Inferensys

Integration

AI for Cloud Storage and Collaboration Tool Discovery

Technical blueprint for integrating AI to automate the collection, analysis, and tagging of data from Microsoft 365, Google Workspace, Box, and Slack, preparing structured inputs for e-discovery platforms like Relativity and Everlaw.
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ARCHITECTURE FOR DATA DISCOVERY

Where AI Fits in the Pre-Ingestion Pipeline

AI agents that automate the discovery, collection, and initial analysis of data from cloud collaboration tools before it enters your e-discovery platform.

The most critical—and often most manual—phase of e-discovery happens before data hits Relativity, Everlaw, DISCO, or Nuix: identifying relevant custodians, scoping data sources, and collecting from platforms like Microsoft 365 (Teams, Exchange, SharePoint), Google Workspace, Box, and Slack. AI integration here focuses on automated custodian identification and intelligent collection workflows. An AI agent can analyze communication patterns, access logs, and project membership from these platforms via their APIs (e.g., Microsoft Graph, Slack Web API) to surface key individuals and data repositories, moving beyond simple HR org charts. This creates a dynamic, evidence-based custodian list and a targeted collection scope, reducing over-collection and initial data volume by 30-50% in many cases.

Once custodians and sources are identified, AI assists in the pre-processing and tagging of collected data. As files are pulled via API or enterprise connectors, a parallel AI pipeline can run lightweight analysis: performing initial language detection, PII/PHI scanning, sentiment or urgency flagging on communications, and concept clustering on document content. These pre-analysis tags (e.g., High_Sentiment_Email, Contains_PHI, Topic_Regulatory_Compliance) are embedded as custom metadata or written to a sidecar file. When the data is ingested into the e-discovery platform, these tags are mapped to platform-native fields or custom objects, giving reviewers a powerful head start. This architecture uses a queue-based system (like RabbitMQ or AWS SQS) to manage the flow between collection tools, AI services, and the final ingestion into the e-discovery platform's processing engine.

Rollout requires careful governance. AI models for custodian identification must be trained on historical matter data to recognize patterns relevant to your organization's litigation profile. The pre-ingestion pipeline should maintain a full audit trail of all AI-applied tags and decisions for defensibility. Start with a pilot on a single data source (e.g., Microsoft 365 Exchange) and a defined matter type. This approach, which we implement for clients, transforms a traditionally reactive, manual scoping process into a proactive, AI-driven workflow, ensuring the right data enters the review platform faster and with greater contextual intelligence. For a deeper look at how AI connects to the platform itself, see our guide on AI Integration for Relativity.

INTEGRATION SURFACES FOR CLOUD DATA DISCOVERY

AI Touchpoints Across Source Platforms

Microsoft Graph API Integration

AI agents connect to Microsoft 365 via the Microsoft Graph API, targeting specific workloads for legal discovery. The primary surfaces are Exchange Online (emails, calendar items), SharePoint Online (team sites, document libraries), and OneDrive for Business (user files).

Key integration patterns include:

  • Delta query endpoints for incremental collection of new or modified items, maintaining a sync state for ongoing legal holds.
  • Batch API calls to efficiently retrieve metadata and content from thousands of mailboxes or sites in a single request.
  • Search API to execute custodian-specific KQL queries, returning results that an AI agent can pre-analyze for relevance, privilege, or key topics before ingestion into the e-discovery platform.

AI workflows here focus on pre-tagging items with initial classifications (e.g., potentially_privileged, high_priority_custodian, contains_financial_terms) based on content analysis, reducing the manual triage load once data lands in Relativity or Everlaw.

CLOUD STORAGE & COLLABORATION TOOLS

High-Value Use Cases for AI-Powered Discovery

AI integration for Microsoft 365, Google Workspace, Slack, and Box transforms the initial data collection and preparation phase of e-discovery. By analyzing and tagging data before it enters platforms like Relativity or Everlaw, you accelerate downstream workflows and reduce manual pre-processing.

01

Automated Custodian Identification & Scope Triage

AI analyzes communication patterns, content volume, and organizational charts across M365, Gmail, and Slack to identify and rank key custodians for legal hold. It surfaces high-risk individuals and communication clusters, generating a prioritized custodian list for review teams, reducing the initial scoping phase from days to hours.

Days -> Hours
Scoping time
02

Pre-Ingestion PII/PHI Detection & Tagging

An AI agent scans files in Box, SharePoint, and Google Drive before they are ingested into the e-discovery platform. It identifies sensitive data (SSNs, credit card numbers, medical codes) and applies metadata tags for automatic routing to privileged review workflows or secure data rooms, ensuring compliance from the start.

