Skip to main content

ChainAlign Enterprise Crawl

Version: 1.0

1. Introduction​

1.1. Purpose​

This document provides the functional and non-functional requirements for the ChainAlign Enterprise Crawl, a decision intelligence platform. It consolidates multiple architectural drafts, strategic analyses, and technical playbooks into a single, authoritative source for the project. The objective is to guide the development, deployment, and evolution of the system.

1.2. Executive Summary​

The modern enterprise requires immediate, holistic, and auditable decision-making capabilities to maintain a competitive edge. Traditional data integration methods (ETL) are too slow, costly, and opaque, creating a critical lag between operational events and strategic response.

The ChainAlign Enterprise Crawl is a paradigm shift away from slow, monolithic integration projects. It is a virtualized intelligence layer designed to deliver production-ready decision support in 5 days, not 12-18 months.

By combining strategic data extraction, in-memory analytics, AI-driven reasoning, and immutable governance, the system provides planners and executives with the ability to model complex scenarios using near real-time data. This approach transforms the planning function from reactive reporting into a proactive, continuous value-optimization loop, enabling users to move from asking "Can we supply this demand?" to "What is the most profitable and compliant plan?"

1.3. Scope​

This FSD covers the initial 5-day pilot deployment and the subsequent 60-90 day refinement period. The scope includes:

  • Data Sources: Initial focus on structured data from Enterprise Resource Planning (ERP) systems (e.g., SAP S/4HANA) and unstructured data from document repositories (e.g., SharePoint, emails).
  • Core Functionality: Data extraction, analytical processing, scenario simulation (Monte Carlo), AI-driven narrative generation, compliance guardrails, and audit logging.
  • Deployment: A self-contained, portable solution deployable on a single virtual machine using Docker.

1.4. Target Audience​

This document is intended for:

  • Software Engineers & Architects: To understand the technical requirements and build the system.
  • Product Managers: To align development with strategic goals and user needs.
  • On-site Deployment Teams: As a playbook for the 5-day installation and go-live process.
  • Executive Stakeholders: To understand the project's value proposition, timeline, and success criteria.

2. Guiding Principles & Philosophy​

The architecture is founded on a set of core principles that differentiate it from traditional enterprise software.

2.1. Speed-to-Value is the Product​

Our primary competitive advantage is delivering production-ready intelligence in 5 days. This is achieved by prioritizing immediate value over initial perfection.

2.2. Progressive Precision​

The system evolves in maturity over time while delivering value from day one.

  • Day 5: Useful. The system is live with 80% data accuracy, covering the top 20% of strategic decisions.
  • Day 30: Valuable. Accuracy increases to 90%, coverage expands, and the system has prevented its first compliance violations.
  • Day 60: Indispensable. Accuracy exceeds 95%, the system is embedded in daily workflows, and the captured decision history becomes a proprietary asset.

2.3. Retrieve Strategically, Analyze Locally​

Pure data virtualization is insufficient for complex analytics. Our hybrid model is:

  • Retrieve Strategically: Extract and copy only the data relevant to high-stakes decisions into a decision-optimized local database.
  • Analyze Locally: Perform all heavy analytical workloads (forecasting, Monte Carlo simulations) on the local data copy for maximum speed and performance.
  • Query Virtually (for lookups): Use data virtualization (e.g., Trino) for low-latency lookups of data not required for intensive analysis.

2.4. Non-Invasive & Read-Only​

The system connects to source systems using read-only credentials and least-privilege access, minimizing risk and removing the primary objection to enterprise software pilots.

2.5. Governance and Auditability by Design​

Every query, scenario, and decision is logged in an immutable, cryptographically-signed audit trail. Compliance rules are enforced as "guardrails" that prevent the AI from recommending non-compliant actions. This provides "decision insurance" and satisfies regulatory requirements.

3. System Architecture​

3.1. High-Level Architecture​

The system is composed of four logical layers that process data from extraction to decision support.

