Kashifa Afrin | AI Builder | AI Deploy Network

About Kashifa

Kashifa Afrin is an AI Architect, Enterprise Systems Leader, and the Founder and CEO of Kaprin. She brings over a decade of hands-on experience designing and scaling distributed systems, cloud-native platforms, and applied AI systems for multi-million-dollar global enterprises.

Prior to founding Kaprin, Kashifa served as Vice President of Engineering at Briq, where she directed technical strategy and execution for a global engineering organization delivering financial automation SaaS. At Briq, she architected enterprise ETL pipelines, Canonical Data Models (CDMs), authentication frameworks, and integration engines supporting more than 2,500 enterprise clients.

Earlier in her career, she spent over six years at Principal Financial Group as Technical Lead and Senior Analyst in the Retirement and Income Solutions division. There, she led core financial calculation migrations from COBOL to Java (slashing nightly batch processing windows by 50%) and automated Department of Labor (DOL) Form 5500 compliance reconciliations, achieving a 100% First-Time-Right track record and compressing turnaround times from three weeks to two days.

At Kaprin, Kashifa partners with founders and executive teams to build production-grade custom software, autonomous workflow agents, and high-throughput data pipelines where clients retain 100% ownership of their source code and IP with zero recurring seat fees. Her engineering philosophy centers on deterministic rigor: pairing modern AI models with schema validation gates, idempotency locks, and self-healing telemetry to eliminate failure modes in production.

Professional profile

Location: India
Time zone: Asia/Calcutta
Work mode: Remote
Availability: Available This Week
Industry experience: 4+ years
Languages: English, Hindi
Typical response: Within a few hours
Travel: Occasional travel

Proven on the network

Implementation records and publicly available work, where published.

1 AI Deployment delivered, including 1 publicly identified implementation available to inspect.

Reported business outcomes

Hours Automated20 hours
period not specified
Automated 20 hours (period not specified)
Cost SavingsUSD 5,000
period not specified
Reduced operating costs by USD 5,000 (period not specified)

Builder-reported outcomes, recorded on deployment and case study records that AI Deploy Network reviewed before publication. AI Deploy Network does not currently capture a measurement basis or calculation methodology for reported outcomes, so none is shown.

What Kashifa builds

Implementation domains listed on this profile. Evidence-supported items are identified separately below.

Specialisations

Agentic Workflow Automation

AI Agents

Autonomous Agents

Intelligent Automation

Multi-Agent Systems

Builder-listed deployment types

AI Agent
Document Processing
Internal Business Automation
Workflow Automation

AI Deployments

Real AI implementations demonstrated through evidence.

01 / AI DeploymentPublished record

Autonomous B2B Purchase Order Parsing & ERP Synchronization Agent with Deterministic Guardrails

Production AI agent that extracts unstructured wholesale PDF purchase orders from email streams, maps non-standard buyer descriptions to internal inventory SKUs via GPT-4o, and drafts validated sales orders directly into Cin7 ERP.

20 hours automated / per week · AUD 5K cost savings / per month

Wholesale Distribution · AI Agent · Document Processing · Internal Business Automation · Workflow Automation

View Deployment

Implementation capabilities

Evidence-supported means linked to a published Deployment or Case Study; other capabilities are Builder declared.

Evidence-supported

AI Agent1 record
Document Processing1 record
Internal Business Automation1 record
Workflow Automation1 record

Builder declared

Agentic Workflow Automation
AI Agents
Autonomous Agents
Intelligent Automation
Multi-Agent Systems

Technology

Technologies listed by the Builder, distinguished by published evidence.

Evidence-supported

Python1 record

Builder declared

Agent Skills
Anthropic Console
React
React Native
Vue

Industry experience

Published Deployments support the listed counts. Other industries are Builder declared.

Wholesale Distribution

1 published Deployment

Construction

Builder declared

Data & Analytics

Builder declared

Marketing & Advertising

Builder declared

Sports & Events

Builder declared

Trust & verification

A closer look at how this profile and its evidence are reviewed.

Community Member

Published evidence is reviewed against the AI Deploy Network Evidence Standard.

How verification works

Community

Verified Builder

Independently reviewed by AI Deploy Labs against the platform Evidence Standard.

How this is verifiedWhat the platform has established, and what remains builder-declared.

