Advanced Data Infrastructure & AI Integration

Data Storage with Connectors
Foundational Storage and Processing System for dlea.io / Revfinity OS

Overview
This project will deliver the foundational storage and processing infrastructure for dlea.io / Revfinity OS. The system will manage both structured (CRM, internal app data) and unstructured (calls, documents, notes) data. The architecture supports AI capabilities including contextual chat, pattern recognition, enablement ticketing, and KPI dashboards. It will utilize Azure PostgreSQL for structured data and Azure Cosmos DB for unstructured data, with support for vector embeddings and agent-accessible query layers.

Scope of Work
Structured Data – Azure PostgreSQL

Provision Azure-hosted PostgreSQL with multi-tenancy and RBAC

Design and implement schemas for:

CRM Data: Accounts, Contacts, Opportunities

Internal Modules: Sales Plans, Users, Enablement Tickets, KPIs

Build and integrate ELT pipeline: Azure Blob → Synapse → PostgreSQL

Enable real-time read/write access for:

Internal APIs

AI agents (via NLP-to-SQL adapters)

Dashboards

Unstructured Data – Azure Cosmos DB

Configure partitioned NoSQL collections in Azure Cosmos DB

Define JSON schema and metadata model for unstructured documents

Ingest and store:

Call transcripts and notes from Gong and Apollo

Documentation from Confluence, Dropbox, Google Drive

Enablement materials from Seismic

Apply vector embeddings (OpenAI/Azure) for semantic indexing

Provide retrieval access for:

AI agents (RAG, TEE, PRE)

Contextual chat and pattern matching layers

AI Agent & Query Abstraction

Build query abstraction layer to prevent direct DB access by agents

Use consistent identifiers (e.g., user_id, call_id) to link structured and unstructured data

Enable agent-driven functionality:

Triggering enablement tickets

Retrieving contextual training content

Surfacing sales and coaching insights

Dashboards & Reporting

Merge data sources to generate unified insights

Optimize queries and caching mechanisms for performance

Build dashboards displaying:

Sales KPIs

Training progress and completion rates

Call quality trends

Agent-level productivity insights

Validation & QA

Test and validate five core data flows:

CRM data synchronization

Call ingestion and transcript storage

Document embedding and search accuracy

Enablement ticket creation logic

Dashboard rendering and data accuracy

Timeline & Milestones – 4 Weeks
Week 1: Infrastructure Setup & Schema Design
Milestone 1: Core Architecture Setup

Set up Azure PostgreSQL and Cosmos DB

Finalize relational and NoSQL schema design

Establish metadata tagging structure for unstructured data

Week 2: Data Ingestion Pipelines
Milestone 2: ETL/ELT & API Integration

Connect Salesforce/HubSpot to PostgreSQL via ELT pipeline

Ingest call data from Gong and Apollo

Pull content from Confluence and Dropbox

Tag, structure, and store all data in Cosmos DB

Week 3: AI Access Layer & Vector Search
Milestone 3: Agent Integration and Vector Indexing

Implement NLP query abstraction for structured/unstructured data

Apply OpenAI/Azure vector embeddings for semantic search

Configure Cosmos DB change feed for downstream triggers

Week 4: Dashboards, Reporting & QA
Milestone 4: Reporting Interfaces and System Validation

Build and test KPI dashboards

Ensure data accuracy and consistency across sources

Conduct QA for all ingestion and access flows

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