You will get an AI agent that uses your tools and finishes multi-step work
Project details
A chatbot answers. An agent acts.
I build AI agents that plan a task, call your APIs, look things up in your data, and finish multi-step work: triaging tickets, enriching records, scoring calls, generating reports, moving data between systems your team currently bridges by hand.
I have put agents into production where the output matters. One live example: a call-scoring pipeline for a US law firm where every score the model returns must carry transcript evidence, and the code raises an exception if it does not. An unsupported grade cannot ship. That is the difference between a demo agent and one you can leave running.
You get the agent, its tool definitions, guardrails against prompt injection and data leakage, a trace log so you can see why it did what it did, tests, and a deployment you own.
Works with Claude, OpenAI, Gemini, Llama, and open models. Integrates over REST, MCP, webhooks, or direct database access.
I build AI agents that plan a task, call your APIs, look things up in your data, and finish multi-step work: triaging tickets, enriching records, scoring calls, generating reports, moving data between systems your team currently bridges by hand.
I have put agents into production where the output matters. One live example: a call-scoring pipeline for a US law firm where every score the model returns must carry transcript evidence, and the code raises an exception if it does not. An unsupported grade cannot ship. That is the difference between a demo agent and one you can leave running.
You get the agent, its tool definitions, guardrails against prompt injection and data leakage, a trace log so you can see why it did what it did, tests, and a deployment you own.
Works with Claude, OpenAI, Gemini, Llama, and open models. Integrates over REST, MCP, webhooks, or direct database access.
AI Development Type
Deep Learning, Knowledge Representation, Model Tuning, Recommendation SystemAI Tools
Amazon SageMaker, Azure Machine Learning, Google AutoML, Keras, MLflow, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$350
|
Standard
$500
|
Advanced
$1,000
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 25 days |
Number of Revisions | 2 | 3 | 4 |
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 - $300
Additional Revision
+$50Frequently asked questions
About Taimour Abdul
AI Agent & RAG Developer | Chatbots, MCP, Automation | Python, Claude
Lahore, Pakistan - 4:05 am local time
Most AI projects stall at the demo. Mine ship with evals, guardrails, and a deploy path, because I came up through ML engineering before LLMs got fashionable. MSc in Artificial Intelligence, 3+ years building production systems, currently AI/ML Engineer at a London analytics company.
WHAT I'VE SHIPPED
✔ 4 production MCP servers (Microsoft Ads, Google Ads, Filevine, Lead Docket) with OAuth 2.1 and ~68 typed tools, deployed on Azure Container Apps for a paying client
✔ A call-scoring pipeline: RingCentral webhook → AssemblyAI transcription → Claude rubric scoring, with code-enforced evidence so no score ships unsupported
✔ A trust-accounting automation with all-or-nothing writes and Teams alerting, replacing a fragile office-PC cron job that handled client money
✔ An LLM cost router benchmarked on real AWS Bedrock: matched top-model quality (0.917) at 71% of the cost
✔ A guardrails firewall (prompt injection, PII, secrets, toxicity) at macro-F1 1.0 with zero false positives on benign traffic
✔ A 17-phase production RAG benchmark, 178 tests: reranking lifted recall@1 from 0.55 to 0.95 on a weak retrieval stage and did nothing on a strong one
WHAT I BUILD FOR CLIENTS
✔ AI agents that use tools, call your APIs, and finish multi-step work
✔ RAG assistants over your documents, with citations and a faithfulness eval
✔ MCP servers connecting Claude or ChatGPT to your CRM, database, or SaaS stack
✔ LLM automation pipelines: ingest, score, route, report
✔ Cost and reliability work: cut token spend, add guardrails, gate regressions in CI
HOW I WORK
Week one you get an architecture doc and a working slice, not a status update. I ship in reviewable increments, write the tests, and hand over documentation plus a deploy you can run yourself. If a technique won't help your case, I say so and save you the money. I measure before I recommend.
STACK
Python • Claude API • OpenAI API • MCP • LangChain • FastAPI • RAG (Pinecone, FAISS, pgvector) • AWS Bedrock • Azure • Docker • PostgreSQL • Next.js • TypeScript
Send me a short description of the problem and what your data looks like. I'll tell you whether it's a fit, a rough scope, and where the hard part is, before you spend anything.
Steps for completing your project
After purchasing the project, send requirements so Taimour Abdul can start the project.
Delivery time starts when Taimour Abdul receives requirements from you.
Taimour Abdul works on your project following the steps below.
Revisions may occur after the delivery date.
Map the workflow
I walk the process you want automated and mark where a model helps and where plain code is safer
Design the tools
Typed, validated tool definitions against your APIs, with writes gated behind auth