Identity and organisation: Identity checks confirm that the person presenting this profile controls the account and the professional identifiers attached to it, such as a LinkedIn profile. Organisation membership is confirmed by the organisation, not self-declared by the builder.
AI Deployments: An approved AI Deployment is an implementation record the builder submitted and the platform reviewed against the Evidence Standard before publication. Approval confirms the record is complete, coherent and consistent with the supporting material provided — it is not an audit of the client's systems.
Business outcomes: Outcome figures are recorded by the builder on the deployment or case study that produced them, and are reviewed at publication. Aggregates shown on this profile are calculated from those approved records only.
Evidence: Supporting evidence comes from the records the builder published on this platform: approved AI Deployments, published AI Case Studies, verification outcomes and organisation participation.
Currency: Older evidence continues to count and is never removed. Recency is presented separately through Professional Currency so that current expertise can be assessed alongside historical work.
Traceability: Every figure on this profile can be followed to the record that produced it. Open the linked AI Deployment or AI Case Study to read the outcome exactly as it was reported.

The platform does not independently audit client systems or financial records. How this is verified

Professional record and further evidence

Client Portfolio

Diverse industry experience

HeyDiscount
Agentic AI • Marketing & Advertising
United Kingdom2025
Problem: E-commerce deal personalization engine designed to replace generic offer feeds with dynamic, intent-driven shopper personas. Architecture: Built an asynchronous batch ETL pipeline in Python with recency-weighted decay algorithms and prompt-cache warming across OpenAI and Vertex AI. Result: Slashed LLM token costs by over 40% while driving higher cross-category engagement across thousands of daily active users.
FansChance
Automation • Sports & Events
United Kingdom2026
Problem: High-volume charity draw platform needing real-time regulatory compliance and fraud prevention across web and stadium smart terminals. AI Architecture: Integrated algorithmic skill-testing trivia gating, automated checkout fraud scoring, and server-side Meta CAPI machine learning attribution into a native Kotlin POS and Next.js stack. Result: Achieved a 0.0% duplicate payment rate, fully automated UK regulatory compliance checks, and tripled matchday conversion volume in cashless venues.
Rally
Automation • Sports & Events
Israel2026
Problem: Racquet sports platform needing to eliminate subjective player self-assessments that caused skill-mismatched, low-retention matches. AI Architecture: Engineered a dynamic multi-variable skill-rating algorithm (1.0 to 7.0 ladder) with automated match feedback loops, paired with a deterministic radial social graph clustering model. Result: Cut court-booking time by 75%, boosted partner club occupancy by 30%, and maintained 99.4% booking accuracy across 2.4 million aggregated search queries.
Slender Morris
Agentic AI
Australia2024
Problem: Wholesale textile operations crippled by manual re-typing and transcription errors from unstructured PDF purchase orders sent via email. AI Architecture: Deployed a zero-shot semantic extraction agent combining GPT-4o, pdfplumber, and client-specific CSV alias prompt context to translate buyer descriptions into official Cin7 ERP inventory SKUs. Result: Compressed order turnaround from 15 minutes to under 10 seconds per order with 95% straight-through processing and 100% duplicate-order prevention.
1 AI Deployment2 Evidence Items
Hours
20
Cost
Currency not specified
View Related AI Deployments →
Hunt Digital Marketing
Automation • Marketing & Advertising
Israel2025
Problem: Performance marketing teams losing hours diagnosing multi-network ad waste and manually drafting weekly executive commentary. AI Architecture: Integrated Claude API for automated performance narrative generation alongside custom Python NLP n-gram clustering to detect and prune zero-conversion broad-match search queries in BigQuery. Result: Cut reporting overhead by 90% and autonomously identified thousands of dollars in ad waste, allowing the agency to double active client accounts with zero added headcount.
Dingo Cement
Automation • Logistics & Transportation
Australia2024
Problem: Industrial supply chain hindered by siloed ERP data and messy, unstructured carrier emails containing critical logistics updates. AI Architecture: Built an automated NLP ingestion engine that extracts container milestones from unstructured freight emails, feeding a dynamic multi-factor landed Cost-to-Serve calculation model. Result: Slashed manual spreadsheet collation by 95%, eliminated carrier data entry errors, and achieved 90%+ daily peak operational adoption across four manufacturing plants.

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