Batch -> Real-time
Compliance screening
03

Slack & Teams Conversation Thread Reconstruction

AI reconstructs fragmented chat conversations from Slack and Microsoft Teams exports. It diarizes speakers, infers reply chains, and summarizes threads, outputting a structured, review-ready format. This transforms chaotic JSON exports into coherent timelines tagged by topic and sentiment for loading into Relativity or Everlaw.

1 sprint
Manual effort saved
04

Dynamic Data Sampling for Early Case Assessment

Instead of random sampling, AI performs semantic sampling across a collected data set in cloud storage. It identifies diverse topics, key dates, and anomalous communications to pull a representative, high-information sample for early case assessment in DISCO or Nuix, providing more accurate risk and cost forecasts.

05

Automated Document Family & Version Grouping

AI analyzes files in Google Docs, Word Online, and SharePoint to intelligently group document families and versions. It links final contracts to drafts, redlines, and related emails, creating a relationship graph. This metadata is exported as a load file, preserving critical context when ingested into the e-discovery platform's review interface.

Hours -> Minutes
Family grouping
06

Foreign Language Detection & Summary Translation

During the collection phase, AI scans cloud storage for non-English documents and chat messages. It detects language, provides a high-level English summary, and tags documents by language. This allows review managers to budget for translation and prioritize relevant documents before they enter the costly review phase in the e-discovery platform.

CLOUD DATA DISCOVERY FOR E-DISCOVERY

Example AI Agent Workflows

These workflows illustrate how AI agents can automate the collection, analysis, and preparation of data from Microsoft 365, Google Workspace, Box, and Slack, creating a structured, pre-analyzed feed for ingestion into platforms like Relativity or Everlaw.

Trigger: A new legal hold notice is created in the e-discovery platform or matter management system.

Agent Actions:

  1. Custodian Identification: The agent parses the hold notice to extract custodian names and date ranges.
  2. Cross-Platform Discovery: Using platform-specific APIs (Microsoft Graph, Google Workspace Admin SDK, Slack SCIM API), the agent enumerates all data sources for each custodian—email, OneDrive/Google Drive files, shared channels, Slack workspaces.
  3. Initial Scope & Risk Analysis: The agent performs a high-level analysis of data volume and types per custodian, flagging potential challenges (e.g., terabytes in a shared drive, high prevalence of video files).
  4. Preservation Workflow Initiation: The agent triggers platform-native hold functions via API or generates precise, auditable collection scripts for the IT/security team to execute.
  5. Output to E-Discovery Platform: A structured manifest (CSV/JSON) is pushed to the e-discovery platform, creating placeholder custodian records tagged with estimated volume, key data sources, and collection priority scores.

Human Review Point: Legal team reviews the agent-generated scope report and custodian priority ranking before authorizing full collection.

COLLECTION AND PRE-ANALYSIS PIPELINE

Implementation Architecture: Data Flow and System Design

A secure, automated pipeline to collect, analyze, and tag data from cloud collaboration tools before ingestion into e-discovery platforms.

The integration begins with a secure connector layer that interfaces with source APIs—Microsoft Graph API for Microsoft 365, Google Workspace Admin SDK and Drive API, Box API, and Slack Web API. This layer handles authentication (OAuth 2.0, service accounts), incremental data syncs, and change detection to collect target data sets (mailboxes, OneDrive/Drive files, channels, shared links) under legal hold. Data is streamed into a temporary, encrypted staging area, never persisting raw data outside the client's controlled environment.

A processing engine then applies a series of AI models to the staged data. This includes: Named Entity Recognition (NER) for PII/PHI; topic modeling and concept clustering to identify key discussion themes in emails and chats; sentiment and urgency scoring on communications; and relationship graphing to map custodian interactions across platforms. Results are transformed into a standardized tag schema (e.g., PII_Present: TRUE, Primary_Topic: "Budget Negotiations", Key_Custodian: Jane Doe). This pre-analysis metadata is packaged alongside the native files into a load file (CSV, DAT) formatted for the target e-discovery platform (Relativity, Everlaw, DISCO, Nuix).

