You will get a Private AI / RAG Proof-of-Concept on AWS Bedrock
Project details
Give your team an AI assistant that answers questions from your own documents, running entirely inside your own AWS account. No data leaves your environment, nothing trains anyone's model, and every answer is cited to the source so your people can trust and verify it.
I build private retrieval-augmented generation (RAG) on Amazon Bedrock, and I am vendor-neutral on the model. Use Anthropic Claude for quality, or an open model like Llama for openness and portability. Either way it stays private to you.
Built by an operator, not just an engineer. I run go-to-market at a systems integrator and build these systems hands-on, so I design for the business outcome and the running cost, not just the demo.
Depending on tier: a private reference architecture with security design and a cost model, a working proof-of-concept trained on your documents with a chat UI or API, and an evaluation harness plus a phased path to production.
Not sure which tier fits? Send a message and I will point you to the right one.
I build private retrieval-augmented generation (RAG) on Amazon Bedrock, and I am vendor-neutral on the model. Use Anthropic Claude for quality, or an open model like Llama for openness and portability. Either way it stays private to you.
Built by an operator, not just an engineer. I run go-to-market at a systems integrator and build these systems hands-on, so I design for the business outcome and the running cost, not just the demo.
Depending on tier: a private reference architecture with security design and a cost model, a working proof-of-concept trained on your documents with a chat UI or API, and an evaluation harness plus a phased path to production.
Not sure which tier fits? Send a message and I will point you to the right one.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI-Enhanced Classification, Conversational AI, Natural Language Understanding, Text RecognitionAI Development Language
PythonAI Tools
Hugging FaceAI Models
GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$2,500
|
Standard
$6,000
|
Advanced
$9,500
|
|---|---|---|---|
| Delivery Time | 7 days | 21 days | 30 days |
Number of Revisions | 1 | 2 | 3 |
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 | - |
Frequently asked questions
About S
AI-Powered GTM Systems | Anthropic, AWS & HubSpot Consultant
New Bern, United States - 3:32 pm local time
Most "AI for marketing" work fails because it's built by people who understand either the technology or the go-to-market motion, never both. I've spent my career on both sides: I co-own and help run a US systems integrator, where I lead go-to-market and have personally architected the AI systems that now drive our business: private LLM deployments, retrieval-augmented generation on proprietary data, and AI-enabled content and sales tooling.
What that means for you: I don't hand you a chatbot and walk away. I design AI systems that plug into how you actually sell and market, HubSpot workflows, content engines that hold your brand voice and convert, sales assistants grounded in your data, and automation a revenue team will actually adopt.
What I build:
• AI content engines: brand-consistent, SEO/AEO-aware, human-in-the-loop — on Claude + your stack
• HubSpot automation & AI enrichment: lead scoring, sequencing, RevOps workflows
• RAG sales & support assistants grounded in your docs, pricing, and CRM
• GTM strategy for AI-enabled teams: where AI actually moves the number
Stack: Anthropic (Claude), AWS Bedrock, HubSpot, and the API/automation glue that connects them.
I work with a small number of clients at a time and treat every engagement like my own revenue is on the line, because for most of my career, it has been.
If you want AI that produces pipeline instead of demos, message me with what you're trying to move, and I'll tell you straight whether I can help.
Steps for completing your project
After purchasing the project, send requirements so S can start the project.
Delivery time starts when S receives requirements from you.
S works on your project following the steps below.
Revisions may occur after the delivery date.
Kickoff & Scope
We start with a short call to confirm your use case, the questions the assistant must answer well, your data, your model preference, and any security or compliance constraints. You leave with a clear scope and success criteria.
Architecture & Security Design
I design the private RAG architecture for your AWS account: data flow, vector store, model choice, access controls, and guardrails, plus an indicative running-cost model. This is the full deliverable at the Architecture tier.