You will get AI integration into your app using OpenAI, Claude, or Groq
Project details
You will get a working AI feature integrated into your existing app, backend, or workflow using OpenAI, Claude, Groq, or another LLM API.
This is best for teams that already have a product or internal process and want to add practical AI functionality: classification, summarization, content generation, structured JSON output, prompt pipelines, document processing, or API-connected AI behavior.
I focus on clean implementation your team can understand and maintain. My recent work includes a TypeScript AI agent deployed on AWS Lambda, EventBridge, API Gateway, S3, SSM, CloudFormation, and CloudWatch through AWS SAM. It ran for six weeks in production at 98% accuracy on a golden eval set.
Depending on the package, I can help with prompt/API design, backend integration, structured outputs, validation, logging, setup notes, and handoff. You will receive source code and documentation so your team can run, review, and extend the integration after delivery.
This is best for teams that already have a product or internal process and want to add practical AI functionality: classification, summarization, content generation, structured JSON output, prompt pipelines, document processing, or API-connected AI behavior.
I focus on clean implementation your team can understand and maintain. My recent work includes a TypeScript AI agent deployed on AWS Lambda, EventBridge, API Gateway, S3, SSM, CloudFormation, and CloudWatch through AWS SAM. It ran for six weeks in production at 98% accuracy on a golden eval set.
Depending on the package, I can help with prompt/API design, backend integration, structured outputs, validation, logging, setup notes, and handoff. You will receive source code and documentation so your team can run, review, and extend the integration after delivery.
AI Development Type
Knowledge Representation, Recommendation System, Software MaintenanceAI Tools
Amazon SageMaker, Google AutoML, MLflow, OpenCV, PyTorch, Sonnet, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$250
|
Standard
$750
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 1 | 2 | 2 |
AI Model Integration | |||
Detailed Code Comments | |||
Knowledge Graph | - | - | |
Model Documentation | |||
Ontology | - | - | - |
Source Code | |||
Taxonomy | - | - | - |
Frequently asked questions
About Jan
AI Engineer | AI Integrations, RAG, Agents | AWS Lambda
Toronto, Canada - 3:48 am local time
I build AI features, RAG systems, and agent-style automations that are meant to run in real products, not just demos. That can mean a quick Claude/OpenAI integration, a document-based chatbot, or a scheduled workflow on AWS Lambda with EventBridge and CloudWatch observability.
What I build:
- AI integrations for existing apps using Claude, OpenAI, Groq, or open-source LLMs
- RAG chatbots and semantic search systems with LlamaIndex + ChromaDB
- AI agent workflows with LangChain, LangGraph, or CrewAI
- Scheduled/event-driven automations on AWS Lambda + EventBridge + API Gateway
- Prompt optimization pipelines, including Promptimus: domain detection + rewrite under 2s
- Document extraction pipelines with OCR, structured outputs, and validation
Stack: Python, TypeScript, Node.js, AWS SAM, Lambda, EventBridge, API Gateway, S3, SSM, CloudFormation, CloudWatch, Groq, LangChain, LangGraph, CrewAI, LlamaIndex, ChromaDB, REST APIs.
Recent work:
- Production AI agent on AWS: TypeScript + Llama 3.3 70B via Groq, full IaC, 6-week run, 98% accuracy on eval set
-AI forecasting pipeline at Happy Nutrition: TypeScript + Llama 3.3 70B via Groq, AWS Lambda/EventBridge deployment, 20% forecast-error reduction after source-scoring improvements
- RAG improvements at Outautomation: 25% better retrieval precision and 35% gain in structured-data accuracy across large document sets
- Promptimus: two-stage LLM prompt optimization pipeline with sub-2s end-to-end runtime
Good fits:
- You want to add AI to an existing product
- You need a RAG chatbot or document search system
- You have a workflow that should be automated with LLMs/APIs
- You need an AI agent deployed cleanly instead of another fragile prototype
Tell me what you want the AI system to do, what data it touches, and where it needs to run. I’ll respond with a concrete first milestone, timeline, and honest scope.
Steps for completing your project
After purchasing the project, send requirements so Jan can start the project.
Delivery time starts when Jan receives requirements from you.
Jan works on your project following the steps below.
Revisions may occur after the delivery date.
Review scope
I review your current setup, examples, and goals, then confirm the exact AI feature, inputs, outputs, and delivery scope.
Design workflow
I design the prompt/API flow, response format, error handling, and integration path before implementation.