IntelliDoc: AI-Powered Document Intelligence Platform

The Problem and Why It Mattered

I designed and built IntelliDoc — a B2B document intelligence platform for SMBs, legal firms, and enterprises. Upload contracts, policies, and documents; interact with them through natural language Q&A, expiry alerts, and conflict detection. The relevant technical and business risk is the cost of leaving this workflow, control, or product boundary unclear, manual, insecure, or difficult to operate. The case study focuses on the decisions used to make that risk manageable.

Objectives and Success Criteria

• Deliver the capability described in the project objective using a maintainable technical design.
• Make the main workflow, control boundary, and operational responsibilities explicit.
• Produce reviewable implementation evidence without publishing confidential values or customer data.

Requirements and Constraints

The product design had to balance a clear user workflow with maintainable technical boundaries, secure handling of user data, and a path for incremental delivery.

My Role and Ownership

I owned the technical design and implementation described in this portfolio record, including the technology choices, core system boundaries, and the documentation of the resulting approach. Related organisational decisions, vendor operations, and formal certification decisions remain outside the scope of this technical case study unless stated explicitly.

Solution Overview

The solution is built around Next.js 14, Supabase, Groq. Its implementation is organised as the components below so that the architecture, control points, and operating model can be evaluated separately rather than treated as a list of tools.

Architecture and System Design

The public architecture description follows the path from the initiating user, workload, or operator action through the relevant product development components. Access boundaries, data or state handling, and operational controls are described at a safe level; secrets, private addresses, and client-sensitive implementation details are intentionally omitted.

Technology Decisions and Trade-Offs

I selected Next.js 14, Supabase, Groq because they support the project objective and make the critical engineering boundaries explicit. The trade-off is operational and platform complexity: the design depends on documented configuration, controlled access, and ongoing review rather than assuming that a managed service or framework removes engineering responsibility.

Implementation Walkthrough

The following implementation notes describe the components that matter most to the project outcome. They are intentionally focused on technical decisions and operating behaviour instead of reproducing entire source files or sensitive configuration.

Implementation: Document Upload & Processing Pipeline

Files are stored in Supabase Storage with row-level security. On upload, a background worker extracts text, chunks it using a sliding window strategy optimised for legal document structure, generates embeddings, and stores them in Supabase's pgvector extension for semantic search.

Implementation: AI Q&A with Source Attribution

A RAG pipeline retrieves relevant document chunks from pgvector and passes them to Groq's LLaMA 3.3 70B to generate grounded answers with explicit citations back to the source document and page number.

Implementation: Expiry & Renewal Alert Engine

A metadata extraction layer identifies dates within documents. A scheduled Cloud Run job evaluates these nightly and sends Resend email alerts at configurable intervals: 90, 30, 7, and 1 day before expiry.

Implementation: Contract Conflict Detection

Clauses are identified, categorised by type, and compared across documents for logical contradictions — different governing law provisions, contradictory liability caps, overlapping exclusivity terms — using the LLM as a structured reasoning layer with explicit clause citations.

Implementation: Identity & Access Management

Organisation-scoped multi-tenancy: three roles — Admin, Editor, and Viewer. Supabase RLS policies enforce data isolation at the row level. Invitations via Resend magic links. Google OAuth and email/password authentication supported.

Implementation: Infrastructure

Containerised and deployed on GCP Cloud Run. Terraform manages all GCP infrastructure. GitHub Actions CI/CD with automated testing and Cloud Run deployment on merge to main.

Challenges, Failures and Corrections

This portfolio record does not invent deployment failures, client incidents, or corrective actions that are not supported by the published project evidence. Where operational details are confidential or were not captured for public release, the case study documents the design boundary and lesson instead of presenting an unsupported narrative.

Security, Compliance and Risk

The public record avoids secrets, customer data, and private infrastructure details. The relevant security controls, access boundaries, and operational risks are addressed in the implementation notes where they are material to the project.

Testing, Validation and Evidence

The evidence published for this project is the documented implementation, its stated architecture and controls, and the linked project reference where available. The published record documents the implemented architecture, the core controls, and the technology choices that can be reviewed without exposing confidential operational data. Claims are limited to the implementation details and outcomes documented in this case study.

Results and Evidence

The published record documents the implemented architecture, the core controls, and the technology choices that can be reviewed without exposing confidential operational data.

What I Learned

This project reinforced that product development work is not a technology checklist. The durable value comes from making boundaries, trade-offs, ownership, and evidence clear enough for another engineer or stakeholder to review and operate the system responsibly.

What I Would Improve Next

• Expand automated validation around the most important workflow and control boundaries.
• Add or deepen operational evidence, such as dashboards, test artefacts, or an architecture diagram where it is safe to publish one.
• Review the design against the next expected scale, reliability, and governance requirement before extending the system.

Final Takeaway

This product development case study addresses a defined technical problem through Next.js 14, Supabase, Groq. It demonstrates my ability to turn requirements into an explainable design, implement the important controls, and document outcomes and limitations without overstating what the evidence proves.