Metrics Before the First Line of Code
Success criteria are agreed with your team upfront: accuracy, response time, and business impact. PoC results are judged against them, not interpreted after the fact.
Machine Learning Consulting Services
ML consultants for scale-ups and enterprises: from data audit to a working PoC in 6 weeks and a model in production with known costs.
80
Certified Engineers
15
Countries with Active Clients
150+
Successfully Completed Projects
How We Differ from Other Machine Learning Consulting Firms
Many machine learning consulting firms focus only on the model. Our machine learning consulting service looks at the entire product: data, infrastructure, inference costs, and performance under growing traffic. With in-house ML consultants, DevOps, and Cloud engineers, we evaluate every recommendation against real-world software, not a data scientist’s notebook. Within 6 weeks of getting access to your data, you receive a working PoC and an ROI assessment. If implementation is worthwhile, the same team takes the model to production, with no handoffs and no lost context.
Costs Known Upfront
We estimate monthly inference, infrastructure, and retraining costs before development begins.
Production-Ready From Day One
Monitoring, automated deployments, and retraining are built in at the PoC stage, so degradation is caught early.
Senior Team in 5 Days
Experts join within a week, no hiring needed. Need more capacity? We add 5+ engineers in 1–2 weeks.
Machine Learning Project Risks
and How We Address Them
We examine the pitfalls that cause scale-up companies to waste time and money on ML and explain our approach to protecting your investment from the start.
Challenge
You promised the board an ML feature but aren’t sure it will work.
Risk
The project’s failure becomes your personal failure.
Solution
A data-backed business case and a PoC on your data let you justify the decision with facts, not promises.
Challenge
You can’t tell whether a contractor really knows ML.
Risk
Six months and your budget go to a team that says “yes” to everything.
Solution
We define success metrics upfront, explain decisions in business terms, and say “no” when an idea doesn’t work.
Challenge
You need to share sensitive data with a contractor.
Risk
One breach or GDPR violation can cost more than the entire ML initiative.
Solution
We work within your environment with role-based access and build in GDPR and SOC 2 requirements.
Challenge
The model could fail in front of customers.
Risk
A fabricated chatbot answer or a wrong scoring decision damages your brand and creates legal exposure.
Solution
We test edge cases, add safeguards and explainability, and keep humans in control of critical decisions.
Challenge
GPU and LLM API bills keep growing, and no one can explain why.
Risk
ML becomes an expense you can’t forecast or justify to the CFO.
Solution
We estimate inference costs before development and optimize them as workloads grow.
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What Our ML Consulting Covers
From strategy and data audits to MLOps and cost control, we handle the entire process or individual stages where your team lacks the necessary expertise.
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ML Strategy & Data Readiness
Our machine learning strategy consulting service helps identify where machine learning can generate revenue and whether your data is ready for it:
Audit of data quality and completeness;
Identification of business use cases with the highest expected ROI;
Assessment of complexity and costs;
Implementation roadmap.
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Proof of Concept & Feasibility
03
Model Review & Audit
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MLOps Consulting
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LLM & GenAI Consulting
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ML Cost Optimization
Not Sure Whether Machine Learning Is Right for Your Business?
Tell us about your goals and data. In one machine learning consultation, we’ll assess feasibility, risks, and costs, and tell you whether a PoC is worth it.
Machine Learning Consulting
Across Industries
Each industry has its own data, regulations, and cost of error. We account for them from the first assessment, not after launch.
FinTech
Credit scoring, fraud detection, and financial risk assessment, with explainable models that meet regulatory and PCI DSS requirements.
Healthcare
Medical data analysis, administrative automation, and clinical decision support, with HIPAA and GDPR compliance built into the ML architecture.
MedTech
Risk assessment, condition prediction, and personalized recommendations, with strict data quality control and explainable results.
Insurance
Document analysis, claims classification, underwriting risk assessment, and fraud detection, with critical decisions kept under specialist control.
Retail
Recommendations, smart search, personalization, and demand forecasting that hold up during seasonal peaks.
Logistics & Transportation
Demand forecasting, route optimization, predictive maintenance, and document recognition, prioritized by measurable savings.
