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Production Grade Machine Learning Services for Enterprise Businesses
We're a machine learning development company helping startups, scale-ups and enterprises build production-grade ML models predictive analytics, NLP, recommendation systems, custom ML and LLM-integrated ML that move out of notebooks and into the systems your business actually runs on. Senior ML engineers, honest assessments and the kind of engineering discipline that turns ML experiments into ML products.
13+
Years of Experience
50+
Experts in Our Team
40+
Happy Customers Worldwide
250+
Projects Delivered Successfully
4.9/5 ratings
5/5 ratings
End-to-End Machine Learning Services From Strategy to Production
Whether you're shipping your first ML model, scaling predictive analytics across business units, integrating ML into existing enterprise systems or fine-tuning LLMs for domain-specific intelligence, our machine learning services cover every layer of how production ML actually gets built. Delivered by senior ML engineers and data scientists who've shipped ML at real scale not researchers who'll hand you a Jupyter notebook and call it a deliverable.
AI/ML Strategy & Consulting
Most ML initiatives don't fail at the model layer. They fail in the strategy that never asks the hard questions which problems are actually ML problems, where the data lives, whether the data is clean enough to train on and whether the predicted outcome can actually drive a business decision. We deliver ML strategy and consulting that surfaces those questions early: ML readiness assessments, use-case prioritization, data audit, build-versus-buy analysis and executive advisory that gives your leadership team the answers needed to defend the ML investment at the next board meeting.
Custom ML Model Development
Off-the-shelf ML rarely fits the problem worth solving. We build custom ML models engineered for your data, your business problem and your production constraints classification, regression, clustering, anomaly detection, time-series forecasting and the kind of bespoke modeling that turns your proprietary data into a defensible advantage. Built on PyTorch, TensorFlow, scikit-learn, XGBoost and the production engineering discipline that determines whether the model actually delivers value after deployment, not just on the validation set.
Predictive Analytics Services
Predictive analytics is where ML pays back fastest when it's engineered against problems with measurable business outcomes. We build predictive models for demand forecasting, churn prediction, customer lifetime value, dynamic pricing, fraud detection, lead scoring, inventory optimization and the kind of high-value prediction problems where a 5% accuracy improvement translates directly into revenue, savings or risk reduction. Engineered for the data you actually have, not the textbook example you wish you had.
Natural Language Processing (NLP) Services
NLP in 2026 is not just LLMs. The right NLP architecture often combines traditional NLP techniques with LLMs using each where it works best. We build NLP solutions covering sentiment analysis, text classification, named entity recognition, document summarization, intent detection, semantic search and the kind of structured-extraction work where LLMs alone are too expensive, too slow or too unreliable. Built on Hugging Face Transformers, spaCy, classical NLP libraries and LLMs combined into systems that work in production.
ML Model Optimization Services
Most production ML models leave performance, cost and latency on the table because the team that built them moved on to the next project before optimization happened. We take existing ML models and engineer them for production reality: accuracy improvements through better features and architecture, latency reduction through model distillation and quantization, cost reduction through smarter inference patterns and reliability improvements through evaluation and monitoring. Sometimes the highest-ROI ML work isn't building new models, it's making the ones you have actually pay back.
AI & LLM Integration
The most valuable ML systems in 2026 combine traditional ML with generative AI using LLMs for unstructured reasoning and classical ML for structured prediction. We engineer hybrid systems that integrate fine-tuned LLMs with ML pipelines, build LLM-powered features grounded in ML-derived signals, deploy custom-trained domain LLMs for specialized industries and design the kind of ML+GenAI architecture that delivers what neither approach delivers alone. LoRA fine-tuning, instruction tuning, RAG-enhanced ML and the production engineering that makes hybrid systems reliable.
Production-Grade ML Engineering, Not Notebook Experiments
Most ML projects die between the Jupyter notebook and production. We engineer ML the way production engineering has always been done with evaluation, monitoring, versioning, cost discipline and the operational maturity that turns ML models from quarterly experiments into systems your business actually runs on.
01
Knowing when your model is actually good enough to ship
We engineer evaluation pipelines, holdout strategies, A/B testing infrastructure and continuous quality monitoring so model quality is measured against real-world performance, not just validation set accuracy.
Highlights:
- Production evaluation pipelines
- Statistical validation rigor
- A/B testing infrastructure
01
Knowing when your model is actually good enough to ship
We engineer evaluation pipelines, holdout strategies, A/B testing infrastructure and continuous quality monitoring so model quality is measured against real-world performance, not just validation set accuracy.
