AI system for remote patient monitoring
- 20%
- reduction in clinical decision-making time
Machine Learning Development Company
Most machine learning projects fail not because the models are wrong, but because the surrounding system is not built to use them.
We build end-to-end ML systems, from data pipeline to model training to production deployment, that connect to your existing operations. Models that run in real systems, on your data, and deliver output your team can act on.
Custom ML models trained on your operational data
End-to-end: data pipeline, training, validation, and production deployment
Integration with your existing apps, CRM, ERP, or dashboards
100+ products shipped including AI and ML-powered systems
Recent outcomes
Voice AI · Research
6× deeper insights
Text-based interviews converted to automated phone calls
AI Automation · Ops
20k+ txns day one
Manual invoice OCR across 40+ gas stations
Loyalty · Retail
1,062 users in 4 weeks
SuperValu & Centra loyalty platform with receipt validation
SaaS · Logistics
2,000+ shipments yr 1
Multi-carrier shipping hub for Indonesian eCommerce
4.9
on ClutchThe problem
Sound familiar?
Your team has data but no system turning it into predictions or decisions?
Tried off-the-shelf ML tools that don't fit your actual data or workflow?
Short answer
RaftLabs builds custom ML systems trained on your operational data and deployed into production. We serve clients across the US, UK, Europe, Canada, GCC, South Africa, and Southeast Asia across demand forecasting, churn prediction, fraud detection, and NLP. 100+ products shipped since 2015. Fixed cost, scoped before development starts.
Key takeaways
- Custom ML models are trained on your operational data and deployed into production, not generic pre-trained solutions.
- A focused ML project covering one use case typically takes 8-16 weeks from discovery to production deployment.
- Project costs range from $25,000-$60,000 for a focused build; larger multi-model systems run $60,000-$150,000.
- RaftLabs built a HIPAA-compliant remote patient monitoring system that cut clinical decision-making time 20% across 150+ patients in the first 12 weeks.
- Every project starts with a fixed-cost scoping phase covering data audit, model feasibility, and a written proposal before development begins.
Trusted by
AI development, by the numbers
- AI products shipped in 24 months
- 20+
- from kick-off to production-ready AI product
- 12 weeks
- rated by clients on Clutch
- 4.9/5
- shipping software and AI products
- Since 2015
ML value comes from production, not from notebooks
A model that lives in a Jupyter notebook is a prototype. A model that runs in your CRM, flags risks in your operations dashboard, or routes decisions in your application is a system.
The gap between prototype and production is where most ML projects fail. Data scientists build accurate models that never reach the people who need the predictions. We build the full system, data pipeline, model, integration, and monitoring, so predictions reach your team automatically.
According to McKinsey's 2024 global AI adoption survey, 65% of organizations are now regularly using AI, yet most report that fewer than a third of their ML initiatives have reached full production deployment. That gap is exactly where custom ML system development, not model experimentation, creates durable competitive advantage.
Capabilities
What we build
- 01Demand and inventory forecasting
Forecasting models for inventory replenishment, staffing, production capacity, and revenue, trained on your historical data and the signals that drive demand. Forecasts ship with confidence intervals your planning team uses to set safety stock, delivered to your ERP or demand planning tool via API, with accuracy tracked as MAPE by SKU and location so degradation can trigger retraining.
- Built with
- Prophet · LightGBM · LSTM
- 02Customer churn prediction
Churn risk scoring for every customer account, updated daily or weekly and written to your CRM as a field the retention team sees in the account view. Models train on transaction history, product usage, support interactions, and contract signals, and feature importance shows why an account is at risk, so a threshold breach can create a task for the account owner with a suggested action.
- Built with
- Salesforce · HubSpot · Pipedrive
- 03Classification and categorisation
Multi-class classification models for data your team currently categorises by hand at volume: document routing, support ticket triage, transaction labelling, and lead qualification. Each prediction carries a confidence score, so high-confidence results route straight to the workflow and low-confidence ones enter a human review queue, with performance evaluated per class rather than on overall accuracy alone.
- Built with
- XGBoost · BERT
- 04Fraud and anomaly detection
Anomaly detection models for transactions, user behaviour, IoT sensor readings, and operational metrics: systems that learn your normal baseline and flag deviations before they become costly incidents. Alerts arrive with supporting evidence, and sensitivity is tunable per metric and team, so fraud ops can run hot without drowning operations teams in false alarms.
