Muhammad Ghulam Jillani Portfolio
Founder and Lead AI Engineer at Jillani SofTech, an enterprise AI studio with 22+ AI systems live in production across 27+ clients; case studies report specific outcomes like a contract-review RAG system with "91% Citation Acc, 0 Hallucinated" and a contact-center AI at "80%+ Autonomous, 45% Lower AHT."
Location
Coordinates unknown. Vibes: international.
Skills
What makes it work
A breakdown of the choices that make this portfolio stand out.
Case studies built around a paired outcome metric, not one number
Pairing accuracy with a cost or speed figure for each system (91% citation accuracy alongside a 60% cost reduction; 80%+ autonomy alongside 45% lower handle time) shows the tradeoff was actually measured, not cherry-picked from one favorable dimension.
A stated zero-failure claim on the highest-risk metric
Reporting "0 Hallucinated" for a contract-review system names the exact failure mode that matters most for that specific use case, rather than a generic accuracy percentage that leaves the riskiest failure mode unaddressed.
Scale stated as a specific client and deployment count
"27+ enterprise clients" and "22+ AI systems live in production" over a stated multi-year period give a concrete, checkable scale claim instead of "extensive experience with enterprise clients."
What AI Engineers can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
Report two paired metrics per case study (an accuracy/quality number and a cost/speed number) — a single metric invites the suspicion it was cherry-picked.
Name the specific highest-risk failure mode for each system and state your result on exactly that metric, not just a generic accuracy score.
State your production deployment count and client count as specific numbers with a timeframe, not a general "extensive enterprise experience" claim.
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