Machine Learning Proof of Concept in Six Weeks
Validate your machine learning idea using your own data before investing in full-scale development.
Your Data, Not a Demo Dataset
Models that shine on public datasets often stumble on missing values and noise. That’s why the PoC runs on real data in your environment.
A Baseline to Beat
Every model is compared with a simple rule or statistical approach. If the simpler option works just as well, you’ll hear it from us.
Cost per Prediction
Inference costs are estimated under your expected workload, so the monthly expense is clear before you decide to launch.
Documented Risks
Edge cases, drift risk, and model limitations are tested and documented, so known risks are addressed before release.
Three Honest Outcomes
Go: you get an architecture and MLOps plan. Pivot: the direction is promising, but the use case needs adjusting. No-go: not worth it yet, and you know why.
MLOps Consulting: Keeping ML Models Stable in Production
Our MLOps service helps monitor and update the machine learning model after deployment to maintain its performance, while keeping data security and model governance under control.
Deployment & Versioning
We automate CI/CD for models and track versions of models, data, and code. Releases do not depend on a single person, and we provide a quick rollback to the previous stable version.
Monitoring & Drift
We track latency and changes in input data, and monitor prediction quality based on available actual outcomes. Alerts help detect signs of model performance degradation earlier.
Retraining
We configure automated retraining on a schedule or in response to drift signals. Before updating, we validate the quality of the new model version.
Cost Control
We optimize infrastructure and models for actual workloads. We monitor GPU and cloud costs and reduce unnecessary expenses as workloads grow.
Data Security & Compliance
We work within your environment using role-based access and build in GDPR, HIPAA, or PCI DSS requirements, depending on your industry.
Explainability & Audit Trail
We document every model change, show what drives its decisions, and test for bias in sensitive cases such as scoring and insurance.
How Much Does Machine
Learning Consulting Cost?
The cost depends on the engagement model, data volume, and task complexity, so you will receive a preliminary estimate after the initial consultation.
ML Audit & Strategy
Fixed scope of work: an audit of data or an existing model, evaluation of business use cases based on expected ROI, and an implementation roadmap.
Proof of Concept
Six weeks with agreed deliverables: a prototype built using your data, a production cost forecast, and a go/no-go recommendation.
ML Expert on Demand
Monthly consulting service: a senior machine learning engineer or data scientist without the need for hiring or long-term commitments.
Get a Cost Estimate for Your ML Project
Share your goals and data sources, and you’ll receive a scoped estimate: recommended format, timeline, and expected costs.
ML Consulting Engagement Models
Choose an engagement model that fits the stage of your ML initiative and adjust it as your needs change.
Fixed Price
Suitable for audits and PoCs: we agree on the scope, timelines, and deliverables before the project begins, so the budget remains fixed within those boundaries.
Time & Materials
Suitable when requirements evolve during the project: you pay for the actual time spent and can flexibly adjust priorities.
Dedicated Team
Suitable for the entire journey from PoC to production and ongoing support: our dedicated team service provides senior machine learning specialists who start within 5 days and can scale up by 5+ engineers within 1–2 weeks.
Advantages
Why Choose ZentixSoft as Your Machine Learning Consulting Company
The value of our services lies not only in the code but also in our approach: each principle reduces risks and brings the model closer to real-world use in your product.
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One Team, No Handoffs
The same senior team handles your project from audit to production. You avoid losing context and wasting time searching for another contractor.
We Build Our Own AI Products
We develop our own AI-native LMS and integrate AI into our own ERP. This means our recommendations come from the experience of a product team that bears the cost of production mistakes firsthand.
Simple Beats Complex
Before recommending ML, we compare it with a simple rule or statistical approach. If a simpler solution performs just as well, you save money by choosing it.
Up to 30% Faster Delivery
Our proprietary frameworks and reusable solutions accelerate development by up to 30%.
55% of Clients Stay or Return
55% of our clients continue working with us or return with new projects. The average partnership lasts 30 months.
How We Take an ML Project from PoC to Production
Each stage of the process has an agreed timeline and clearly defined deliverables, so you know in advance what you will receive and when, while progress remains transparent.
