You will get an MCP server connecting Claude or ChatGPT to your tools
Project details
Model Context Protocol is how AI assistants get safe access to your systems. I have shipped it in production for a paying client.
I built four MCP servers exposing about 68 typed tools that give Claude live read and write access to a law firm's Microsoft Advertising, Google Ads, Filevine, and Lead Docket accounts. Each runs its own OAuth 2.1 authorization server with JWT, PKCE, dynamic client registration and refresh rotation, deployed on Azure Container Apps. Raw credentials are never exposed to the model.
I can do the same for your stack: CRM, ERP, database, ads platforms, project tools, internal APIs. Your team then works in Claude Desktop, ChatGPT, Cursor or your own app, and the assistant can actually read and change things instead of just talking about them.
Every tool is typed and validated. Writes are auth-gated. Read-only queries are guarded against injection. You get the server, tests, deploy scripts, and documentation for adding tools yourself.
I built four MCP servers exposing about 68 typed tools that give Claude live read and write access to a law firm's Microsoft Advertising, Google Ads, Filevine, and Lead Docket accounts. Each runs its own OAuth 2.1 authorization server with JWT, PKCE, dynamic client registration and refresh rotation, deployed on Azure Container Apps. Raw credentials are never exposed to the model.
I can do the same for your stack: CRM, ERP, database, ads platforms, project tools, internal APIs. Your team then works in Claude Desktop, ChatGPT, Cursor or your own app, and the assistant can actually read and change things instead of just talking about them.
Every tool is typed and validated. Writes are auth-gated. Read-only queries are guarded against injection. You get the server, tests, deploy scripts, and documentation for adding tools yourself.
AI Development Type
Deep Learning, Knowledge Representation, Model Tuning, Recommendation SystemAI Tools
Amazon SageMaker, Azure Machine Learning, Keras, MLflow, NVIDIA AI Platform, OpenCV, PyBrain, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$300
|
Standard
$500
|
Advanced
$800
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 28 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 - 1:52 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.
Pick the surface
We agree which systems the assistant needs and which actions are read-only versus write
Design typed tools
Each tool gets a schema, validation, and clear error messages the model can act on