- Built with
- Isolation Forest · Autoencoders · PagerDuty · Slack
- 05NLP and text intelligence
NLP systems built on language models fine-tuned to your domain vocabulary, document types, and entity taxonomy, not generic models applied without adaptation. We build document classification, named entity recognition, sentiment analysis, and text summarisation, deployed as an endpoint that takes your text and returns structured JSON in milliseconds.
- Built with
- BERT · Hugging Face transformers · FastAPI
- 06Computer vision
Image classification, object detection, and defect identification for manufacturing, logistics, retail, and healthcare, with models trained on your labelled image data and integrated into your inspection, camera, or document processing workflow. See our computer vision development page for specific use cases.
Services
Machine learning services we offer
- 01
AI development
The broader AI build: LLM-powered features, agents, and generative systems that sit alongside the predictive models we train on your data.
- 02
Machine learning consulting
Strategy, data assessment, and model architecture without a full build commitment. We tell you what is feasible and what it will take before you commit budget.
- 03
Predictive analytics
Business-focused forecasting and risk scoring: demand, churn, lead scoring, and revenue prediction wired into the dashboards and CRMs your team already uses.
- 04
Computer vision development
Image classification, object detection, and defect identification trained on your labelled image data and integrated into inspection and document workflows.
- 05
NLP development
Fine-tuned language models for document classification, named entity recognition, sentiment analysis, and text summarisation calibrated to your domain vocabulary.
- 06
Recommendation system development
Personalisation and recommendation engines that rank products, content, or next-best actions using your behavioural and transaction data.
- 07
Data engineering
The pipelines that feed the models: ingestion, cleaning, feature stores, and warehouse loads that keep training and inference data accurate and current.
- 08
MLOps
Model versioning, drift detection, automated retraining, and deployment pipelines that keep production models accurate as your data evolves.
Ready to scope your machine learning project?
30 minutes. You walk away with a clear cost, timeline, and team. No commitment.
How we run ML projects
Why us
Why teams choose RaftLabs
- 01Senior engineers build what they scope
The engineers who assess your problem also build the solution. No bait-and-switch, no offshore handoff after the contract is signed. The team you meet in week 1 ships in week 12.
- 02Fixed price before development starts
We scope the work, calculate the cost, and lock it in writing before any development starts. A scope change is a change request: priced, agreed, or dropped. It never absorbs into the project and appears on the final invoice.
- 03100+ products shipped since 2015
Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. Track record across AI, SaaS, mobile, automation, and enterprise platforms across healthcare, fintech, logistics, and hospitality.
- 04Compliance built in from the start
GDPR, HIPAA, SOC 2 compliance requirements are scoped in week 1, not retrofitted before launch. We have shipped HIPAA-compliant ML systems for US healthcare clients and GDPR-compliant products for European markets.
What clients say
What our clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
Amer Abu Khajil
CanadaFounder, Peak Studios & Perceptional
I found RaftLabs to be the perfect partner for Perceptional, with their expertise in helping startup founders build MVPs, a free consultation, a prototype that matched my vision, and their unwavering support.
01 / 02
The stack we build ML systems on
We are not tied to one framework or one cloud. We pick the tools that fit your data volume, latency needs, and handover requirements, then document every choice so any competent data or engineering team can maintain the system. The technologies we reach for most often:
| Layer | Technologies we use | Where it fits |
|---|---|---|
| Languages and frameworks | Python, PyTorch, TensorFlow, scikit-learn, XGBoost, LightGBM | Model training, deep learning, and gradient-boosted tabular models |
| Data and pipelines | Pandas, Spark, Snowflake, BigQuery, dbt | Feature engineering, large-scale processing, and warehouse loads |
| Serving and MLOps | MLflow, Kubeflow, Docker, Kubernetes, SageMaker, Vertex AI, FastAPI | Model versioning, deployment, drift monitoring, and retraining pipelines |
| Cloud | AWS, GCP, Azure | Production hosting, managed training, and inference at scale |
The rule holds at every layer: no proprietary frameworks that lock you in, and no stack we cannot hand to your team on day one. For NLP work we build on Hugging Face's transformers; for text classification and entity extraction we fine-tune BERT, RoBERTa, and DeBERTa, the same tooling described in our NLP development service.