01
Discovery Call
We discuss your business goal, data, and constraints, and tell you upfront whether machine learning makes sense for the task.
02
Audit & Assessment
We assess data readiness, identify use cases with the highest expected ROI, and estimate costs so you can decide whether to start a PoC.
03
Proof of Concept
Within 6 weeks, we build a prototype on your data and validate it against agreed metrics to reach a go/no-go decision.
04
Production Roadmap
We prepare the architecture, MLOps plan, and model cost forecast, so you can choose whether to implement in-house or with our team.
05
Knowledge Transfer & Support
We document the solution, train your engineers, and stay involved when needed, so the model keeps performing after launch.
Our team
Meet Our Machine Learning Consultants
Senior ML engineers, data scientists, MLOps, and Cloud experts with experience in FinTech, HealthTech, and EdTech. We deploy models to production ourselves and build our own AI products.
CEOAgness
CTOAndrew
HRDYevhenii
Project ManagerOlha
Full Stack DeveloperDmytro
Full Stack DeveloperMarko
Team LeadYehor
Content LeadMaksim
Project ManagerYuliia
BDMPavlo
Project ManagerOlga
Full Stack DeveloperYurii
Full Stack DeveloperVitalii
Team Lead | DevOpsOleg
Backend DeveloperMaks
Team Lead | DevOpsOleg
QA EngineerElina
Full Stack DeveloperOleksandr
RecruiterOksana
CSOOlga
CMOSolomiya
Project ManagerYeva
Full Stack DeveloperAnastasiia
Tender ManagerYurii
Head of DeliveryKristina
UI/UX DesignerVladyslav
Business AnalystOlena
Our Machine Learning
Consulting Tech Stack
Proven frameworks, LLMs, MLOps tools, and cloud platforms we recommend and work with to take machine learning from PoC to production.
Machine Learning
PyTorch
TensorFlow
Scikit-learn
XGBoost
LLMs & RAG
OpenAI
Anthropic Claude
Llama
LangChain
MLOps & Evaluation
MLflow
Weights & Biases
Giskard
LangSmith
Cloud & Infrastructure
AWS
Azure
Google Cloud
Kubernetes
Trusted by These Companies
What Our Clients Say
About Working with Us
Hear from clients who have worked with us from the initial hypothesis to a model running in production. These are stories of partnerships built on trust and results.
They adapted quickly to changing priorities
The team quickly understood our goals and translated them into practical solutions. Their ability to adapt and move fast made the collaboration smooth and highly productive.
Joseph F.
Ozeaon | Portugal
Joseph F.
Ozeaon | Portugal
Their e-commerce expertise made a real difference
Zentix helped us build and launch our e-commerce platform with great attention to detail. The team was responsive, professional, and easy to work with throughout the entire process.
Amir B.
Servicom | Sweden
Amir B.
Servicom | Sweden
Zentix Soft built a tool for how a staffing agency actually operates, not a generic tool adapted to fit
Zentix Soft's work helped the client reduce their time-to-placement and administrative overhead. The team delivered on schedule, communicated clearly, and was responsive to the client's needs. Moreover, Zentix Soft's ability to build a system custom to the client's specific workflow stood out.
Max B.
COO, Zero Task Labs
Responsiveness and dedication
Responsiveness and dedication to delivering high-quality services were outstanding.
Maria А.
ARGUNOVA | Ukraine
Maria А.
ARGUNOVA | Ukraine
They delivered great results and provided useful support throughout the project
Zentix's work met the client's expectations. The team provided timely deliverables and professional responses throughout the engagement. Their daily online meeting, constant updates on the project's progress, and excellent results were remarkable. Zentix approached tasks promptly and creatively.
Andrey H.
Bestclevers | UK
Andrey H.
Bestclevers | UK
When you find a team that gets your business, you keep them close
Zentix Soft's work helped the client increase sales, conversions, and inquiries. The team was responsive, attentive, and communicative throughout the engagement. Moreover, Zentix Soft understood the client's business and delivered a warm and beautiful design that aligned with their vision.
Andrii K.
