.

Incident response

• RPO/RTO targets, incident notification timelines, and remediation procedures.

Access and identity controls

• SSO (SAML/OIDC), MFA, RBAC, and SCIM provisioning.