Highlights:
- Production evaluation pipelines
- Statistical validation rigor
- A/B testing infrastructure
ML Solutions We Build Every Prediction Problem, Every Production Workload
Machine learning isn't one capability. It's a category that spans predictive modeling, recommendation systems, NLP, computer vision, anomaly detection and the production ML infrastructure underneath all of it. Here are the production-grade ML solutions we build, and the engineering bar we hold ourselves to on each one.
Recommendation Engines
Production-grade recommendation systems that drive measurable conversion and engagement lift product recommendations, content personalization, next-best-action engines, dynamic merchandising and the kind of recommendation architecture that combines collaborative filtering, content-based models and modern deep learning approaches. Built for real-time inference, A/B testing rigor and the lift attribution that proves the system actually works.
Predictive Models
Custom predictive models for the high-value forecasting and classification problems where ML pays back fastest demand forecasting, churn prediction, customer lifetime value modeling, lead scoring, credit risk modeling, propensity scoring and the kind of predictive ML that turns historical data into forward-looking business decisions.
Fraud Detection & Anomaly Detection Systems
Real-time fraud detection ML, anomaly detection for transactions, payments and user behavior, network intrusion detection and the kind of production ML that combines supervised learning, unsupervised techniques and rule-based logic into systems that survive both the regulator's review and the adversarial pressure of real fraud.
NLP Pipelines & Text Intelligence
Production NLP systems combine classical NLP and modern LLMs sentiment analysis, named entity recognition, document classification, intent detection, semantic search, document summarization and the kind of text intelligence that turns unstructured text into structured business data. Built on Hugging Face Transformers, spaCy, classical NLP and LLMs combined for production reliability.
Demand Forecasting & Time-Series Models
Time-series forecasting models for demand planning, inventory optimization, capacity planning, sales forecasting and the kind of forward-looking prediction that drives operational and financial decisions. Built on Prophet, ARIMA, LSTM, Transformer-based forecasters and the ensemble approaches that real-world forecasting actually requires.
Dynamic Pricing & Optimization Models
ML-powered dynamic pricing for ecommerce, hospitality, travel and marketplaces combining demand elasticity modeling, competitive intelligence, inventory constraints and customer segmentation into pricing engines that optimize revenue without sacrificing brand trust. Engineered for the speed and reliability dynamic pricing actually demands in production.
Document Intelligence & OCR Pipelines
ML-powered document processing combines OCR, structured extraction, classification and human-in-the-loop workflows invoice processing, contract analysis, claims processing, KYC/AML document review, medical record extraction and the kind of document intelligence that replaces hours of manual review with seconds of automated extraction.
Customer Segmentation & Behavioral ML
Unsupervised learning and behavioral ML for customer segmentation, cohort analysis, lifetime value clustering, propensity modeling and the kind of customer intelligence that turns marketing from generic outreach into individually-targeted engagement. Built into your CRM, marketing automation and product analytics not as standalone dashboards.
Why Founders and Entrepreneurs Pick Solvios as Their Machine Learning Development Company
We're not just another ML consulting company. We're the production-grade ML partner founders and entrepreneurs hire when the ML investment has to be measurable, defensible at the next board meeting and built on the engineering foundations that hold up under real users, real data drift and real audit scrutiny not just at the validation set.
Production ML Engineering, Not Notebook Theater
We ship ML to production. Evaluation pipelines, monitoring, drift detection, feature stores, cost discipline and the engineering practices that turn ML experiments into ML systems your business actually runs on.
Senior ML Engineers, No Bench Warmers
You work directly with senior ML engineers, data scientists and MLOps specialists who've shipped models at real scale not juniors learning scikit-learn on your retainer.
Hybrid ML + GenAI Expertise
The most valuable ML systems in 2026 combine traditional ML with LLMs. We engineer hybrid systems that use each where it works best: predictive ML for structured prediction, LLMs for unstructured reasoning combined into production architectures.
Engineering DNA Behind Every ML Decision
We're a technology company first. MLOps, data engineering, cloud infrastructure, security and compliance the technical depth pure-play data science consultancies usually outsource, we build in-house and bake into every engagement.
Transparent, Predictable Delivery
Clear sprints, honest reporting, no surprise infrastructure bills. You always know what's shipping, what it's costing and what it's earning in measurable outcomes.
