You will get a custom AI assistant that connects to your data, APIs and workflows
Project details
Most AI chatbots just answer questions. I build assistants that actually do things inside your product, look up a customer's order, update a record, trigger a workflow, and escalate to a human when it isn't sure.
With 8+ years of full-stack engineering, I connect the LLM directly to your real systems: your database, your internal APIs, your business rules. That's the part most chatbot builds get wrong, and it's the part that decides whether your team keeps using it after week two.
What I build:
• Context-aware assistants with streaming chat and conversation memory
• RAG over your docs, help centre, product catalogue or database
• Tool/function calling against your existing APIs
• Permission-scoped actions — the assistant only touches what that user is allowed to touch
• Human-in-the-loop approval before anything irreversible
• Cost controls, logging and evals so you can see what it's doing and what it's spending
Stack:
• Claude, GPT-4/5, or open models.
• Python or TypeScript/Node.
• Deployed to AWS, Vercel, or your own infra.
• Web widget, Slack, WhatsApp, or embedded in your app.
Message me with what you're trying to automate and I'll tell you what it takes.
With 8+ years of full-stack engineering, I connect the LLM directly to your real systems: your database, your internal APIs, your business rules. That's the part most chatbot builds get wrong, and it's the part that decides whether your team keeps using it after week two.
What I build:
• Context-aware assistants with streaming chat and conversation memory
• RAG over your docs, help centre, product catalogue or database
• Tool/function calling against your existing APIs
• Permission-scoped actions — the assistant only touches what that user is allowed to touch
• Human-in-the-loop approval before anything irreversible
• Cost controls, logging and evals so you can see what it's doing and what it's spending
Stack:
• Claude, GPT-4/5, or open models.
• Python or TypeScript/Node.
• Deployed to AWS, Vercel, or your own infra.
• Web widget, Slack, WhatsApp, or embedded in your app.
Message me with what you're trying to automate and I'll tell you what it takes.
AI Development Type
Knowledge Representation, Recommendation SystemAI Tools
Amazon SageMaker, deeplearn.js, MLflow, OpenCV, PyTorch, Sonnet, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$299
|
Standard
$600
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 20 days |
Number of Revisions | 1 | 2 | 5 |
AI Model Integration | |||
Detailed Code Comments | - | ||
Knowledge Graph | - | - | |
Model Documentation | - | ||
Ontology | - | - | |
Source Code | |||
Taxonomy | - | - |
Frequently asked questions
About Zubair
AWS Solutions Architect | React & Node.js | AI Agent Engineer
Lahore, Pakistan - 7:49 am local time
I am a solutions architect and full-stack engineer 9 years in fintech,
payments and collections platforms. I design the architecture, then write
the code myself. Lately: AI agents that take real actions inside real
products updating records, resolving cases, editing content by
instruction not chatbots that just talk.
PROOF
→ Took deploy-safety coverage from 10% to 100% across 102 repos canary
releases, gated pipelines, auto-rollback. Bad code now dies in minutes,
not in front of clients.
→ Built the AI layer behind three production assistants (strategy, email,
landing-page editors), controllable by plain instruction with
permissioned "Act" tools so AI could safely update records, not just
describe what it would do.
→ Rebuilt a Snowflake/S3/QuickSight reporting pipeline: fixed silently
missing events, added PII masking across every downstream API. including
the AI tools, so client data stayed clean and clients stayed confident.
→ A client accidentally resolved 127 tasks with no way to undo it. I
built bulk task-recovery so it could never happen again, and that entire
category of support ticket disappeared.
→ Cut infra cost ~20% and helped pass an ISMS security audit on a 100+
microservice, cloud-native platform.
I do my best work with teams building something real, not prototypes
chasing the cheapest bid. Fintech and payments are my deepest reps, the
underlying skill, systems that don't fall over under real usage, travels
anywhere.
Tell me what you are building. I will tell you straight whether I am the
right fit.
Core stack: React, Node.js, TypeScript, Vue.js, GraphQL, AWS (Lambda,
Fargate, DynamoDB, CloudFormation, Bedrock, QuickSight), Microservices,
AI Agent Development, Snowflake, CI/CD.
Steps for completing your project
After purchasing the project, send requirements so Zubair can start the project.
Delivery time starts when Zubair receives requirements from you.
Zubair works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & AI Planning
Understand the use case, data, tools, and desired AI workflow.
AI Architecture & Setup
Select the appropriate model, architecture, prompts, and integration approach.