You will get AI/RAG Pipeline Architecture Review & Optimization Plan
Project details
Your RAG pipeline is hallucinating, your retrieval is inconsistent, or your LLM costs are climbing faster than your revenue. I've seen this pattern dozens of times.
I'm a PhD security engineer with 2 years at AWS and a decade of production infrastructure experience. I specialize in diagnosing AI system failures that aren't obvious from the outside: embedding drift, cache staleness, retrieval confidence gaps, and the architectural decisions that seemed fine at MVP but break at scale.
What you get: A written assessment that identifies exactly what's wrong, why it's happening, and how to fix it. Prioritized recommendations (P0/P1/P2) with effort estimates and expected impact. No fluff, no generic advice. Specific to your stack, your data, your problems.
I work with Pinecone, Weaviate, pgvector, Neo4j, OpenAI, Anthropic, AWS, and GCP. If your system uses it, I've probably debugged it.
Typical clients see 30-50% cost reduction and significant reliability improvements from implementing the recommendations.
Review the sample deliverables attached to see exactly what you'll receive.
I'm a PhD security engineer with 2 years at AWS and a decade of production infrastructure experience. I specialize in diagnosing AI system failures that aren't obvious from the outside: embedding drift, cache staleness, retrieval confidence gaps, and the architectural decisions that seemed fine at MVP but break at scale.
What you get: A written assessment that identifies exactly what's wrong, why it's happening, and how to fix it. Prioritized recommendations (P0/P1/P2) with effort estimates and expected impact. No fluff, no generic advice. Specific to your stack, your data, your problems.
I work with Pinecone, Weaviate, pgvector, Neo4j, OpenAI, Anthropic, AWS, and GCP. If your system uses it, I've probably debugged it.
Typical clients see 30-50% cost reduction and significant reliability improvements from implementing the recommendations.
Review the sample deliverables attached to see exactly what you'll receive.
AI Development Type
Deep Learning, Knowledge Representation, Recommendation SystemAI Tools
Apache MXNet, Chainer, deeplearn.js, Google AutoML, NVIDIA AI Platform, OpenCV, PyBrain, PyTorch, SonnetAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$350
|
Standard
$850
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | - | ||
Detailed Code Comments | - | - | |
Knowledge Graph | - | ||
Model Documentation | |||
Ontology | - | - | |
Source Code | - | - | |
Taxonomy | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$100 - $350
Additional Revision
+$150
Additional 30-min consultation call
+$150
GCP/AWS cost optimization audit
+$400
Ongoing support (1 month)
(+ 10 Days)
+$1,200Frequently asked questions
About James
AI Systems Architect | Rust, Python, Multi-Agent Orchestration
Coupeville, United States - 5:48 pm local time
What I do: AI/ML integration, Python, APIs, automation, cloud architecture, backend systems, security.
Recent work: 200+ ML models at AWS. Fortune 100 security platforms. Enterprise container deployments.
Certs: CISSP, CCNA, CEH, MCSA
Let's talk about your project.
Steps for completing your project
After purchasing the project, send requirements so James can start the project.
Delivery time starts when James receives requirements from you.
James works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Access Setup
Review your responses, confirm scope, and request any additional access needed (cloud console, vector DB dashboard, logs).
Architecture Analysis
Audit current system: data flow, component health, retrieval pipeline, LLM integration, caching, and observability gaps.