You will get AI Semantic Memory - Vector Search Integration
Project details
Standard databases find exact matches. Semantic memory finds meaning. I'll build a vector-based memory system for your AI application that stores knowledge and retrieves the most contextually relevant results — even when the exact words don't match.
This is the same architecture powering production AI assistants: text is converted to high-dimensional embeddings using NVIDIA NIM, stored in PostgreSQL with pgvector, and retrieved via cosine similarity search in milliseconds.
I built and run this system in production with over 3,600 memory entries across multiple namespaces — handling everything from technical facts to project decisions and code snippets. I know every edge case.
Use cases include AI chatbots with persistent memory, internal knowledge base search, customer support bots, document retrieval systems, and any application where "find the most relevant result "matters more than "find the exact match."
This is the same architecture powering production AI assistants: text is converted to high-dimensional embeddings using NVIDIA NIM, stored in PostgreSQL with pgvector, and retrieved via cosine similarity search in milliseconds.
I built and run this system in production with over 3,600 memory entries across multiple namespaces — handling everything from technical facts to project decisions and code snippets. I know every edge case.
Use cases include AI chatbots with persistent memory, internal knowledge base search, customer support bots, document retrieval systems, and any application where "find the most relevant result "matters more than "find the exact match."
AI Development Type
Deep Learning, Knowledge Representation, Recommendation SystemAI Tools
NVIDIA AI PlatformAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$500
|
Standard
$1,000
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 14 days |
AI Model Integration | |||
Detailed Code Comments | - | - | - |
Knowledge Graph | - | - | - |
Model Documentation | - | - | - |
Ontology | - | - | - |
Source Code | - | - | - |
Taxonomy | - | - | - |
About Caesar
AI Integration & Business Automation Expert | 20+ Years IT
Bogor, Indonesia - 5:00 am local time
can be fully automated. I eliminate that waste — fast.
With 20+ years in enterprise IT, I specialize in integrating AI into your
existing business workflows, building automation systems that actually work,
and delivering clean, scalable infrastructure. I've designed and built
enterprise database systems for major corporations and non-profit
organizations — systems still running today.
Here's what I can build for you:
✦ AI workflow automation (n8n, Make, Zapier, custom pipelines)
✦ AI chatbot & assistant integration into your existing apps
✦ Enterprise database design & optimization (PostgreSQL, MySQL, Oracle)
✦ Cloud infrastructure setup & migration (AWS, GCP, Azure)
✦ CI/CD pipeline implementation & DevOps setup
✦ REST API development & third-party system integration
✦ Network architecture design & engineering
My approach is simple: I understand your problem first, then I build the
right solution — not the most complex one. Clear communication throughout.
No surprises.
📌 Based in Indonesia (UTC+7) — available for clients across APAC, EU & US timezones.
📌 Available 30+ hours/week for dedicated project work.
Describe your challenge and I'll respond with a clear plan within a few hours.
Steps for completing your project
After purchasing the project, send requirements so Caesar can start the project.
Delivery time starts when Caesar receives requirements from you.
Caesar works on your project following the steps below.
Revisions may occur after the delivery date.
Use Case Design
Define namespace structure and memory schema for your use case
Database Setup
Deploy PostgreSQL with pgvector extension via Docker