Building AI integrations that actually ship.

AI Engineer · LLM applications, RAG systems and agentic automation · Lahore, Pakistan

Who is Musaab Javed?

Book a call

3+

Years production AI

10–20x

Cost-per-output cut on a live LLM pipeline

5,000+

Documents behind production RAG

60%

Fewer production pipeline failures

What I build

Lead Intelligence Systems

Multi-stage lead sourcing, qualification, and enrichment pipelines across Apollo, Clay, and Apify.

RAG Pipelines

Production document retrieval systems over 5K+ docs using LangChain, LlamaIndex, and Pinecone.

CRM & Workflow Automation

Event-driven n8n automations synced to HubSpot and GoHighLevel, cutting manual ops by 60–70%.

Content Automation

AI content factories, SEO articles, newsletters, and reports generated end-to-end at scale.

Clients I've worked with

Selected Projects

Arabic OCR Post-Correction: A 0.5B Model That Makes Scanned Archives Searchable

A 0.5B language model, fine-tuned with LoRA on 57k synthetic pairs, that repairs Arabic OCR output. Word error rate falls from 41% to 20% on held-out text. Trained in 80 minutes on a laptop GPU, and it runs on CPU, on-premise.

Problem

Scanned Arabic archives are indexed and still unsearchable.

Solution

A 0.5B model repairs the OCR. Word errors halved.

Qwen2.5-0.5B

LoRA / PEFT

PyTorch

Hugging Face

Python

Tell me more

View project

View Workflow Details

Workflow Features

Confusion model weighted by how Arabic script actually fails

Dot and skeleton confusion, word splits and merges, dropped and spurious characters

Severity sampled 4–18% per example, so one model covers clean and degraded pages

LoRA r=32 on all attention and MLP projections, bf16, no quantisation

Greedy decoding: one right answer, no sampling

Guardrail rejects wholesale rewrites and keeps the original

CER / WER evaluation by severity band against the raw-OCR baseline

Trains on a free Colab T4 with an automatic fp16 fallback

Business Impact

Word error 41.1% → 20.4%

Character error 8.1% → 6.7%

80 minutes on one laptop GPU

Runs on CPU, on-premise

Workflow Architecture

Clean Arabic news corpus

Script-aware corruption model

57k noisy → clean pairs

LoRA finetune of Qwen2.5-0.5B

Greedy decode

Drift guardrail

CER / WER scoring

Dossier Bot: On-Demand Lead Briefings in Slack

A Slack bot that turns a name or a LinkedIn URL into a PDF lead briefing. An agent with memory and four tools decides whether to fetch an existing dossier or build a new one, and a nightly run briefs the top five leads before anyone asks.

Problem

Every call starts with twenty minutes of tab-opening.

Solution

Mention a name in Slack. Get a briefing.

n8n

Slack API

LangChain Agent

GPT-4o

Supabase

html2pdf.app

Tell me more

View Workflow Details

Workflow Features

Answers an @mention in-thread

Conversation memory across turns

Cache-first: checks the bucket before generating

Lookup by profile URL or by name

On-demand generation as an agent tool

Briefing written from the lead's own recent posts

PDF rendered, stored and linked

Nightly pre-briefing of the top five leads

Business Impact

Briefing on demand, without leaving Slack

Top 5 leads pre-briefed nightly

Cached before generated, no duplicate work

One shared PDF instead of scattered notes

Workflow Architecture

Slack @mention trigger

Session parsing

Agent with memory buffer

Bucket lookup for existing dossier

Lead search by URL or name

On-demand generation tool

Agent writes from profile + posts

PDF render, store, reply in-thread

View all projects

Don't just take my word for it

"I had the pleasure of working with Musaab on several AI automation projects, and I am very happy with the results. What stands out most is his proactive approach — he doesn't wait for problems to escalate but tackles them head-on with clear communication and a genuine problem-solving mindset. Musaab is reliable, and his deep knowledge of automation and AI made the entire process smooth and efficient. If you're looking for someone who delivers results and makes collaboration easy, Musaab is your guy!

Stefan LuttikFounder · Like a Human AI

"What truly stands out is his ability to combine deep technical expertise with real business impact. Musaab doesn't automate for the sake of automation. He builds solutions that fundamentally improve how teams work and drive commercial performance. Professional, autonomous, fast, and always focused on delivering value.

Yves MimeranFounder · Excelr8

How I Work

From discovery to a monitored production system, the same 7 steps every time.

01
Discovery
Map your manual workflows, data sources, and tools to find the highest-ROI automation targets.
02
Workflow Design
Blueprint the end-to-end pipeline, triggers, CRM and API integrations, and human-in-the-loop checkpoints.
03
AI Logic
Design the LLM layer: prompt chains, RAG grounding, structured outputs, and model selection tuned for cost and accuracy.
04
Development
Build the integration in n8n and custom code, API connections, error handling, retries, and deduplication.
05
Testing
Validate against real data: edge cases, LLM output QA, and failure-mode drills before anything touches production.
06
Deployment
Ship to production with monitoring, alerting, and rollback paths, no silent failures.
07
Optimization
Track cost, latency, and output quality; iterate prompts and flows as volume grows.
Iterate

What could you save?

Adjust the sliders to estimate your savings with AI automation.

TIME SAVINGS

People in manual workflows3 people

Manual task hours / person / week10 hrs

~84 hrs

saved / month

~11 days

freed up

COST SAVINGS

Avg monthly salary per person$3,500

Monthly agency / tool spend to replace$1,000

~$2.6k

saved / month

~$31.7k

saved / year

Get your custom automation plan

Let's Build Your Next Integration

Have a workflow that eats hours every week? Tell me about it, I usually reply within a day.

Emailmusabjaved47@gmail.com

Book a call30-min intro call

WhatsApp+92 324 4606015

LinkedInin/musabjaved

GitHubCrypto47

Ready to get started?

Send message