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Machine Learning Consulting
Machine learning systems either scale to handle production traffic or they become expensive experiments that never deliver ROI. The difference comes down to architecture decisions made in week one.
You need partners who've already debugged the integration failures, data drift issues, and scaling bottlenecks that kill most implementations. Webisoft's Montreal-based team of 90%+ senior engineers builds production systems processing millions of transactions daily across regulated industries.
Our machine learning consulting delivers working code designed for your specific business constraints, while competitors waste months on pilots that never ship.
AI 005
Recent industry data shows 85% of machine learning projects fail. Companies that work with ML consultants avoid these failures and deploy faster. Every month without expert guidance means lost revenue opportunities and growing technical debt.
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Clear ML Strategy From Day One
Poor planning destroys more ML projects than technical challenges ever will. In fact, 42% of companies canceled most of their AI initiatives in 2025, wasting millions in development costs. Most teams discover their model doesn't fit business requirements after months of building. However, consultants identify these misalignments during initial planning, saving you from expensive rework down the line.
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Faster Deployment and Time-to-Market
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Lower Operational Costs Sooner
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Scalable Architecture That Prevents ML Debt
72% of US enterprises now consider ML standard IT operations, but most built systems that can't scale beyond pilot projects. Poor architecture decisions made early become exponentially expensive to fix later. Expert consultants design systems that grow with your business from the start.
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Quicker, Measurable Return on Investment
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Data Quality Issues Kill Projects Before They Start
Most teams spend 60-80% of project time on data preparation that they didn't anticipate. Consultants assess data readiness before you write a single line of code. They've seen every data nightmare and know how to fix problems that would otherwise derail your entire initiative.
Webisoft delivers end-to-end machine learning services from strategy to production deployment. We've built systems that process millions of transactions daily. Your challenge becomes our engineering blueprint.
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ML consulting & strategy building
We follow an expert AI app development process, like mapping your technical architecture before writing a single line of code. Our consultants audit your data infrastructure, validate model feasibility, and design MLOps pipelines that scale. Companies using strategic ML guidance achieve 25-45% cost reduction through optimized implementation. Skip the pilot-project trap and build production systems from day one.
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ML strategy development
We build technical roadmaps that connect business objectives to deployable ML systems. Our strategists assess your data maturity, infrastructure gaps, and competitive positioning before recommending specific algorithms. 70% of global enterprises now use AI in at least one function. Without a strategy, you're building features competitors have already shipped.
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Data assessment and preparation
Teams waste over 60% of project time on data preparation as they didn't plan for properly. Missing values, class imbalances, and feature engineering problems surface after you've already committed resources. Our machine learning consulting experts audit your datasets first, identifying quality gaps and building transformation pipelines before training begins.
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MLOps consulting
Most models work perfectly on laptops but crash in production under real load. Teams struggle with deployment pipelines, model versioning, and monitoring drift. We build automated CI/CD systems that detect performance degradation before customers notice. Your models stay accurate while competitors scramble to fix broken predictions.
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Machine learning development
Most teams build models that work locally but fail when handling real user traffic. Production systems need automatic failover when services go down, response caching to reduce latency, and gradual rollout mechanisms for safe deployment. Our machine learning consulting builds infrastructure that manages concurrent requests and maintains consistent performance under load.
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Enterprise machine learning as a service
Building internal ML infrastructure costs millions and takes years before delivering value. Your engineering team spends time managing Kubernetes clusters instead of solving business problems. We deliver complete machine learning consulting as a managed service from data pipelines to production deployment, so your team focuses on strategy while we handle operations.
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Model maintenance and monitoring
Production models degrade silently until customer complaints surface the damage. Distribution shifts change prediction accuracy without triggering alerts in standard monitoring tools. Our machine learning consulting team implements drift detection systems that track feature distributions, retrain schedules, and automated rollback mechanisms before performance drops.
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Custom ML model development
Generic frameworks fail when your data patterns don't match standard use cases. You need models trained specifically on your domain's edge cases and business constraints. We architect custom neural networks, ensemble methods, and hybrid algorithms that solve your exact problem, not approximate solutions adapted from someone else's requirements.
