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Founder & Lead AI Engineer · Jillani SofTech

Muhammad
Ghulam Jillani

Most enterprise AI dies in the gap between a demo that impresses and a system that survives Monday morning. I work on the other side of that gap: 22+ systems live in production for 27+ enterprise clients across the US, UK, EU and Australia, over 6 years. Average 47% efficiency gain, $870K+ in documented client savings, 99.9% uptime. Every engagement starts with the number we are trying to move, and a written acceptance test for it.

$100k+ earned · $870K+ saved for clients
24x LinkedIn Top Voice in AI & ML
Top 100 Global Kaggle Master
NVIDIA and AMD Developer Programs
Claude Certified Architect · AWS · Azure · GCP
See Projects & ROI Visit Jillani SofTech Download Capabilities Deck
Muhammad Ghulam Jillani, Founder and Lead AI Engineer at Jillani SofTech
Muhammad Ghulam Jillani
Founder & Lead AI Engineer · Claude Certified Architect
27+Enterprise Clients
22+AI Systems Live
47%Avg Efficiency Gain
100%Job Success Rate
$100,000+ Earned on Upwork · Top Rated Plus · 100% JSS
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Upwork Top Rated Plus · $100k+ Earned Claude Certified Architect · Anthropic AWS · Azure · Google Cloud Certified 24x LinkedIn Top Voice in AI HIPAA · GDPR · SOC 2 Compliant USA · UK · EU · Canada · Australia 6+ years shipping production AI Top 100 Global Kaggle Master NVIDIA Developer Program AMD AI Developer Program
Every credential links to its issuer

Certified, Recognized & Trusted On

A proctored architect certification from Anthropic, plus vendor certifications across AWS, Microsoft Azure and Google Cloud. Click any card to verify it.

2026 Claude Certified ArchitectAnthropic · Foundations Proctored exam · Credly AWS AWS Certified Solutions ArchitectAmazon Web Services Verify on Credly AZURE Microsoft Certified: Azure AI EngineerMicrosoft · Associate Verify on Credly GCP Google Cloud CertifiedGoogle Cloud Platform Verify on Credly
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27+Enterprise Clients 22+AI Systems Deployed $870K+Client Cost Savings $100k+Upwork Earned 47%Avg Efficiency Gain 18+Verified Credentials 99.9%Production Uptime
Why AI projects stall

The Demo Passed. Production Did Not.

I get called into stalled AI programmes often enough to see the same 4 failure points. They are not model problems. They are engineering and governance problems, and the build plan is designed around them from day 1.

It answers confidently and wrongly
A wrong answer nobody can trace is worse than no answer. Retrieval is grounded per clause, every claim carries a citation, and an evaluation harness gates each release. On a legal review build that produced 91% citation accuracy with zero hallucinated citations.
Evaluation gates before release
It cannot clear compliance review
Legal and security stop the rollout because data residency, redaction and audit trail were treated as a later phase. I design them into the architecture first, which is how a HIPAA-bound clinical build went live inside a US hospital network rather than stalling in review.
HIPAA, GDPR and SOC 2 by design
Nobody owns it after go-live
Accuracy quietly decays, no one notices for a quarter, and trust never comes back. Models ship onto one control plane with automated drift detection and audit-ready lineage. On one engagement that put 5 production models from 3 different teams under a single governance layer.
Monitoring and drift detection from day 1
The ROI was never actually defined
If nobody agreed what success is, the project cannot succeed. Discovery ends with a named metric, a baseline, and acceptance criteria in writing. That is what the average 47% efficiency gain and $870K+ in documented savings are measured against.
Acceptance criteria agreed before the build
Documented outcomes

3 Systems, 3 Numbers That Moved

Each one is a live system with a metric attached and a full write-up of how it was built, including what went wrong first. Clients are under NDA, so sectors are named and companies are not.

Contract review, 2 days to 2 minutes
Paralegals spent 2 full days per agreement cross-referencing clauses against precedent. Agentic RAG with per-clause retrieval and citation validation now does the pass in 2 minutes, at a lower inference cost than the first architecture.
91%Citation Acc
0Hallucinated
60%Lower Cost
LegalTechAgentic RAGGemma 3 27B
Read how it was built
Tier-1 calls answered without a queue
An earlier automation attempt had failed on latency and on handovers that lost context. This one holds sub-second response, completes over 80% of tier-1 calls end to end, and hands the rest to a human with the transcript and intent already attached.
80%+Autonomous
45%Lower AHT
<1sLatency
Voice AIReal-timeContact centre
Read how it was built
$340K of pipeline from routing done properly
Good leads were reaching the wrong rep and follow-up ran late. An agent pipeline now scores, enriches and routes every inbound lead against live CRM data, firing 900+ automated triggers a day so sales stops doing operations work.
$340KPipeline, 6 mo
1.8xQualified Leads
48%Less Sales Ops
RevOpsMulti-agentCRM
Read how it was built
See all 9 case studies
Measured across the portfolio

What The Numbers Are Measured Against

Averages across delivered engagements, each one baselined before the build started. Where a figure is specific to a single project, the case study states the client sector and the measurement window.

$870K+
Cost Savings Delivered
Documented client cost reductions via AI-driven workflow automation across 27+ enterprise deployments in Finance, Healthcare, and Retail.
47%
Average Efficiency Gain
Measured process efficiency improvement across RAG systems, automation pipelines, and predictive analytics solutions post-deployment.
65%
Manual Workload Reduced
Average reduction in human hours via intelligent automation using n8n, RPA, multi-agent AI, and LLM-powered document workflows.
99.9%
Enterprise Uptime SLA
Production AI systems maintained at 99.9% uptime across AWS, Azure, and GCP with 24/7 monitoring and zero-downtime deployments.
Engagement Models

3 Ways In, One Standard of Delivery

Scope and acceptance criteria are agreed before the build starts, so you are buying an outcome rather than hours. Most clients start with 1 narrow workflow and scale up only once it has earned that. Whichever path you take, the engagement ends the same way: a working system in your cloud, documentation your team can use, and code you own outright.

Scoping Call
30 minutes, free. The workflow worth tackling first, whether your data can support it, and a rough cost and timeline in writing afterwards. An honest no if AI is the wrong tool.
Free, no obligation
RAG or Automation Build
2 to 4 weeks, from $3,500. One system solving 1 clear problem, live in production and measured against acceptance criteria set before the build starts. Not a proof of concept.
Fixed scope, confirmed in writing
Enterprise AI Platform
8 to 16 weeks, $15K to $60K+. Multi-agent systems, APIs and dashboards with the MLOps, governance and HIPAA, GDPR or SOC 2 architecture around them, handed over documented.
Milestone billing you sign off
Retainer and Team Augmentation
$4.8K to $6K per month. Dedicated engineering hours inside your team: monitoring, drift detection, model refresh and roadmap input, so the system keeps earning after go-live.
Rolling, 6 months or longer

Start With The Number, Not The Tool

Book a 30 minute scoping call. You leave with the workflow worth automating first, the data it depends on, and a realistic cost and timeline. If traditional ML or plain automation is the better answer than a large language model, I will tell you that on the call. No cost, no obligation to build anything.

Book a 30 Minute Scoping Call Chat on WhatsApp
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