You will get a Serverless Retrieval-Augmented Generation (RAG) Search Engine
Project details
Scaling AI leads to runaway API costs. Whether you're an enterprise with token bloat from unoptimized pipelines or a startup losing 30-50% margins to no-code wrappers, poor architecture bottlenecks your profitability.
I build high-performance, Serverless RAG systems that maximize unit economics and eliminate runaway spend. I replace expensive, high-latency orchestration with a Python/Serverless backend delivering fast, cited answers while reducing costs by up to 90% via intelligent ingestion and prompt engineering.
What I Deliver:
A complete Document Intelligence pipeline for proprietary PDFs, Docx, and Scans. My system doesn't just "chat"—it provides verifiable citations with direct links to sources in your S3/Cloud storage.
Technical Excellence:
• Layout-Aware: Preserves table structures and hierarchies so the AI actually understands data.
• Inference Optimization: Tiered routing (o4-mini/GPT-4o) and Prompt Caching to protect margins.
• Deterministic Citations: Claims map back to specific document chunks and pages.
• Serverless Scale: Near-zero idle costs, scaling to millions of documents.
I build high-performance, Serverless RAG systems that maximize unit economics and eliminate runaway spend. I replace expensive, high-latency orchestration with a Python/Serverless backend delivering fast, cited answers while reducing costs by up to 90% via intelligent ingestion and prompt engineering.
What I Deliver:
A complete Document Intelligence pipeline for proprietary PDFs, Docx, and Scans. My system doesn't just "chat"—it provides verifiable citations with direct links to sources in your S3/Cloud storage.
Technical Excellence:
• Layout-Aware: Preserves table structures and hierarchies so the AI actually understands data.
• Inference Optimization: Tiered routing (o4-mini/GPT-4o) and Prompt Caching to protect margins.
• Deterministic Citations: Claims map back to specific document chunks and pages.
• Serverless Scale: Near-zero idle costs, scaling to millions of documents.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Regression Analysis, Transformer ModelAI Applications
AI Chatbot, AI-Generated Code, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Hugging Face, PyTorch, TensorFlowAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$300
|
Standard
$1,000
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 12 days |
Number of Revisions | 1 | 3 | 10 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | |||
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | - | |
Model Deployment | - | - | - |
Model Documentation | - | ||
Model Monitoring | - | - | - |
Model Testing & Optimization | - | - | |
Model Tuning | - | - | - |
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | - | ||
Setup File | - | ||
Source Code |
Optional add-ons
You can add these on the next page.
Enterprise Full-Stack UI (Next.js 15)
(+ 7 Days)
+$1,500
Custom MCP Server
(+ 1 Day)
+$100Frequently asked questions
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EB
Ebony B.
Jun 17, 2026
AI / ML Engineer – Conversational AI & NLP (AWS Bedrock)-8 Week Project
DG
Darren G.
Mar 18, 2026
Build Serverless RAG SaaS: PDF Search Engine (Softr, Pinecone, Make.com, Python)
Dev was knowledgeable and insightful and understood many of business aspects of dev work during our project together.
About Will
AI Systems Architect & Lead Developer | RAG, Agents & Security
100%
Job Success
Atlanta, United States - 9:42 pm local time
I apply that same rigidity to AI. I don't believe in replacing humans with bots; I build "augmentation" systems where the software handles the grunt work and your team stays in control. My focus is entirely on the plumbing—connecting your messy legacy data (like QAD or old ERPs) to modern AI tools without exposing your proprietary information to the public web.
Graph RAG: Implementing knowledge graph-enhanced retrieval to capture structured relationships, improving LLM reasoning.
Generative AI & LLMs: Designing "Human-in-the-Loop" augmentation systems and bridging legacy systems (including paper-based ones) with high performance bleeding edge systems.
Production RAG: Grounding AI in enterprise data to prevent hallucinations using hybrid search strategies.
Agentic Workflows: Using LangChain and Python to build autonomous agents.
Vector Databases: Implementing semantic search infrastructure and embeddings.
Data Pipelines: ETL design and Data Extraction for high-volume systems.
Vulnerability Assessment: Threat modeling and attack planning, Risk Assessments, Security Posture Assessments, Adversary Simulation
Security: Security Code Reviews, , Remediation & System Hardening, Incident Response, Root Cause Analysis
Reverse Engineering: Deconstructing legacy modules and undocumented APIs.
Secure API Integration: Managing complex API integrations
ERP Customization: Deep expertise in QAD and Progress OpenEdge (4GL).
Logistics Technology: Building proprietary Warehouse Management Systems (WMS), integrating RF Scanning hardware, and modernizing warehouse operations.
Legacy Modernization: Bridging modern RESTful interfaces with legacy mainframes/ERPs.
Modern Frontend Architecture: Data-dense enterprise UIs using Next.js and TanStack.
Full Stack Development: Next.JS, TanStack, Django, FastAPI, LangChain, GraphRAG, Python, SQL, Agentic AI, automated document processing, API Development, custom backends.
Database Management: PostgreSQL optimization and query tuning for high-concurrency environments (99.99% uptime).
Enterprise Software Design: Architecting for scale, reliability, and "line-down" prevention.
Steps for completing your project
After purchasing the project, send requirements so Will can start the project.
Delivery time starts when Will receives requirements from you.
Will works on your project following the steps below.
Revisions may occur after the delivery date.
Architecture Review
We align on your document corpus, metadata requirements, and technical stack. I draft a TDD for your approval before any code is written.
Staged Development
I build the pipeline (Starter) or the API/UI (Standard/Advanced) in a secure, isolated sandbox environment. This allows you to review progress and test functionality without impacting your production infrastructure.