What machine learning development costs
We price by project, not by the hour. After a scoping session that includes a data audit and model feasibility assessment, you get a fixed quote with a defined scope, timeline, and price, so you know the number before development starts.
| Project type | Typical timeline | Cost range |
|---|---|---|
| Focused ML project, one use case, discovery, model training, validation, and API deployment | 8-16 weeks | $25,000-$60,000 |
| Larger build, custom data pipelines, multiple models, BI dashboard integration, and automated retraining | 4-9 months | $60,000-$150,000 |
What pushes cost up: sparse or poorly labelled data that needs cleaning before it is model-ready, deep integrations with legacy or on-premise systems, and strict compliance requirements such as HIPAA, SOC 2, and GDPR. What keeps it down: a narrow first use case, clean historical data, and managed cloud services such as SageMaker or Vertex AI rather than self-hosted infrastructure early on. We scope every project before pricing it, and the predictive analytics and machine learning consulting engagements follow the same fixed-cost model.
Machine learning by industry
The model changes with the domain. Compliance-heavy sectors need audit trails, explainability, and access controls designed in from the first sprint, while high-volume consumer platforms need low-latency inference and horizontal scaling. We build ML systems for:
FinTech and financial services: fraud and anomaly detection, credit and risk scoring, and transaction classification.
Healthcare and life sciences: HIPAA-compliant triage classification, clinical note extraction, and patient risk models.
Retail and e-commerce: demand forecasting, recommendation engines, and inventory optimisation.
Logistics and supply chain: route and capacity forecasting, exception detection, and delivery time prediction.
Insurance: claims triage, underwriting risk scoring, and fraud detection.
Manufacturing: predictive maintenance, defect detection, and production capacity forecasting.
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Read moreFrequently asked questions
Custom machine learning development means building a model trained on your specific data to solve your specific problem, not using a generic pre-trained model with limited customisation. Custom models outperform generic solutions when your data has patterns specific to your business, your domain, or your customer base. The development process includes data assessment, feature engineering, model selection and training, validation against held-out data, and integration into your production system.
The minimum data requirement depends on the problem. Classification models for tabular data (churn prediction, fraud detection, lead scoring) typically need 10,000-50,000 labelled examples. Time-series forecasting needs 12-24 months of historical data at the required granularity. Computer vision models need 1,000-10,000 labelled images per class. NLP models fine-tuned on a base model (BERT, GPT) need fewer examples, 100-1,000 is often sufficient for classification tasks. We assess your data during scoping and tell you exactly what we need before committing to a build.
Supervised learning: classification (yes/no, multi-class) and regression (continuous output) for prediction problems. Unsupervised learning: clustering and anomaly detection for pattern discovery without labels. Time-series forecasting: demand, capacity, and trend prediction. Natural language processing: document classification, entity extraction, sentiment analysis, and text summarisation. Computer vision: image classification, object detection, and OCR. We match the approach to the problem, not the other way around.
A focused ML project, one use case, one data source, training, validation, and deployment to one target system, typically takes 8-16 weeks. More complex projects with multiple models, custom data pipelines, and integrations with multiple systems take 4-9 months. Every project starts with a 2-3 week discovery phase to assess data quality, define success metrics, and scope the build before committing to a timeline.
We deploy ML models as REST APIs (FastAPI or Flask), containerised with Docker, and hosted on AWS or GCP. Your existing application calls the model API to get predictions. For real-time use cases, predictions are returned in milliseconds. For batch use cases, the model runs on a schedule and writes predictions to your database or data warehouse. We handle model versioning, monitoring (drift detection, performance tracking), and retraining pipelines so the model stays accurate as your data evolves.
A focused ML project, discovery, model training, validation, and API deployment, typically runs $25,000-$60,000. Larger projects with custom data pipelines, multiple models, BI dashboard integration, and automated retraining pipelines run $60,000-$150,000. We scope every project before pricing. The scoping process includes a data audit, model feasibility assessment, and a fixed-price proposal.
Work with us
Tell us what you need. We'll tell you what it would take.
We scope Machine Learning Development Company in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.