Built for What's Next in ML
We architect for foundation models, multimodal learning, on-device ML, real-time inference and the ML patterns shaping the next five years not just the playbook from when scikit-learn was the state of the art.
Our Machine Learning Services Delivery Process
Most ML engagements don't fail technically they fail because the strategy never asked whether the problem was actually an ML problem, the data wasn't ready to train on or the model never had an evaluation harness behind it. Our machine learning services follow a structured delivery methodology designed to surface those problems early and turn ML from an experiment into a production system.
Here's exactly how it works.
01
Discovery & ML Readiness Assessment
We audit your data, workflows, technical infrastructure and business goals to define which ML use cases are actually ready to build and which need foundational work first. The deliverable is an honest picture of where ML will pay back and where it won't yet.
02
Strategy & ML Architecture
We design the target ML architecture, algorithm selection strategy, data pipeline requirements, evaluation framework and the success metrics that define when the model is actually ready for production. A roadmap your team can execute against measurable outcomes.
03
Data Engineering & Feature Engineering
We build the data foundation. The ML system depends on feature pipelines, feature stores, data quality validation, training data preparation and the kind of data engineering that determines whether the model actually works on production data, not just clean training data.
04
Model Development & Validation
We develop the ML model in iterative sprints, baseline models, feature experimentation, algorithm comparison, hyperparameter tuning, holdout evaluation and the kind of statistical rigor that determines whether the model is actually better than the baseline.
05
Production Deployment & Hardening
We deploy the model to production with a controlled rollout shadow mode first, then limited traffic, then full deployment with monitoring, rollback capability and the operational discipline that production ML launches actually require.
06
MLOps & Continuous Improvement
We operate the ML system in production continuous monitoring, drift detection, automated retraining, model versioning, cost optimization and quarterly architecture reviews that surface the next improvements before model decay surfaces in customer outcomes.
Flexible Engagement Models to Hire Our Machine Learning Development Company
Your ML engagement doesn't fit a template, and the contract shouldn't either. The right way to hire a machine learning development company depends on your scope, your timeline and your in-house team's depth. Three models, all built for how production ML actually gets delivered.
You need ML engineers who know your data, your environment and your business goals as well as your in-house team building for your ML roadmap, not splitting attention across five other clients. The Dedicated Team model gives you a fully embedded ML unit accountable to your production ML outcomes.
- Right for you if
You're running a multi-quarter ML roadmap, building ML-powered products from scratch, scaling ML across business units or augmenting your in-house data science team without the cost and lead time of full-time hires.
- What you get
Hand-picked ML engineers, data scientists, MLOps specialists and a delivery lead working only on your initiative. Sprint planning, model reviews and weekly reporting run on your calendar and your tooling.
- Economics
Monthly retainer. No surprise invoices, no scope-creep billing. Team composition flexes as your ML roadmap evolves.
Typical profile
3-8 engineers
6-month minimum
Scales with 30-day notice
Not sure which model fits your ML initiative?
Most companies start with one and evolve into another as their ML roadmap matures. Let's figure out the right starting point together.
Ready To Ship ML That Actually Produces Outcomes?
Building Production-Grade Machine Learning for the Businesses That Will Define the Next Decade. The companies investing in production-grade ML engineering now won't be the ones explaining stalled pilots, models that never shipped and ML investments without outcomes in two years' time.
Industries We Deliver Machine Learning Services For
ML use cases look different across industries. The data shape, the regulatory posture, the business outcome and the operational rhythm all change the engineering. These are the verticals where we've shipped ML in production and know what real ML deployment looks like.
HIPAA-compliant ML architectures, PHI-aware feature pipelines, audit-ready ML logging, clinical risk prediction, medical document intelligence, hospital readmission models, prior-authorization automation and the kind of safety engineering and explainability that healthcare ML actually demands. We ship ML in regulated healthcare. We know what compliance looks like in a live ML system, not just in the framework documentation.
Explore MoreHIPAA-compliant ML architectures, PHI-aware feature pipelines, audit-ready ML logging, clinical risk prediction, medical document intelligence, hospital readmission models, prior-authorization automation and the kind of safety engineering and explainability that healthcare ML actually demands. We ship ML in regulated healthcare. We know what compliance looks like in a live ML system, not just in the framework documentation.
Explore MoreFrequently Asked Questions
Honest answers to the questions every founder, CTO and head of data asks before they hire a machine learning development company. If something isn't covered here, our ML architects will walk you through it on a discovery call, no sales pitch, no fluff.
