AI Agentic Analytics Platform

Project objective
Build a multi-tenant, agentic analytics SaaS platform where enterprise customers connect their data, let AI agents clean and govern it, analyze it, generate dashboards and reports, and activate insights into downstream communication and media systems. The platform should also include natural language querying, automated experiment design, recommendation engines, and agent templates so users can move from raw data to action faster.

Scope of work
1. Develop a secure multi-tenant SaaS architecture with tenant isolation, role-based access, audit logs, SSO/SCIM, usage metering, billing, and subscription management. Consumption-based pricing and run-level logging are common patterns in modern analytics platforms.
2. Build data connectivity and ingestion services for structured and unstructured sources, including databases, cloud warehouses, files, APIs, ad platforms, CRM systems, and communication platforms. The ingestion layer should support batch and near-real-time processing.
3. Create AI agent workflows for data profiling, cleaning, normalization, deduplication, transformation, and semantic tagging so data can be organized into reusable governed datasets. Agentic platforms increasingly emphasize automated documentation, quality, and reusable data products.
4. Implement a governance and metadata layer that manages lineage, classifications, permissions, policy enforcement, retention rules, and approval workflows. Governance controls should be available to both human administrators and agents.
5. Deliver analytics capabilities for descriptive, diagnostic, predictive, and prescriptive analysis, including self-service exploration, KPI tracking, anomaly detection, forecasting, and yield management. Modern analytics platforms already highlight anomaly detection, automation, and governed self-service as core capabilities.
6. Build customer journey analytics that maps touchpoints across channels, identifies paths, attrition points, conversion moments, and segment behavior, and produces journey maps and recommended interventions. This should support identity resolution and cross-channel deduplication where needed.
7. Add cross-channel media measurement to calculate deduplicated reach, frequency, overlap, incremental reach, lift, and channel contribution across media sources such as paid media, email, SMS, web, and social. Cross-channel measurement depends on linked exposures across channels and campaign-level frequency and outcome measurement.
8. Enable audience clustering and segmentation using AI-driven clustering, propensity scoring, similarity models, and audience scoring to support targeting, personalization, and optimization.
9. Provide dashboard, report, and narrative generation tools so agents can create executive summaries, operational reports, and embedded dashboards in reusable templates.
10. Integrate activation and optimization workflows so insights can be pushed to media buying, marketing automation, customer communication, and other execution platforms via APIs, webhooks, or orchestration protocols.
11. Include model management, explainability, evaluation, versioning, and human review so outputs are traceable and safe for enterprise use.
12. Natural language query. Users should be able to ask questions in plain English and have the platform translate them into governed queries, visualizations, and summaries. This should work across the customer’s approved datasets and respect permission boundaries. Natural language query support is a common feature in modern analytics environments and improves self-service adoption.
13. Recommendation engine. The platform should generate recommendations for campaign optimization, audience selection, channel allocation, budget shifts, operational actions, and revenue improvement. Recommendations should be explainable, ranked by confidence or expected impact, and optionally tied to experiments or lift measurements.
14. Automated experiment design. The system should support automated test planning, including holdouts, geo tests, time-based incrementality tests, channel pause tests, and budget reallocation studies. The agent should be able to propose the test design, identify required sample sizes or constraints, monitor the test, and summarize the lift.
15. Agent library. The product should include a library of reusable agents, workflows, connectors, and templates. Customers should be able to add approved modules for journey analytics, media measurement, forecasting, anomaly detection, and industry packs without custom development.

Functional modules
| Module | Required capabilities |
| Data ingestion | Connectors, file/API import, streaming, schema mapping, validation. |
| Data prep agents | Cleaning, deduplication, normalization, enrichment, tagging. |
| Governance | RBAC, lineage, catalog, policy engine, audit trail, approvals. |
| Analytics engine | KPI definitions, BI queries, anomaly detection, forecasting, clustering. |
| Journey intelligence | Touchpoint stitching, path analysis, journey maps, drop-off analysis. |
| Media measurement | Reach, frequency, overlap, lift, incrementality, audience exposure tracking. |
| Activation layer | APIs, webhooks, sync to ad/CRM/email/communications systems. |
| Reporting layer | Dashboards, scheduled reports, AI-written summaries, exports. |
| Monetization | Tenant plans, usage metering, quotas, overage billing, contract admin. |

Deliverables
- Product requirements document and technical architecture.
- Multi-tenant SaaS application with admin console and customer workspace.
- Data ingestion and governance framework.
- Agent orchestration layer with workflow definitions and approvals.
- Analytics, dashboard, and reporting experience.
- Customer journey and media measurement modules.
- Activation connectors and optimization workflows.
- Billing, usage metering, and licensing system.
- Security, compliance, logging, and audit documentation.
- QA test plan, deployment plan, and operational runbooks.

Nonfunctional requirements
- Enterprise-grade security, encryption in transit and at rest, and full auditability.
- Scalable architecture that supports variable usage and high-volume workloads.
- Explainable outputs with human override for sensitive actions.
- High availability, backup, disaster recovery, and monitoring.
- Compliance support for customer-specific regulatory obligations.

Commercial model
The platform should be sold as SaaS with usage-based billing tied to data volume, compute, active agents, API calls, or activation events. That model aligns with modern usage-based analytics products and supports customer growth without forcing a one-size-fits-all license.

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