CEO, Happy Paws LLC
The team delivered exactly what we needed
The team delivered exactly what we needed — high-quality solutions and smooth collaboration from start to finish.
Alex L.
Wavory | Cyprus
Alex L.
Wavory | Cyprus
Worked in sprints
Zentix delivered their development work on time, which was an excellent start for the client. The team worked in sprints, updated the client weekly, and delivered tasks on schedule.
Andrew R.
RaDevs | Estonia
Andrew R.
RaDevs | Estonia
Any profile mismatches were addressed immediately with alternative candidates
Zentix Soft successfully placed specialists who contributed to the end clients' projects. The team provided structured onboarding support to ensure a smooth integration into the client's team and processes. Zentix Soft's responsiveness and ability to meet requirements stood out.
Yulia N.
Marketing Manager, OnHires
Their communication remained clear and professional at every stage
The new platform improved visibility across client relationships and reduced the client's administrative overhead. Zentix Soft demonstrated a thorough understanding of the business operational needs, enabling them to deliver a genuinely usable system from day one. Overall, the project was a success.
Anonymous
CEO, IT Company
The company had a team that was well-versed in creating the product I needed
Zentix successfully implemented the product quickly. The team was responsive and communicated regularly via email and virtual meetings. The client was impressed with the team's skilled resources and high technical expertise.
Vitalii D.
CEO, RD2
Everything went well
Zentix Soft delivered a detailed audit report that provided the end client with a clear and prioritized roadmap for their redesign. The team identified accessibility issues and inconsistencies, saving time in the subsequent design system consolidation phase. Moreover, Zentix Soft's speed stood out.
Vladymyr H.
COO & Co-Founder, Yarandin
Their ability to translate operational nuance into system logic was consistently strong throughout
The ERP system improved visibility into project profitability, reduced financial reconciliation time, and automated management reporting. Zentix Soft structured the engagement across clearly defined delivery phases and translated the client's operational nuances into system logic.
Anonymous
Marketing Manager, OmiSoft
They ultimately exceeded my expectations
Zentix Soft delivered all MVP features on time and within budget. The platform was pilot-ready, and the wearable device had moved on to live patient testing. The team was organized, responsive, and easy to work with. They were always available to answer questions and address feedback.
Inna M.
Co-Founder & Product Lead, BraveShe
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Turn Your ML Idea into a Model That Runs in Production
Start with a data audit or a six-week PoC. Get senior expertise on your project within 5 days and know the model's cost before development begins.
FAQ
A machine learning consultant provides a service that helps determine where machine learning can deliver business value, assess data, select algorithms, and turn ideas into working models. Unlike development, the main deliverables are insights and informed decisions about what to build, how to build it, and how much it will cost.
We advise on predictive analytics and forecasting (regression models for demand and churn), classification and scoring, customer segmentation through clustering, anomaly and fraud detection, document processing with NLP, and optimization tasks such as pricing, routes, and predictive maintenance. We select use cases based on expected ROI and your business needs rather than trends.
We start by assessing the condition of your data. An audit and analysis reveal what can be built with your existing data and what needs to be improved or changed in your data collection processes for the model to work.
The cost of our services depends on the engagement model, data volume, and task complexity. We offer data audits and six-week PoCs with an agreed scope, as well as monthly engagements with an expert. You will receive an initial estimate after our first conversation.
MLOps refers to the processes and tools used to maintain model quality and stability in production, including automated deployment, monitoring, drift detection, and retraining. Without them, declining model performance may go unnoticed until business metrics begin to suffer.
We work within your infrastructure using role-based access controls and manage data transfers to external services. We incorporate applicable GDPR, HIPAA, and PCI DSS requirements into the architecture and processes from day one.
We document every model change for compliance purposes, explain the factors behind its decisions, and test for bias in sensitive use cases such as credit scoring, insurance, and healthcare.
A large consulting firm is suitable for enterprise-wide transformation, while a freelancer may be a good fit for a one-off, narrowly defined task. For a model intended for production, consider a company that combines consulting, ML expertise, and engineering capabilities.
Yes. After a Go decision, the same team takes the model into production and integrates it into your software. This means you avoid losing context or spending time searching for another contractor.
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