AssistX Enterprise · AI Engineering
PUBLIC PRODUCT CONTEXTAssistX Suite Platform Engineering
Platform engineering across retrieval-augmented generation, intelligent document processing, and customer-intelligence workflows, with implementation details kept intentionally high level.
FIG // 01.A
What I built.
My contribution spans ingestion, document-review tooling, customer analytics, and deployment. The diagrams below show the employer’s public product context; the notes describe my engineering work.
- Retrieval and agent-ingestion workflows
- Document-intelligence operations
- Customer-intelligence services and tooling
- Made mixed RAG and PDF inputs easier to ingest, normalize, and operate.
- Expanded document-review workflows with nested extraction and structured controls.
- Made customer-intelligence views more explainable with tenant-scoped segmentation and privacy-safe previews.
From documents to validated actions.
These employer-published diagrams make the workflow legible: visual document understanding, structured extraction, confidence checks, and downstream automation. They are product context, not individual deliverables. View the public overview ↗
Brief
Selected work at AssistX Enterprise spans three connected product areas: retrieval-augmented generation, intelligent document processing, and customer intelligence. This case study describes the engineering scope without exposing internal product names, customer information, private endpoints, or repository links.
Public product context
AssistX Suite is publicly presented as an intelligent document-processing platform: it analyzes document structure, classifies inputs, extracts validated fields with confidence scoring, and automates downstream workflows. The public product page is linked below; the implementation notes here remain focused on my engineering contribution.
The public diagrams on the case-study page are product context from that same overview: they show the document-to-workflow path and the extraction handoff I worked around. They are not screenshots of customer data or private implementation surfaces.
What I worked on
- Extended enterprise RAG and agent-ingestion workflows with custom-node compatibility, VLM/PDF parsing, chunk normalization, parser batching, structured dataset views, and provider administration.
- Contributed to document-intelligence services and their React operations console, including nested-list extraction, scoring-table sorting and filtering, column selection, and document-review tooling.
- Built customer-intelligence services and console workflows for structured ingestion, tenant-scoped RFM/persona segmentation, explainable product eligibility, data operations, privacy-safe previews, and observability.
- Hardened delivery paths with automated tests, Docker/Jenkins deployment updates, and server-side credential handling.
Confidentiality boundary
The public summary is deliberately capability-focused. Internal names, customer data, private implementation details, and source links are omitted; further detail can be shared in an appropriate interview or under a non-disclosure agreement.