∂i~ƒ | data intelligence factory

Crafting AI/ML Models for Your Business Needs

di-factory | Data Intelligence Factory

Tailor-made machine learning. Fixed time. Fixed budget.

The Problem

  • Businesses sit on data goldmines but lack the expertise to extract value
  • Traditional ML projects are expensive, slow, and unpredictable
  • Off-the-shelf solutions don't fit your specific business context
  • Internal teams lack specialized ML engineering capacity
  • Projects drag on for months with uncertain outcomes

Our Solution

  • Tailor-made ML/DL models delivered in fixed time & budget
  • No surprises — scope, timeline, and cost determined upfront
  • From ideation to production in 2–4 weeks
  • You own everything: code, models, pipelines, IP

What We Build

Predict

  • Sales, costs, inventory, churn, credit scoring

Automate

  • Processes, fraud detection, quality control

Generate

  • Content, recommendations, intelligent chatbots

Use Cases

  • 📈 Predict next sales / costs / inventory levels
  • 💳 Credit scoring & risk assessment
  • 🔄 Churn prediction & retention triggers
  • 🤖 Intelligent chatbot for customer service
  • 🚚 Logistics optimization & route planning
  • 🛒 Product recommendations engine
  • 🔍 Fraud detection in real time
  • ⚙️ Process automation with ML decision-making

Our Pipeline — 5 Phases

Phase Focus Timeline
1 Consultation & Solution Ideation Days 1–2
2 Data Acquisition & Analysis Days 3–5
3 Data Transform Pipeline Week 2
4 ML Modeling & Transformation Weeks 2–3
5 Deployment & Evaluation Final days

Phase 1: Consultation & Solution Ideation

Days 1–2

  • Design Thinking workshop with stakeholders
  • Data landscape assessment
  • Use case mapping & prioritization
  • Success criteria definition
  • Solution architecture proposal

Phase 2: Data Acquisition & Analysis

Days 3–5

  • Data profiling & exploration
  • Quality assessment & gap analysis
  • Data governance review
  • Source integration planning
  • Baseline metrics established

Phase 3: Data Transform Pipeline

Week 2

  • Data preprocessing & cleaning
  • Feature engineering
  • Feature Store setup
  • Automated data pipelines
  • Reproducible transformations

Phase 4: ML Modeling & Transformation

Weeks 2–3

  • Algorithm selection & benchmarking
  • Model training & hyperparameter tuning
  • Validation & cross-validation
  • ML Registry for versioning
  • Performance evaluation against success criteria

Phase 5: Deployment & Evaluation

Final Days

  • Production integration (API / batch / streaming)
  • Monitoring & alerting setup
  • Knowledge transfer sessions
  • Documentation handoff
  • Go-live support

The Squad

Role Responsibility
BIZ Engineer Requirements, use cases, business alignment
Data Engineer Pipelines, data quality, Feature Store
ML Engineer Models, training, validation, registry
SW Engineer APIs, integration, deployment, monitoring

Each specialized. Working in concert.

Timeline

  • 2–4 weeks depending on complexity
  • Determined upfront during Phase 1
  • No surprises. No scope creep.
  • Weekly progress demos
  • Daily async updates

What You Get — Deliverables

  • ✅ Solution Brief & Architecture
  • ✅ Data Quality Report
  • ✅ Production Data Pipeline
  • ✅ Trained Model + Model Card
  • ✅ Monitoring Dashboard
  • ✅ Operations Guide
  • ✅ API Documentation
  • ✅ Knowledge Transfer Sessions
  • ✅ Full IP Transfer — you own everything

Industries We Serve

Industry Example Use Cases
🏥 Healthcare Patient risk scoring, readmission prediction
🏦 Banking Credit scoring, fraud detection
🛡️ Insurance Claims prediction, risk assessment
💰 Fintech Transaction anomaly detection, churn prediction
🛒 Retail Demand forecasting, product recommendations
🏭 Manufacturing Quality control, predictive maintenance
🏠 Real Estate Price prediction, market analysis
🎓 Education Student performance prediction, personalized learning

Why di-factory

  • ⏱️ Fixed Time — 2–4 weeks, guaranteed
  • 💰 Fixed Budget — no surprises, no overruns
  • 📉 Lowest Investment — AI-powered efficiency reduces cost
  • 🔧 Lowest Ops Cost — open source, zero licensing fees
  • 🤝 Best of Both Worlds — human expertise + AI acceleration

Technology

  • Open source stack — no vendor lock-in
  • Cloud-native architecture — scalable from day one
  • No licensing fees — zero ongoing software costs
  • You own everything — code, models, pipelines, IP
  • Standard tools: Python, scikit-learn, PyTorch, TensorFlow, MLflow, Airflow, AWS/GCP

After Delivery

  • 🛟 2 weeks support included at no extra cost
  • 🔄 Optional maintenance sprints for enhancements
  • 🤖 Automated retraining pipelines — models stay fresh
  • 📊 Monitoring dashboards for ongoing performance tracking
  • 🚀 Scale with us or independently — no dependency

Next Steps

  1. Choose an experiment — pick your highest-value use case
  2. We run the pipeline — 2–4 weeks, fixed scope
  3. Deliver & evaluate — production model + full documentation
  4. You get a roadmap — what's next for your data strategy

Start small. Prove value. Scale with confidence.

Let's Talk

🌐 di-factory.biz 📧 contact@di-factory.biz

Fixed time. Fixed budget. Your AI model in production.

di-factory — Data Intelligence Factory

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