You will get an AI agent that acts on your legacy system but cannot break it
Rising Talent
Rising Talent
Project details
You want an LLM to act on a system you cannot afford it to break — an ERP, a mainframe green-screen, a 4GL line-of-business app, an internal API with no test environment.
The common approach hands the model your data and hopes. The safer one lets it plan over the verbs your system already exposes, and makes the dangerous states unrepresentable rather than merely discouraged.
That is what I build:
Capability by address — read-only is a property of the handle the agent holds, not a flag someone can forget. It cannot construct a writable handle to data you protected.
A typed function contract — a call naming a field that does not exist cannot be built, let alone sent. Bad plans fail before they reach production.
An inspectable plan — a goal-directed planner composes your existing operations into a plan you approve before anything runs.
It costs almost nothing at runtime. In my reference implementation six legacy operations ran in 40ms combined, while the single model call took 430 seconds. The old system is a rounding error next to the model.
Working reference implementation, 36 tests, no mocks:
github.com/YuvarajAravindan-AI-Agent/mapper-style-4gl-agent
The common approach hands the model your data and hopes. The safer one lets it plan over the verbs your system already exposes, and makes the dangerous states unrepresentable rather than merely discouraged.
That is what I build:
Capability by address — read-only is a property of the handle the agent holds, not a flag someone can forget. It cannot construct a writable handle to data you protected.
A typed function contract — a call naming a field that does not exist cannot be built, let alone sent. Bad plans fail before they reach production.
An inspectable plan — a goal-directed planner composes your existing operations into a plan you approve before anything runs.
It costs almost nothing at runtime. In my reference implementation six legacy operations ran in 40ms combined, while the single model call took 430 seconds. The old system is a rounding error next to the model.
Working reference implementation, 36 tests, no mocks:
github.com/YuvarajAravindan-AI-Agent/mapper-style-4gl-agent
What's included
| Service Tiers |
Starter
$299
|
Standard
$1,500
|
Advanced
$3,500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 1 | 2 | 3 |
Model Validation/Testing | - | - | |
Model Documentation | |||
Data Source Connectivity | - | - | |
Source Code | - | - |
Frequently asked questions
About Yuvaraj
Senior Solutions Architect | Java, AWS, React, Node.js | AI & LLM
Chennai, India - 2:21 am local time
WHAT I BUILD
• Backend & APIs — Java, Spring Boot, GoLang, Node.js, Python
• Frontend — React, TypeScript
• Cloud & DevOps — AWS (Certified Solutions Architect), GCP, Docker, CI/CD, Ansible (Red Hat Certified)
• Data & streaming — Kafka, Druid, MongoDB, SingleStore, Redis, DynamoDB
• AI & automation — LLM integration, RAG, self-hosted models, agentic workflows, n8n, KNIME
RECENT HANDS-ON WORK
• Built a self-hosted real-time voice agent — speech-to-text, local LLM inference and speech synthesis on GPU, tuned for sub-second response.
• Built and deployed a production React application with payment-gateway integration and serverless API endpoints.
• Deployed n8n agentic automation on AWS and KNIME workflows across GCP, AWS and local environments.
BACKGROUND
Architect and delivery leader across financial services, retail, telecom and government. Onshore delivery at client sites in London, Manchester, Preston, Stockholm and Santiago. Certified in Oracle Generative AI, Databricks Generative AI and Oracle AI Vector Search; AWS Certified Solutions Architect. Currently pursuing a BS in Data Science at IIT Madras.
HOW I WORK
I stay hands-on. You deal with me directly — no account manager, no handoff to a junior. I scope work in writing before starting, and I will tell you when a simpler approach than the one you asked for will get you there faster.
I am new to Upwork. I am not new to this work — happy to do a paid trial task so you can judge the output rather than the profile.
Message me with what you're trying to build and I'll tell you honestly whether I'm the right fit.
Steps for completing your project
After purchasing the project, send requirements so Yuvaraj can start the project.
Delivery time starts when Yuvaraj receives requirements from you.
Yuvaraj works on your project following the steps below.
Revisions may occur after the delivery date.
Inventory the verbs your system already exposes
Screens, transactions, stored procedures or endpoints — whatever the system already does safely. The agent gets these verbs and nothing else. No direct data access, no free-form SQL, no shell.
Model capability by address, so read-only cannot be forgotten
Read-only becomes a property of the handle the agent holds, not a flag in a prompt or a policy someone can forget to apply. A writable handle to protected data cannot be constructed.