┌─────────────────────────────────────────────────────────────┐
│  LAYER 1: STRATEGIC DATA EXTRACTION (Day 1-2)               │
│  ────────────────────────────────────────────────────────── │
│  Goal: Get decision-relevant data INTO ChainAlign DB        │
│  Components: Extraction Orchestrator, SAP Extractors, Doc Ingestors│
│  Output: Populated local PostgreSQL DB (Orders, Materials, etc.)│
└─────────────────────────────────────────────────────────────┘
               ▼
┌─────────────────────────────────────────────────────────────┐
│  LAYER 2: ANALYTICAL PROCESSING (Day 3)                     │
│  ────────────────────────────────────────────────────────── │
│  Goal: Transform raw data into decision-ready features      │
│  Components: Feature Engineering, Forecasting, Enrichment   │
│  Output: Analytical Views (order_features, demand_forecast) │
└─────────────────────────────────────────────────────────────┘
               ▼
┌─────────────────────────────────────────────────────────────┐
│  LAYER 3: REASONING & SIMULATION (Day 4)                    │
│  ────────────────────────────────────────────────────────── │
│  Goal: Generate scenarios using local analytical data       │
│  Components: Monte Carlo Engine, LLM Reasoner, Guardrails   │
│  Output: Scenarios API with probabilistic outcomes          │
└─────────────────────────────────────────────────────────────┘
               ▼
┌─────────────────────────────────────────────────────────────┐
│  LAYER 4: UI & REFRESH (Day 5+)                             │
│  ────────────────────────────────────────────────────────── │
│  Goal: Present insights and keep data current               │
│  Components: React UI, Data Refresh Scheduler, Monitoring   │
│  Output: Live, production-ready decision intelligence system│
└─────────────────────────────────────────────────────────────┘

3.2. Core Components & Technology Stack​

PurposeRecommended StackDeployment
OrchestrationCron, APScheduler (or lightweight Airflow)Docker Container
Data ExtractionPython scripts (Pandas, Requests)Docker Container
Local DatabasePostgreSQL 15+ with pgvector extensionDocker Container
API LayerFastAPI (Python)Docker Container
CachingRedisDocker Container
Data VirtualizationTrino (for optional live lookups)Docker Container
AI/ML - EnrichmentSentence-Transformers (local)api Container
AI/ML - ReasoningLLM API (e.g., Anthropic, Gemini) or Local ModelExternal or api Container
AI/ML - ForecastingProphet (or similar time-series library)api Container
Frontend UIReact, Tailwind CSSServed via FastAPI or Nginx
DeploymentDocker ComposeSingle VM / Laptop

4. Functional Requirements​

4.1. Data Extraction & Ingestion (FR-01)​

  • FR-01.1: SAP Data Extraction: The system must provide multiple, reality-checked strategies for extracting read-only data from SAP systems. The priority is speed-to-value.
    • FR-01.1.1: Replica Access: Connect to existing data warehouse replicas (Snowflake, BigQuery, etc.) via JDBC.
    • FR-01.1.2: Excel Automation: Ingest .xlsx files automatically exported by users to a shared folder.
    • FR-01.1.3: GUI Automation: (Fallback) Use SAP GUI scripting (RPA) to extract data from transactions if APIs are unavailable.
    • FR-01.1.4: OData/API Access: Connect to standard SAP OData APIs or CDS Views (long-term target).
  • FR-01.2: Unstructured Data Ingestion: The system must ingest documents from sources like SharePoint, Google Drive, and local file shares.
  • FR-01.3: Strategic Filtering: The ingestion process must apply a strategic_score to prioritize artifacts related to high-stakes decisions (e.g., containing executive titles, financial thresholds, or regulatory keywords). Only high-scoring artifacts are fully processed initially.
  • FR-01.4: Data Refresh: The system must support configurable, incremental data refreshes.
    • FR-01.4.1: High-frequency data (e.g., open orders) should be refreshed every 15-60 minutes.
    • FR-01.4.2: Low-frequency data (e.g., material masters) should be refreshed daily.

4.2. Data Modeling & Storage (FR-02)​

  • FR-02.1: Decision-Optimized Schema: Data must be stored in a local PostgreSQL database using a denormalized, decision-optimized schema, not a mirror of the source ERP schema. Key tables include orders, materials, capacity_allocations, and decision_artifacts.
  • FR-02.2: Vector Storage: Text embeddings for documents and other artifacts must be stored in a pgvector column within the database to enable semantic search.

4.3. Analytical Processing & Feature Engineering (FR-03)​

  • FR-03.1: Feature Engineering: The system must transform raw extracted data into decision-ready features (e.g., margin_pct, delivery_urgency, compliance_risk_score) and store them in materialized views like order_features.
  • FR-03.2: Demand Forecasting: The system must generate time-series demand forecasts for critical materials using historical order data.
  • FR-03.3: Semantic Enrichment: The system must use LLMs to enrich data by extracting entities, classifying data domains, and tagging risk-relevant features (e.g., PFAS, ITAR).

