Service
Turn your raw data into reliable decisions — with ML models and MLOps pipelines that stay accurate, observable, and profitable in production.
25%
Avg. conversion lift from recommendation models
6–10wk
Typical data-to-first-model timeline
30%
Avg. inventory cost reduction from forecasting
Overview
Machine learning is only valuable when it works reliably in production — not just in notebooks. Arcifacts provides end-to-end machine learning services: from exploratory data analysis and feature engineering, through model development and rigorous validation, all the way to deployment and ongoing MLOps. We cover tabular, NLP, and vision modalities and integrate with your existing data infrastructure.
Ideal for teams with existing data assets who want to convert that data into predictions, recommendations, or automation — without the overhead of building an internal ML team from scratch.
What you get
Reliable, measurable model performance with clear baselines
Reproducible experiment pipelines and version-controlled models
Faster iteration with automated retraining and CI/CD for ML
Quantifiable business impact: cost savings, revenue lift, or efficiency gains
Deliverables
Every engagement is scoped to your exact needs — here's the full catalogue of deliverables we can provide.
Scalable ingestion, transformation, and feature engineering pipelines integrated with your data warehouse, lake, or streaming platform.
Tabular (XGBoost, LightGBM), NLP (transformers), and vision (CNNs) models tailored to your prediction or classification problem.
End-to-end infrastructure: experiment tracking (MLflow), model registry, automated training, staging, and production deployment.
Rigorous offline and online evaluation: cross-validation, backtesting, A/B testing, and shadow mode deployment.
Data drift, concept drift, and performance monitoring with alerting and automated retraining triggers.
Full model cards, runbooks, and team training sessions so your engineers can own and iterate on the work.
— Process
We align on goals, scope, and success metrics through structured workshops and stakeholder interviews.
Blueprint and UX aligned with your brand, technical requirements, and long-term scalability needs.
Agile sprints with regular demos, feedback loops, and transparent progress tracking.
Rigorous testing across devices and edge cases with performance tuning before launch.
Smooth deployment, full documentation, and knowledge transfer so your team owns the outcome.
Ongoing support, monitoring, and iterative improvements to keep pace with your growth.
We align on goals, scope, and success metrics through structured workshops and stakeholder interviews.
Blueprint and UX aligned with your brand, technical requirements, and long-term scalability needs.
Agile sprints with regular demos, feedback loops, and transparent progress tracking.
Rigorous testing across devices and edge cases with performance tuning before launch.
Smooth deployment, full documentation, and knowledge transfer so your team owns the outcome.
Ongoing support, monitoring, and iterative improvements to keep pace with your growth.
ML Stack
Work
A selection of outcomes we've delivered for clients across industries.
Built a real-time collaborative filtering and content-based recommendation system for a mid-market e-commerce platform, integrated into their product page and email flows.
25% lift in conversion rate and 18% increase in average order value.
Deployed a time-series forecasting system for a logistics company, covering 3,000+ SKUs with automatic model selection and confidence intervals per SKU.
30% reduction in overstock costs and 20% fewer stockouts.
Why us
End-to-end ML ownership — we don't hand you notebooks, we hand you production systems
Strong MLOps and reproducibility practices so experiments are auditable and repeatable
Clear metrics and business-aligned KPIs tied to actual impact, not just model accuracy
Integration-first mindset — we build around your existing data stack, not against it
We build production ML systems from the start — with monitoring, automated retraining, and clear upgrade paths — so your models stay accurate and cost-effective as data volume and complexity grow.
FAQ
Still have questions? Reach out and we'll answer directly.
Get started
Share your use case and data situation and we'll propose a pragmatic ML roadmap with realistic timelines and expected impact.
