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ChainAlign Build Log — Weeks 1 → 7

· 5 min read
Pramod Prasanth
Pramod Prasanth
Founder

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🎤 ChainAlign Build Log — Weeks 1 → 7​

(Sept 6 – Oct 26, 2025)

Purpose: A transparent record of how ChainAlign evolved from an initial commit to an AI platform that I am about to demo to potential customers. This documents the progress and unique architectural choices made as a solo-founder. While, I am a solo-founder, I feel I am working with a team as building with Gemini, ChatGPT and Claude I feel I have a strong team who can help me brainstrom ideas, dive deeper into topics, read reasearch papers together, plan, write FSDs, and then build, test, document. Hence I feel its a team effort and hence the use of "we."

🗓️ Week 1: Foundations, Data, and The Name (Sept 6–10)​

Summary: The foundational architecture was defined and deployed. The team moved instantly from idea to code, integrating core data connectors and choosing the project's permanent identity.

Key Updates:

  • Architecture Setup: Completed initial project setup, configuration, and scaffolding for the multi-tenant core.
  • Data Services: Integrated initial Google Sheets API and developed the STT (Speech-to-Text) backend.
  • Initial Milestones: Implemented Phase 1: Data Onboarding and Phase 2: Intelligent Ingestion Engine.
  • Project Identity: Project formally renamed from opsPilot to ChainAlign.

Founder Insight: Speed is crucial. By the end of this week, we had core data ingestion and the full multi-tenant architecture in place, ready for the intelligence layers.

🗓️ Week 2: Intelligence Engine & Migrations (Sept 11–17)​

Summary: The core AI layers were launched, including the GraphRAG foundation and the Adaptive UX engine. This was balanced with tackling infrastructure debt, specifically the CJS/ESM module conflicts.

Key Updates:

  • Core AI Launch: Completed the AI Insights Service and laid the groundwork for the Constraint Intelligence Engine.
  • Adaptive UX: Implemented the InteractionLearningEngine and AdaptivePageGenerator to predict user detail preference and dynamically render the dashboard.
  • RAG Foundation: Initial setup for the modular RAG architecture and core PDF ingestion pipeline.
  • Database Stabilization: Refactored and cleaned all core database migrations (M14).

Founder Insight: Complexity demands rigor. While RAG was being built, we proactively tackled hard module conflicts (Jest/ESM) to ensure long-term stability and faster future development.

🗓️ Week 3: Governance, Compliance, and Trust (Sept 18–28)​

Summary: Architectural focus shifted entirely to security, auditability, and enterprise adoption. This phase defined the AI Firewall and compliance layer.

Key Updates:

  • Core Compliance (E-Layer): Implemented PII Data Minimization, full Role-Based Access Control (RBAC), and the Immutable Audit Trail.

  • AI Firewall Architecture: Built the architecture to enforce centralized LLM routing and deployed the Redaction Engine core1111.

  • Platform Stabilization: Resolved cascading container build errors and finalized database connections.

  • Product Flow: Completed CSV Onboarding and Product Review Dashboard components.

Founder Insight: You can’t sell AI to an enterprise without a CISO's sign-off. The security and audit layer is the real moat, turning AI from a risk into a governed asset2222.

🗓️ Week 4: The Solo Slog & Test Debt (Sept 29–Oct 12)​

Summary: This period focused on cleaning up technical debt and preparing for the major forecasting milestones, while continuing to harden the platform.

Key Updates:

  • Test Suite Overhaul: Completed the backend migration from Jest to Vitest, achieving a 70% test pass rate despite major ESM/CJS conflicts.
  • Forecasting Prep (M26): Implemented initial steps for the Hybrid Forecasting Service (M26) and the Shadow AI Defense layer (M25).
  • Decision OS Backend: Implemented the Judgment Engine DB schema foundation and integrated access control into the Data Abstraction Layer (DAL).
  • Refactoring: Finalized the Repository Pattern Refactoring across multiple services for scalability.

Founder Insight: Fighting CJS/ESM hell was painful, but necessary. That 70% test pass rate is now a solid foundation, not a pile of legacy debt, allowing us to accelerate safely.

🗓️ Week 5: The Decision-Intelligence Milestone (Oct 13–22)​

Summary: A massive wave of development finalized the Decision OS, launched the forecasting core, and introduced critical performance improvements.

Key Updates:

  • Decision OS Complete (M36): Finalized the Judgment Engine. Completed the Backend API for Scenarios & Decisions and the Web UI for Decision Workflow.
  • CRITICAL FIX: Implemented local redaction fallback to AIGateway to prevent PII leaks.
  • Latency Optimization: Implemented pre-built page caching with live overlays for latency optimization (40-60x faster).
  • External Data Integration (M38): Launched Weather Hindcasting and Economic Indicators to integrate external data into forecasting models.

Founder Insight: We achieved production speed and security. Moving beyond features, we are solving enterprise-grade MLOps problems and ensuring the platform is fast enough for C-level users.

🗓️ Week 6: Hybrid Forecasting & Learning Loop (Oct 23–26)​

Summary: The final push brought the adaptive forecasting architecture online and delivered high-value integrations for the CatobuildCA workflow.

Key Updates:

  • Hybrid Forecasting Core (M26/M42): Finalized the architecture for Prophet XGBoost hybrid service and integrated external data sources.
  • MLOps Infrastructure: Designed the Adaptive Parameter Optimization System, including Warm/Cold Start modes and the logic for iterative hindcasting until convergence.
  • CAtobuildCA Integration: Added the Linear API integration to create tasks from insights, completing the feedback loop where ChainAlign helps build itself.
  • Final Validation: Added VN2 Newsvendor Challenge infrastructure and simulator for competitive forecasting validation.

Founder Insight: The foundation is strong. ChainAlign is now is becoming a self-optimizing system that is ready for initial pilot data and validation of its core intelligence layers.