The final step is automated ingestion via the e-discovery platform's processing API or watched import folder. The pre-generated tags are mapped to platform-native fields—Custom Objects in Relativity, Smart Tags in Everlaw, DISCO Tags, or Nuix Metadata—immediately populating the review workspace with AI-derived intelligence. This architecture shifts hours of manual first-pass review and tagging to an automated, pre-ingestion step, allowing legal teams to start their substantive review on a pre-analyzed, prioritized document set from day one.

AI-ENABLED DATA COLLECTION PIPELINES

Code and Payload Examples

Ingesting and Pre-Tagging M365 Data

Use the Microsoft Graph API to programmatically collect emails, Teams messages, and SharePoint documents for e-discovery. An AI agent can process this stream in real-time, applying initial tags for relevance, privilege, and key topics before the data lands in Relativity or Everlaw.

Example Python call to fetch mail and run pre-analysis:

python
import requests
import json

# Fetch recent emails from a custodian
graph_endpoint = 'https://graph.microsoft.com/v1.0/users/{custodian_id}/messages'
headers = {'Authorization': 'Bearer {access_token}'}
params = {
    '$top': 100,
    '$select': 'subject,body,from,toRecipients,createdDateTime',
    '$orderby': 'createdDateTime DESC'
}

response = requests.get(graph_endpoint, headers=headers, params=params)
emails = response.json().get('value', [])

# Send batch to AI service for pre-tagging
ai_payload = {
    "documents": [
        {
            "id": msg['id'],
            "text": msg['body']['content'],
            "metadata": {
                "source": "m365_mail",
                "author": msg['from']['emailAddress']['address'],
                "date": msg['createdDateTime']
            }
        } for msg in emails
    ],
    "analysis_types": ["relevance_score", "privilege_indicators", "topic_clusters"]
}

# AI service returns tags for each document
# Map results to platform-specific import format (e.g., Relativity load file)

This pipeline reduces the time-to-first-review by performing initial AI triage during collection, populating native platform fields like Custodian, Date, and custom fields like AI_Relevance_Score upon ingestion.

AI-ENHANCED DATA COLLECTION FOR E-DISCOVERY

Realistic Time Savings and Operational Impact

This table illustrates the operational impact of integrating AI into the data collection and preparation phase from cloud collaboration tools, before ingestion into platforms like Relativity or Everlaw.

Workflow PhaseTraditional Manual ProcessAI-Assisted ProcessKey Impact Notes

Custodian Identification & Scope

Manual interviews and email sampling over 1-2 weeks

AI analysis of communication patterns to propose key custodians in 1-2 days

Reduces initial scoping cycle by ~80%; focuses human effort on validation.

Data Preservation & Legal Hold

Manual list management; risk of missed custodians

Automated custodian list sync from AI analysis; trigger-based hold notices

Minimizes preservation risk; ensures defensible, consistent process.

Cloud Data Collection (M365, Slack, Box)

Manual export requests per custodian; complex filtering

API-driven collection targeted by date, domain, and AI-identified topics

Cuts collection setup time from days to hours; reduces data volume by 30-50%.

File Processing & Preliminary Triage

Batch processing; uniform OCR; manual spot-checks for issues

AI-powered file type detection, enhanced OCR, and pre-tagging for PII/Priority

Flags critical documents and problems early; improves downstream review efficiency.

Data De-duplication & Threading

Platform-native algorithms applied to full dataset

AI-enhanced email threading and near-duplicate detection post-collection

Creates more intelligent families and threads, reducing reviewer items by 15-25%.

Pre-Ingestion Tagging & Metadata Enrichment

Limited to system-extracted metadata (date, author, type)

AI applies preliminary issue tags, sentiment scores, and custom metadata fields

Provides a 'running start' for review teams; enables immediate strategic searches.

Load File Preparation & QC

Manual validation of Bates ranges, family relationships, and fields

AI agents run automated checks for consistency, gaps, and formatting errors

Reduces pre-production QC time from a full day to 2-3 hours; minimizes re-work.

Platform Ingestion & Project Setup

Sequential: ingest, then build review workflows

Parallel: AI pre-analysis feeds directly into workspace template and workflow rules

Enables review to begin with prioritized batches on day one of platform access.

ARCHITECTING FOR SCALE AND COMPLIANCE

Governance, Security, and Phased Rollout

A secure, governed approach to integrating AI into the data collection pipeline for e-discovery.

