Enterprise Document Intelligence & Hybrid RAG Retrieval Engine
Processing, indexing, and querying multi-format enterprise records with semantic precision, deterministic citation, and zero data leakage.
The Operational Challenge
Organizations handling high volumes of contracts, technical manuals, and financial audits struggle with manual information extraction. Traditional keyword search misses semantic context, while standard public LLM APIs risk data exposure, hallucination, and token truncation.
The Engineering Solution
We architected a hybrid Retrieval-Augmented Generation (RAG) platform. Documents are securely chunked, embedded using domain-adapted models, stored in isolated vector collections, and surfaced through hybrid dense-sparse reranking with verified source citations.
| Architecture Layer |
Technical Implementation |
System Deliverable |
| Document PipelineLayout-aware parsing |
Extracts tables, forms, and headers using PyMuPDF and vision-assisted OCR with metadata tagging. |
Sub-second ingestion |
| Embedding & Vector IndexingHigh-dimensional representation |
Domain-adapted embeddings stored in PostgreSQL with pgvector, partitioned by tenant workspace. |
Tenant isolation |
| Hybrid Retrieval & RerankerDense + sparse search fusion |
Reciprocal Rank Fusion (RRF) combining BM25 keyword matching with cross-encoder semantic reranking. |
99.4% precision |
| Inference & GuardrailsFactual verification & safety |
Source-grounded synthesis with automated citation checks, PII redaction, and hallucination filters. |
Zero data leakage |
Tech Stack:
Python
FastAPI
pgvector
PostgreSQL
Claude 3.5 / Llama 3
Redis
Docker
Autonomous Customer Support & Operational Workflow Orchestrator
Building a stateful agent system capable of resolving inquiries, querying internal databases, and executing authenticated multi-step API transactions.
The Operational Challenge
First-tier customer queries often require interacting with multiple disparate systems—checking order databases, verifying account permissions, and triggering inventory updates. Rigid decision-tree chatbots frustrate users and escalate too many tickets to human staff.
The Engineering Solution
BRIKCODE designed a stateful, deterministic multi-agent workflow. An intent classifier categorizes requests, dedicated agents execute authenticated API tool calls with strict parameter validation, and human-in-the-loop triggers handle edge cases gracefully.
| System Component |
Technical Implementation |
System Deliverable |
| Intent ClassifierLow-latency triage router |
Fast semantic classifier routes requests into atomic operational domains with confidence scoring. |
<120ms routing |
| Tool Execution SandboxParameterized API connector |
Strictly typed Pydantic parameter schemas connecting to Stripe, Salesforce, and internal SQL databases. |
Safe execution |
| Session State StoreContext & memory management |
Redis-backed distributed state machines maintaining conversational context across multi-step transactions. |
Zero context loss |
| Human Fallback GatewaySupervised escalation path |
Automated handoff to human operators whenever ambiguity or low-confidence thresholds are triggered. |
Graceful escalation |
Tech Stack:
Node.js
Python
Redis
LangGraph
OAuth 2.0
PostgreSQL
Webhooks
High-Performance Multi-Tenant SaaS Platform Architecture
Engineering a secure, scalable SaaS backend and responsive web application from foundational technical architecture to production release.
The Operational Challenge
Early-stage products frequently accumulate technical debt by coupling user interfaces tightly to ad-hoc database schemas. Scaling requires isolated tenant data, robust permission controls (RBAC), predictable API latencies, and modern responsive frontends.
The Engineering Solution
We built a clean, modular SaaS architecture: tenant-isolated database layers with PostgreSQL Row-Level Security, performant REST & GraphQL APIs, role-based access control, and a responsive web application optimized for Core Web Vitals and zero render-blocking bloat.
| Engineering Discipline |
Technical Implementation |
System Deliverable |
| Backend API MicroservicesHigh-throughput service layer |
Containerized Go & Node.js microservices with Redis caching, rate limiting, and connection pooling. |
10k+ req/sec |
| Data Isolation & SecurityTenant partitioning & RBAC |
PostgreSQL Row-Level Security (RLS) policies, JWT claims validation, and tenant-partitioned backups. |
Enterprise RBAC |
| Responsive Web FrontendZero-bloat user interface |
Sub-second page loads, accessible UI tokens, mobile responsiveness, and zero layout shift. |
Sub-second LCP |
| DevOps & CI/CD PipelineContinuous delivery & observability |
Automated GitHub Actions pipelines, Dockerized deployments, health checks, and 24/7 telemetry. |
Zero-downtime deploys |
Tech Stack:
Go
Node.js
PostgreSQL
Redis
Docker
AWS
GraphQL
Vanilla JS
