Models and pipelines are designed to handle more data, markets and products as you grow.
Machine Learning
Machine Learning. Predictions built on the data you already have.
We build machine learning models on the data your business already has: sales orders, invoices, stock movements, attendance records. Predictions you can explain, at a lower running cost than generative AI.
[ 01 · Trusted by ]
Our partners
[ 02 — Why Do Clients Choose Deliverydevs? ]
Two different tools for two different problems.
Generative AI gets the headlines. Machine learning still does most of the quiet, reliable prediction work inside a business. We recommend whichever one solves the problem at the lowest cost, and sometimes the answer is neither.
Machine learning
Generative AI
What it does
Finds patterns in your numbers and predicts what happens next
Reads, writes and reasons over language and documents
Works on
Structured data: orders, invoices, stock, attendance
Unstructured content: emails, contracts, PDFs, chat
Data you need
Often a few years of clean records from one system
Pretrained models, plus your documents
Running cost
Low. Runs on your own infrastructure.
Higher. Usually priced per request.
Can you see why it decided?
Yes. Most models can be traced and explained.
Harder to trace.
Example
Which customers are likely to pay late
Answer staff questions from your policy documents
Machine learning
- What it does
Finds patterns in your numbers and predicts what happens next
- Works on
Structured data: orders, invoices, stock, attendanceStructured data: orders, invoices, stock, attendance
- Data you need
Often a few years of clean records from one system
- Running cost
Low. Runs on your own infrastructure.
- Can you see why it decided?
Yes. Most models can be traced and explained.
- Example
Which customers are likely to pay late
Generative AI
- What it does
Reads, writes and reasons over language and documents
- Works on
Unstructured content: emails, contracts, PDFs, chat
- Data you need
Pretrained models, plus your documents
- Running cost
Higher. Usually priced per request.
- Can you see why it decided?
Harder to trace.
- Example
Answer staff questions from your policy documents
[ SMALL DATA ]
You don't need big data. You need clean data.
Most machine learning work does not start with millions of records. A few years of sales orders in ERPNext or Odoo is often enough to forecast demand by product. What decides the result is whether the data is clean, consistent and connected across your systems. That is the part most projects skip, and the part we start with.
Under the hood
Gradient boosting, regularized regression and cross-validation are built to perform on modest datasets. Model choice follows data size, not the other way round.
[ 03 — Why Do Clients Choose Deliverydevs? ]
Why Do Clients Choose Deliverydevs?
[ 01 ]
Proficiency
Engineers, researchers and data scientists who build machine learning models for production use, not demos.
[ 02 ]
Creativity
We test new methods in our own research before they reach a client project, so you get what works.
[ 03 ]
Customization
Every model is built for your sector, your data and the systems you already run.
[ 04 ]
Scalability
[ 05 ]
Dependability
Quality checks run from the first dataset through deployment and after it.
[ CAPABILITIES ]
Predictions your team can act on.
Demand forecasting
Know how much of each product you will sell next month, so you stop over-ordering and running out.
Under the hood
Time-series forecasting on sales history, with seasonality and promotion effects.
Demand forecasting
See which customers are likely to pay late or leave, before it happens.
Under the hood
Time-series forecasting on sales history, with seasonality and promotion effects.
Demand forecasting
Know how much of each product you will sell next month, so you stop over-ordering and running out.
Under the hood
Time-series forecasting on sales history, with seasonality and promotion effects.
Demand forecasting
Know how much of each product you will sell next month, so you stop over-ordering and running out.
Under the hood
Time-series forecasting on sales history, with seasonality and promotion effects.
[ 04 — What Services Do We Provide? ]
What Services Do We Provide?
01
Data Mining
We examine large databases to find the trends and correlations that should shape your decisions.
02
Pattern Recognition
Segment customers, spot anomalies and see trends repeat before they cost you.
03
Supervised Learning
Prediction models trained on your labeled history, such as which orders will ship late.
04
Unsupervised Learning
Clustering and association mining find structure in data nobody has labeled yet.
05
Reinforcement Learning
Systems that learn from outcomes to improve scheduling, pricing and resource allocation.
06
Model Development
From data preparation to validation: models built for accuracy and explained in plain terms.
[ 05 — ML Data Engineering Solution ]
ML Data Engineering Solution
We build the pipelines, ETL and databases that get clean data to your models on time.
[ DATA AUDIT ]
Before we build anything, we read what you have.
Every engagement starts with a data audit: a short consulting engagement where our engineers review your organization’s data. It tells you what your data can answer about your business, what it cannot, and the cheapest way to get there. The same audit is the first step for machine learning and for Deliverydevs AI projects.
01
Map.
Where your data lives, across ERP, spreadsheets and tools.
02
Assess.
How complete, consistent and connected it is.
03
Recommend.
A report, a machine learning model, or AI. Whichever costs least and works.
04
Build.
Only once the data supports it.
[ 06 — How We Leverage Machine Learning ]
Our approaches to machine learning.
Supervised learning
Models trained on labeled examples, such as predicting customer churn or delivery demand by area.
Unsupervised Learning
Finds groups and outliers in unlabeled data, for market segmentation or flagging suspicious orders.
Reinforcement Learning
Models that learn by trial and error. Still early, with uses in route planning and dynamic pricing.