This integration operates at the critical intersection of legal compliance and enterprise data. The AI agent is deployed as a secure intermediary layer between your cloud storage systems (Microsoft 365, Google Workspace, Box, Slack) and your e-discovery platform (Relativity, Everlaw, DISCO). It never stores collected data permanently. Instead, it processes data streams in-memory or via encrypted temporary storage, applying AI for pre-analysis—like identifying key custodians, detecting privileged communications, or tagging data types—before securely pushing enriched metadata and tagged documents into the e-discovery platform's processing queue via its API. All actions are logged with full audit trails, linking AI-generated tags back to the source data and the specific model version used.

A phased rollout is critical for managing risk and proving value. We recommend starting with a pilot matter involving a single data source, such as a specific Microsoft 365 SharePoint site or Google Workspace team drive.

  • Phase 1 (Pilot): The AI agent is configured to run in a "human-in-the-loop" review mode. It suggests tags (e.g., Potential Privilege, Key Financial Term, Relevant to Custodian X) which a senior reviewer must approve before they are written to the e-discovery platform. This builds trust in the AI's accuracy and establishes baseline metrics for time saved.
  • Phase 2 (Expansion): After validation, the agent moves to automated tagging for low-risk categories (e.g., language identification, document type, date extraction) while keeping high-stakes tags (privilege, responsiveness) in review mode. The scope expands to include additional data sources like Slack channels or Box folders.
  • Phase 3 (Production): The fully tuned agent operates with conditional automation. Rules-based governance determines which tags are auto-applied based on confidence scores and matter type. The system feeds performance data (precision/recall) back to the operations team for continuous model refinement.

Governance is enforced through technical controls and process integration. Access to the AI agent is managed via RBAC, aligning with existing e-discovery platform permissions. All AI-generated outputs are stamped with provenance metadata—including the prompt version, model ID, and processing timestamp—ensearing defensibility. The system is designed for policy-aware execution; for example, it can be configured to automatically exclude data from certain jurisdictions or apply specific tagging rules for healthcare or financial data based on the matter's compliance profile. This architecture ensures the AI augments the process without creating new chain-of-custody or data privacy risks, making the results auditable and admissible.

Enabling Efficiency, Speed & Accuracy

Intelligent Analysis, Decision & Execution

We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.

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Search across company data

Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.

Useful when people spend too long searching or get different answers from different systems.

Enterprise searchRAGPermissions
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Automate internal workflows

Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.

Useful when repetitive work moves across multiple tools and teams.

AI agentsWorkflow automationGovernance
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Add AI to products and internal tools

Build assistants, guided actions, or decision support into the software your team or customers already use.

Useful when AI needs to be part of the product, not a separate tool.

AI integrationDecision supportModel routing
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AI FOR CLOUD DATA DISCOVERY

Frequently Asked Questions

Practical questions for legal and IT teams planning AI integration to collect, analyze, and prepare data from Microsoft 365, Google Workspace, Box, and Slack for e-discovery ingestion.

AI integrates via the platform's APIs and, when necessary, sanctioned third-party connectors. The architecture typically involves:

  1. Authentication & Scoping: Using OAuth 2.0 or service accounts with least-privilege access (e.g., Microsoft Graph API, Google Workspace Admin SDK, Box API, Slack Web API). The AI system first maps the custodian's accessible data sources—Mail, Drive, Teams/Channels, Shared Drives, etc.
  2. Intelligent Collection: Instead of a bulk export, AI agents can perform targeted collection by:
    • Analyzing communication patterns to identify relevant custodians and date ranges.
    • Using keyword/concept seeds to prioritize data locations (e.g., specific SharePoint sites over entire OneDrive).
    • Filtering out known non-relevant data types (e.g., system-generated alerts, public news channels) at the source.
  3. On-the-Fly Analysis: As data is streamed via API, a processing pipeline applies initial AI analysis:
    • Language Identification & OCR: For files lacking text.
    • PII/PHI Detection: Flags sensitive content for special handling.
    • Concept Tagging: Applies preliminary issue tags (e.g., mentions_product_x, tone_escalated) based on the matter's themes.
  4. Structured Output: The system outputs a processed, searchable dataset with metadata and preliminary tags into a staging area (e.g., Azure Blob Storage, S3) formatted for ingestion into Relativity, Everlaw, or DISCO.
Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.

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How We Work

Custom AI workflows for your Business

One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.

01

Review the use case

We understand the task, the users, and where AI can actually help.

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02

Pick the right approach

We define what needs search, automation, or product integration.

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03

Build the first useful version

We implement the part that proves the value first.

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04

Improve from there

We add the checks and visibility needed to keep it useful.

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The first call is a practical review of your use case and the right next step.

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