Independent AI Engineer · Applied AI & Automation

Engineering AI systems that make decisions, not just predictions.

I design and deploy intelligent automation systems that replace manual decision layers, transform unstructured data into action, and deliver measurable operational impact for enterprise workflows.

View selected work ↓Discuss a project →
20+AI systems deployed
85%Avg. process time saved
6+Industries served
Portrait of Asif Mehmood
Decisions my systems makeIn production
Patient referral PDF→ Accept / Decline
Truck dispatch call→ Rate agreed
NI 43-101 report→ Structured data
Support ticket→ Resolved / Escalated
Deployed acrossHealthcare/Legal/Logistics/Investment Analysis/Enterprise Operations
(01) Selected work

Case studies

Real-world AI systems I've designed and deployed — each solving specific business problems with measurable outcomes.

PDFPatient referralREFERRAL PDFSAI LAYEROCR + NLPextractrulesclinicalPIIFACILITY RULE MATCHINGDECISIONACCEPTPayer verifiedClinical criteria metBed availableYES / NO + SUMMARY
01 / 06 · Healthcare AI

Healthcare AI Referral Decisioning Agent

Automated patient acceptance workflows for Skilled Nursing Facilities — turning complex clinical documents into instant YES/NO decisions.

InputPatient referral PDFs
AI layerOCR + NLP extraction, facility rule matching
DecisionYES/NO acceptance + clinical summary

Skilled Nursing Facilities received hundreds of patient referral documents daily — each requiring manual extraction of SSN, DOB, clinical data, and cross-referencing against facility criteria. The process was slow, error-prone, and created staffing bottlenecks.

PythonLLMsOCRPDF ParsingFastAPIRAG
(02) What I do

Expertise

I build AI systems that sit at the intersection of machine intelligence and business operations — where decisions happen.

01

AI Automation Architecture

End-to-end design of autonomous AI workflows that replace manual decision layers and scale operations without linear headcount growth.

02

Intelligent Document Processing

Extract structured intelligence from complex documents — PDFs, reports, medical records, legal filings — using OCR, NLP, and LLM pipelines.

03

Decision Support Systems

AI-powered platforms that analyze data, surface insights, and produce actionable recommendations — turning information overload into clarity.

04

Voice AI Systems

Conversational AI agents for real-time voice interactions — automating negotiations, dispatch coordination, and customer engagement at scale.

05

Applied LLM Engineering

Production-grade implementation of large language models — RAG architectures, fine-tuning, prompt engineering, and knowledge-grounded agents.

06

Workflow Transformation

Re-engineering business processes with AI-driven automation — from intake to decision to output — reducing friction and accelerating outcomes.

(03) Track record

Experience & clients

Companies I've worked with and clients I've built production AI for.

  1. [YEAR] — PRESENT

    Independent

    AI Solutions Architect

    Designing and deploying AI agents, document intelligence and voice AI for clients in healthcare, legal, logistics and investment analysis.

  2. [YEAR] — [YEAR]

    [Company name]

    [Role title]

    [What you built there and the result it had — one or two lines.]

  3. [YEAR] — [YEAR]

    [Company name]

    [Role title]

    [What you built there and the result it had — one or two lines.]

  4. [YEAR] — [YEAR]

    [Company name]

    [Role title]

    [What you built there and the result it had — one or two lines.]

(04) In their words

Testimonials

01 / 03
“
[Featured client quote — two or three sentences on the problem Asif solved and what changed for the team afterwards.]
[Client name][Role], [Company]
(05) Process

How I work

A structured approach to deploying AI that delivers production-grade reliability from day one.

  1. 01

    Understand Business Bottlenecks

    Deep dive into existing workflows to identify where manual decisions, repetitive tasks, and data friction create operational drag.

  2. 02

    Map Decision Logic

    Document the rules, edge cases, and judgment criteria that drive current decisions — creating a blueprint for AI automation.

  3. 03

    Design AI Workflow

    Architect an intelligent system that mirrors human decision quality while operating at machine speed and scale.

  4. 04

    Deploy Human-in-the-Loop

    Launch with smart escalation paths — AI handles the routine, humans manage the exceptions. Builds trust while delivering value immediately.

  5. 05

    Optimize for Reliability

    Continuously monitor, measure, and improve. Track accuracy, latency, and business outcomes to compound ROI over time.

(06) Engineering depth

Technology stack

Production-grade tools and frameworks I use to build reliable AI systems at scale.

AI & ML

Large Language ModelsRAG SystemsVector DatabasesFine-TuningPrompt Engineering

Engineering

PythonFastAPINode.jsREST APIsGraphQL

Data & Processing

OCRNLPData PipelinesPDF ProcessingData Extraction

Infrastructure

Cloud DeploymentDockerCI/CDWorkflow OrchestrationAPI Integrations

Specialized

Speech AIVoice AgentsKnowledge GraphsAutomation PipelinesDocument Intelligence
(07) Start a project

Let's build AI that actually gets used.

I work with teams that have real operational problems and want production-grade AI solutions — not prototypes that sit on a shelf. If you're ready to automate decision-making, let's talk.

Emailch.asifmehmood97@gmail.com→LinkedInin/asifmehmood1997↗
What do you want to automate?