4.4. Reasoning & Simulation Engine (FR-04)​

  • FR-04.1: Scenario Generation API: The system must expose a REST API endpoint (/api/scenarios/generate) that accepts a decision context (e.g., "compare Order A vs. Order B") and returns a set of evaluated scenarios.
  • FR-04.2: Monte Carlo Simulation: For decisions involving uncertainty, the system must run a Monte Carlo simulation (e.g., 10,000 iterations) against the local data to generate probabilistic outcomes (e.g., confidence ranges, probability of profit).
  • FR-04.3: LLM Narrative Generation: The system must use an LLM to synthesize simulation results, contextual data, and compliance warnings into a human-readable narrative for each scenario.
  • FR-04.4: Socratic Inquiry & Learning: The system must detect decision artifacts (e.g., emails stating "we chose A over B") and be capable of prompting users for the underlying rationale. This captured human judgment must be stored and indexed to improve future recommendations.

4.5. Governance & Security (FR-05)​

  • FR-05.1: Immutable Audit Log: Every API request, scenario generation, and user action must be logged to an append-only audit_log table. Log entries must include user ID, timestamp, request context, and a hash of the result for tamper-proofing.
  • FR-05.2: Compliance Guardrails: The system must implement a rule engine to enforce business and regulatory constraints.
    • FR-05.2.1: The system must be able to block the recommendation of scenarios that violate hard constraints (e.g., using a banned substance for a specific customer).
    • FR-05.2.2: The system must flag scenarios that violate soft constraints (e.g., dipping below a target margin floor).
  • FR-05.3: Role-Based Access Control (RBAC): The system must support role-based access, configurable at the API layer (e.g., via Hasura or FastAPI middleware).
  • FR-05.4: Authentication: The system must integrate with standard enterprise SSO providers (SAML/OIDC).

4.6. User Interface (FR-06)​

  • FR-06.1: Scenario Comparison View: The UI must present generated scenarios side-by-side in a card-based layout, allowing for easy comparison.
  • FR-06.2: Probabilistic Outcomes: Financial impacts must be displayed with probabilistic context (e.g., confidence ranges, progress bars for probability of profit).
  • FR-06.3: Compliance & Risk Display: Compliance warnings and risks must be clearly and prominently displayed on each scenario card. Blocked scenarios should be visually distinct.
  • FR-06.4: Data Provenance: The UI must display data freshness information (e.g., "Data as of 10:15 AM") to maintain user trust.
  • FR-06.5: Audit Log Viewer: An interface must be provided for authorized users (e.g., compliance officers) to search and review the audit log.

4.7. Monitoring (FR-07)​

  • FR-07.1: Health Check Endpoint: The system must provide a /health endpoint that reports the status of all critical components (database, data freshness, scheduler).
  • FR-07.2: Metrics Endpoint: The system must provide a /metrics endpoint with key performance indicators in a Prometheus-compatible format (e.g., scenario latency, data staleness, API error rates).

5. Non-Functional Requirements​

5.1. Performance (NFR-01)​

  • NFR-01.1: Scenario Generation Latency: P95 latency for the /api/scenarios/generate endpoint must be < 5 seconds.
  • NFR-01.2: Semantic Search Latency: P95 latency for semantic search queries must be < 3 seconds.
  • NFR-01.3: Data Extraction Throughput: The system must be able to extract and process at least 50,000 order records in under 15 minutes.

5.2. Scalability (NFR-02)​

  • NFR-02.1: Pilot Scale: The initial single-VM architecture must support up to 20 concurrent users, 100,000 indexed documents, and 1 million order records.
  • NFR-02.2: Production Path: The architecture must have a clear path to scale horizontally by migrating from Docker Compose to a container orchestration platform like Kubernetes.

5.3. Reliability (NFR-03)​

  • NFR-03.1: Fallbacks: Every critical subsystem must have a documented failure mode and recovery path. For example, if live API access fails, the system must fall back to the last successful data snapshot.
  • NFR-03.2: Uptime: The system should target 99.9% availability during business hours after the initial pilot phase.

6. 5-Day Pilot Deployment Playbook​

This playbook outlines the aggressive but achievable plan to deploy a production-ready system in one week.

Day 0: Pre-Flight Preparation​

  1. Send SAP Access Request: Email the customer's IT team with the 5 ranked SAP access options, recommending the fastest path for Day 1.
  2. Acquire Credentials: Obtain read-only service accounts for SAP (or user credentials for automation), SharePoint, and other data sources.
  3. Provision VM: Provision a cloud VM or prepare a physical laptop with Docker and Docker Compose installed.