Machine Learning Libraries
TensorFlow and PyTorch, to build, test and deploy models faster.
Cloud Computing Platforms
On-demand compute such as Google Cloud AI Platform, so heavy training needs no upfront hardware.
Deep Learning
Neural networks for image recognition, such as damage detection, and language tasks like support chatbots.
Discuss Your Machine Learning Project with Our Experts
Tell us the problem. We’ll design a machine learning solution around it.
[ 07 — Decoding Our Process ]
Decoding Our Process
01
First Communication
Call, email or use the contact form to tell us your goals and expected results.
02
The Discovery and Needs Inspection
A meeting with our project managers and data scientists about your data, your problem and the expected gains.
03
Data Interpretation & Project Extent
We check your data’s quality and fit, then set objectives, schedule and deliverables.
04
Designing MLaaS Solutions
We choose algorithms, data preparation and deployment plans for your project.
05
Training and Development of Models
Once approved, we deploy to the cloud and connect the model to your existing systems.
06
Continuous Support and Maintenance
We monitor performance, retrain when needed and fix technical issues.
[ IN PRODUCTION ]
Pakistan Customs
Centralized Command and Control Platform with an AI predictive engine for risk and anomaly detection.
[ 08 — Benefits You’ll Enjoy With Our Services ]
Benefits You’ll Enjoy With Our Services
01
Expertise and experience
Specialists in data analytics, machine learning and AI on every project.
02
Tailored Solutions
Each solution fits your requirements, goals and constraints.
03
Technology and Innovation
Ongoing research keeps our tools and methods current.
04
Scalability and flexibility
Solutions that grow with you, from startup to enterprise.
05
Cost-Effectiveness
Results you can measure against what you spend.
06
Reliability and Support
A support team that answers questions from kickoff to handover.
[ 09 — FAQs ]
Frequently Asked Questions
What does machine learning mean?
Machine learning is a subset of artificial intelligence (AI) that allows systems to learn and improve via experience without being explicitly programmed. It uses algorithms to examine data, detect patterns, and make predictions or judgments with little human participation.
How does Machine Learning as a Service (MLaaS) work?
Machine Learning as a Service (MLaaS) offers ready-made machine learning tools and infrastructure via cloud platforms, allowing enterprises to leverage machine learning capabilities without requiring considerable in-house resources. It usually comprises data pretreatment, model training, deployment, and monitoring, making incorporating machine learning into operations simpler.
What is the scope of your machine learning services?
At Deliverydevs, we provide many machine learning services, such as data mining, pattern recognition, supervised and unsupervised learning, reinforcement learning, model development, natural language processing (NLP), image recognition, predictive analytics, and custom solutions.
What are the benefits of machine learning?
Machine learning can improve decision-making through predictive analytics, automate repetitive jobs, customize consumer experiences, identify and manage risks and fraud, optimize supply chain and operations, and deliver insights from massive data.
Can I request a machine learning solution if I do not have adequate data?
Yes, you can. While big datasets are useful for training machine learning models, approaches such as data augmentation, transfer learning, and synthetic data creation can assist data limits. We can also help you locate other data sources and progressively create your data-gathering strategy.
What is necessary to carry out a machine learning service?
To carry out a machine learning service, we require a thorough understanding of your business objectives and challenges, access to relevant and high-quality data, collaboration with your team to ensure alignment and knowledge transfer, and defined success metrics to assess the performance of the machine learning models.
What makes machine learning as a service (MLaaS) attractive?
The advantages of MLaaS include faster deployment of machine learning models, lower infrastructure and maintenance costs, access to cutting-edge machine learning technologies, increased scalability and flexibility, and a greater focus on core business activities while leveraging external expertise.
How does machine learning enhance customer service?
Machine learning improves customer service by allowing for personalized interactions, predicting customer needs and preferences, automating customer support with chatbots and virtual assistants, analyzing customer feedback for sentiment analysis, and improving response and resolution times for customer inquiries.
In which industries is machine learning useful?
Machine learning is applied in a variety of industries, including healthcare for diagnostics and personalized treatment, finance for fraud detection and risk management, retail for customer segmentation and inventory management, manufacturing for predictive maintenance and quality control, marketing for targeted advertising and campaign optimization, and transportation for route optimization and autonomous driving.
What is the significance of machine learning in business?
Machine learning’s role in business is to improve decision-making with data-driven insights, automate and optimize processes to increase efficiency, provide personalized customer experiences, proactively identify and mitigate risks, foster innovation through advanced analytics, and drive competitive advantage by leveraging data and artificial intelligence.
[ 10 — Who Make Us Stronger ]
Your data probably lives in a system we already implement.
Deliverydevs is a certified Odoo and ERPNext implementation partner. Most machine learning projects lose weeks understanding where the data sits and what each field means. We start already knowing the schema, because we build and migrate these systems for a living.
Not sure you need machine learning? The audit will tell you.
[ Get In Touch With Us ]
Let’s Start a Project Together
We’ll contact you within a couple of hours to schedule a meeting to discuss your goals.
Three steps come next
01
We set up a business consultation to discuss your idea.
02
We dive into the details of your project and conduct our own pre-discovery.
03
You get an offer tailored to your requirements with an actionable plan.
Book a Free Consultation
[ 11 — Who Make Us Stronger ]
Who Make Us Stronger
