You will get a secure RAG assistant with approvals and audit logs
Project details
I build controlled AI systems for document-heavy business work—not generic chatbots.
Your assistant can retrieve approved information, produce evidence-grounded answers, cite sources, create structured outputs and, within the selected package, call bounded APIs or tools. Sensitive actions can pause for human approval, while important events are recorded for review.
Typical capabilities include RAG over approved sources, “insufficient evidence” fallbacks, structured tool calls, user roles, approvals, audit events, retry handling, evaluation cases, source code and deployment handover.
I have 15 years of enterprise and public-sector delivery experience and focus on requirements, integration, permissions, failure recovery and maintainability—not just prompt engineering.
Enterprise SSO, penetration testing, high availability, large-scale data cleaning, unsupported legacy automation and formal compliance certification require separate scoping.
Your assistant can retrieve approved information, produce evidence-grounded answers, cite sources, create structured outputs and, within the selected package, call bounded APIs or tools. Sensitive actions can pause for human approval, while important events are recorded for review.
Typical capabilities include RAG over approved sources, “insufficient evidence” fallbacks, structured tool calls, user roles, approvals, audit events, retry handling, evaluation cases, source code and deployment handover.
I have 15 years of enterprise and public-sector delivery experience and focus on requirements, integration, permissions, failure recovery and maintainability—not just prompt engineering.
Enterprise SSO, penetration testing, high availability, large-scale data cleaning, unsupported legacy automation and formal compliance certification require separate scoping.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Hugging FaceAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$950
|
Standard
$2,750
|
Advanced
$5,800
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 1 | 2 | 2 |
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 Lielie
AI App & Systems Integration Engineer | APIs, Automation, DevOps
Changzhou, China - 2:35 pm local time
If you need more than a chatbot demo, I can turn a repetitive business process into a working web application, automation, AI agent, or internal tool. I handle the full delivery process—from requirements and architecture to APIs, databases, deployment, testing, and ongoing support.
With 15+ years of software delivery experience, I have built and maintained enterprise and public-sector systems that require reliability, security, and long-term operation.
I can help you with:
- AI agents and workflow automation
- AI integration for existing web applications
- Internal tools, dashboards, and admin portals
- API, webhook, database, and third-party integrations
- Document processing, reporting, search, and knowledge systems
- Legacy system modernization, debugging, and on-premises deployment
My approach is practical: define the workflow and acceptance criteria, build a testable milestone, verify edge cases, and then make the system stable enough for daily use.
Send me your current workflow, files, or system requirements. I can help you identify the simplest reliable solution before development begins.
Steps for completing your project
After purchasing the project, send requirements so Lielie can start the project.
Delivery time starts when Lielie receives requirements from you.
Lielie works on your project following the steps below.
Revisions may occur after the delivery date.
Confirm use case, sources, actions and security boundaries
Review users, approved knowledge sources, required actions, sensitive data, permissions, approval rules, deployment constraints and acceptance criteria.
Design retrieval, tools, approval and audit flow
Define the RAG pipeline, structured outputs, bounded tool calls, human approval checkpoints, audit events, failure handling and evaluation approach.