Day 1: Infrastructure & Core Data Extraction​

  • Goal: Get 80% of decision-relevant data into the local ChainAlign database.
  1. Deploy Stack: Run docker-compose up -d to start PostgreSQL, Redis, FastAPI, etc.
  2. Deploy Schema: Apply the decision-optimized SQL schema to the PostgreSQL database.
  3. Execute First Extraction: Run the extraction script for the top 3-5 most critical SAP tables (e.g., Orders, Materials, Suppliers) using the fastest available access method.
  4. Verify: Confirm that data is present and queryable in the local database.

Day 2: Complete Extraction & Document Ingestion​

  • Goal: Ingest remaining core tables and high-value strategic documents.
  1. Extract Remaining Tables: Run extractors for secondary SAP tables (e.g., Capacity, BOMs).
  2. Ingest Strategic Documents: Run the document ingestor, which crawls SharePoint/email, applies the strategic_score, and processes only the top ~10-20% of documents.
  3. Generate Embeddings: Create and store vector embeddings for all ingested text content.

Day 3: Analytical Processing & Feature Engineering​

  • Goal: Transform raw data into decision-ready analytical features.
  1. Run Feature Engineering: Execute SQL scripts to create the order_features materialized view.
  2. Generate Forecasts: Run the Python script to generate initial demand forecasts for the top 50 materials.
  3. Enrich Data: Perform an initial batch enrichment of data using the LLM for tagging and classification.

Day 4: Reasoning Engine & Simulation​

  • Goal: Enable the system to generate scenarios using local data.
  1. Implement Simulation: Finalize and test the Monte Carlo simulation engine.
  2. Integrate LLM: Connect the simulation engine to the LLM reasoning layer to generate narratives.
  3. Deploy Guardrails: Activate the compliance guardrail engine.
  4. Test API: Run end-to-end tests on the /api/scenarios/generate endpoint, ensuring it returns probabilistic and compliant scenarios in under 5 seconds.

Day 5: UI, Refresh, & Go-Live​

  • Goal: Go live with a production system used by real end-users.
  1. Deploy UI: Connect the React frontend to the live API backend.
  2. Configure SSO: Integrate with the customer's SSO provider for authentication.
  3. Activate Refresh Scheduler: Start the automated data refresh jobs (e.g., 15-minute refresh for orders).
  4. Handoff & Train: Conduct a handoff session with the customer team, onboard the first users, and have them generate their first live scenario.
  5. Monitor: Confirm that the /health and /metrics endpoints are operational.

7. Success Metrics​

7.1. Day 5 Go-Live Checklist​

The pilot is considered a success if the following are true by the end of Day 5:

  • At least one senior planner can successfully generate a scenario using real data.
  • The generated scenario includes a valid compliance check.
  • The action is logged correctly in the immutable audit trail.
  • The system is accessible via the customer's SSO.
  • The data refresh scheduler is running.

7.2. Day 60 "Indispensable" Metrics​

The system is considered indispensable if the following are true by Day 60:

  • Adoption: >10 daily active users.
  • Usage: >25 scenarios generated per day.
  • Value: >5 documented compliance violations prevented or high-impact decisions improved.
  • Accuracy: Scenario accuracy is rated as "insightful" or "couldn't do this manually" by planners.
  • Data Freshness: All critical data paths have been migrated from initial fast-access methods to optimal, near real-time APIs (e.g., OData).

8. Appendix​

8.1. Key Data Models​

orders Table​

CREATE TABLE orders (
  id uuid PRIMARY KEY,
  source_system text,
  source_id text,
  customer_id text,
  material_id text,
  quantity numeric,
  net_value numeric,
  margin_pct numeric,
  requested_delivery_date date,
  priority_tier text,
  compliance_flags jsonb,
  extracted_at timestamptz
);

decision_artifacts Table​

CREATE TABLE decision_artifacts (
  id uuid PRIMARY KEY,
  artifact_type text, -- 'email', 'doc', 'meeting_note'
  title text,
  content text,
  decision_maker text,
  reasoning_extracted text, -- Socratic learning result
  embedding vector(1536),
  extracted_at timestamptz
);

audit_log Table​

CREATE TABLE audit_log (
  id uuid primary key,
  user_id text,
  event_type text,
  context jsonb,
  result_hash text,
  source_systems text[],
  timestamp timestamptz
);

8.2. API Endpoint Example​

Request: POST /api/scenarios/generate

{
  "decision_type": "capacity_allocation",
  "order_a_id": "uuid-for-order-A",
  "order_b_id": "uuid-for-order-B",
  "user_id": "planner@company.com"
}

Response: A JSON object containing an array of scenarios, each with financial impact, risk analysis, compliance checks, and a recommendation. (See detailed example in source documents).