Look beyond the model accuracy claims. The right machine learning development company asks more questions than it answers in the first conversation about your data quality, your business outcome, your existing infrastructure and what a model failure would actually cost you. Evaluate on production ML depth in the discovery phase, transparency about whether ML is the right approach for your problem, data engineering rigor (not just modeling fluency) and whether they push back constructively when they disagree. Anyone who promises a specific accuracy number before they've seen your data isn't the right partner.
End-to-end machine learning services cover ML strategy and consulting, custom ML model development, predictive analytics, NLP, ML model optimization, generative AI and LLM integration, MLOps and ML infrastructure, model evaluation and monitoring, drift detection, and responsible ML governance. The best engagements also include a data readiness assessment before any model work begins because decisions made in week one shape ML outcomes for years.
A focused ML proof-of-concept: $20,000-$60,000. A mid-complexity ML build (predictive model, NLP pipeline, recommendation engine): $60,000-$250,000. An enterprise ML platform built with multiple models, custom MLOps and feature stores: $250,000-$1M+. Ongoing ML operations typically run $8,000-$60,000 per month depending on model count and infrastructure scale. The number that matters isn't the build cost, it's the outcome the model produces. We provide detailed estimates after a readiness assessment.
A focused ML proof of concept: 6-10 weeks. A production ML model (prediction, classification, recommendation): 3-6 months. An enterprise ML platform with multiple models, custom infrastructure and full MLOps: 9-18 months, executed in phases. We provide milestone-based timelines in writing before development begins.
Machine learning is the broader discipline of training models to recognize patterns in data and make predictions or decisions supervised learning, unsupervised learning, deep learning, reinforcement learning. Generative AI is a specific category of ML focused on generating new content text, images, audio, video typically using foundation models like LLMs. Most valuable production AI systems in 2026 combine both: traditional ML for structured prediction tasks where it's faster, cheaper and more reliable, and generative AI for unstructured reasoning where its flexibility shines. We engineer hybrid systems that use each where it works best.
Data quality is engineered as a continuous practice, not assumed. Data audits at the start, schema validation in pipelines, data quality monitoring in production, drift detection across both data and predictions, feature store discipline that catches silently broken features and the kind of data engineering rigor that determines whether the model actually works on production data or only on the clean training set. Most ML failures are data failures.
Fairness and bias evaluation is wired into the development process. Bias audits during data exploration, fairness testing across protected attributes, explainability through SHAP and LIME, sensitivity analysis and continuous fairness monitoring in production. Responsible ML isn't a quarterly review, it's an engineering practice. Particularly for regulated industries (healthcare, finance, hiring), this is non-negotiable.
We measure what actually matters: accuracy on holdout data (not training data), business outcome metrics (revenue lift, cost savings, time reduction), operational metrics (latency, cost per prediction, drift indicators) and statistical significance against baselines. Vanity metrics like "we shipped X models" don't pay for themselves.
Yes. We start with a technical audit model quality, data pipeline health, MLOps maturity, monitoring posture and accumulated technical debt no one's documented. We give you an honest picture of what you've inherited and a clear remediation roadmap. Sometimes the answer is rebuild. Sometimes it stabilizes and hardens. Sometimes the existing work is better than it looks.
Three models: Dedicated ML Team (for long-term, multi-quarter ML roadmaps), Time & Material (for evolving scope and iterative ML exploration) and Fixed Cost (for well-scoped ML projects with defined deliverables). When you hire our machine learning development company, we recommend honestly based on your situation not based on which model is most profitable for us.
MLOps is wired in from day one, not added at the end. Model versioning, automated deployment pipelines, monitoring across model performance and data quality, automated retraining triggers, rollback capability and continuous drift detection. We treat ML systems with the same operational discipline as production software because that's what they need to actually stay healthy.
Yes. Hybrid ML \+ generative AI is one of our specialties. We engineer systems that combine fine-tuned LLMs with traditional ML pipelines, build LLM-powered features grounded in ML-derived signals, deploy domain-specific LLMs and design the kind of ML+GenAI architecture that delivers what neither approach delivers alone. Production-grade hybrid systems, not stitched-together demos.
Yes. MLOps and ML operations is one of our core offerings: continuous monitoring, drift detection, automated retraining, model versioning, cost optimization, incident response and quarterly ML architecture reviews. You can hand over the operational load entirely, or run it alongside your in-house team. SLAs are defined and reported on every month.
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