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Machine learning software integration
Your existing database expects structured tables, while your model outputs probability scores in different formats. Type mismatches cause runtime errors during data exchange. We create transformation layers with validation rules that convert model predictions into formats your current systems already process. No rewrites needed for existing code.
Our delivery process follows industry-standard methodologies that ensure your ML project delivers measurable business value. We guide you through six structured phases with built-in validation checkpoints that prevent costly pivots and ensure alignment with your business objectives.
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Step 1: Business Understanding & Technical Feasibility
We define your business objectives and translate them into measurable ML success criteria. Our consultants audit your data infrastructure, validate data quality, and assess technical feasibility. You'll know if your use case is viable and receive a feasibility report with projected ROI before spending on development.
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Step 2: Data Preparation & Strategy Development
We design a complete ML strategy from data pipelines to deployment architecture. Our team handles data cleaning, transformation, and feature engineering while addressing quality issues and class imbalances. We select appropriate algorithms, define your technology stack, and create a detailed roadmap connecting business goals to deployable systems.
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Step 3: Model Development & Proof of Concept
We build a working proof of concept using a subset of your data to validate our approach before full-scale investment. This phase tests multiple algorithms, compares performance, and demonstrates the solution works with your actual constraints. You receive a functioning POC with performance metrics and technical documentation.
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Step 4: Model Evaluation & Business Validation
We rigorously evaluate POC results against your defined success criteria before committing to production. This critical phase tests model performance against business KPIs, validates results with stakeholders, assesses interpretability, and identifies potential limitations. We make an informed go/no-go decision based on projected ROI and real-world scenario testing.
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Step 5: Production Development & Integration
We develop production-ready systems with enterprise-grade error handling, automated testing, and CI/CD pipelines. Our team integrates models into your existing workflows using REST APIs or message queues, implements security measures, and conducts performance optimization. Your applications consume predictions without rewriting existing business logic.
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Step 6: Deployment & Continuous Monitoring
We deploy models with gradual rollout mechanisms and establish monitoring for data drift, prediction latency, and accuracy degradation. Automated alerts notify us when performance drops below thresholds. We handle retraining, updates, and optimization continuously while tracking business metrics to ensure sustained ROI.
Production machine learning demands stability, speed, and control. Our stack prioritizes proven tools that scale reliably, reduce risk, and keep models performing under real business pressure
We build model serving infrastructure that handles thousands of predictions per second while maintaining sub-100ms response times.
Our team creates dashboards that turn complex model outputs into clear visualizations stakeholders actually understand and use daily.
On-device deployment eliminates cloud dependencies and delivers instant predictions even without internet connectivity for your users.
Our engineers store both structured business data and high-dimensional embeddings in systems optimized for real-time feature retrieval.
Managed platforms eliminate infrastructure headaches while providing enterprise-grade security, compliance, and monitoring capabilities teams need.
Our machine learning consulting automates everything from training pipelines to production deployments, ensuring models ship faster without errors.
We organize model documentation, experiment logs, and team knowledge in systems that make critical information instantly searchable.
Automated testing catches data quality issues, model drift, and performance regressions before they ever impact your customers.
Every failed ML project shares the same warning signs. We've debugged enough production disasters to recognize fatal flaws before you waste months building. These challenges appear in every industry.
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Data Overload
Companies collect millions of customer interactions, purchase histories, and behavioral signals daily. Our machine learning consulting organizes this chaos into structured datasets. We transform scattered information into training-ready inputs that models actually understand and learn from effectively.
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Complexity in Model Development
Testing dozens of algorithms to find what works burns valuable time and budget. Our machine learning consulting runs systematic experiments comparing performance across approaches. We identify the best-fit solution faster, delivering accurate predictions without endless trial-and-error cycles.
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Integration into Existing Systems
CRM platforms, inventory software, and payment systems weren't built for AI models. We create smooth connections between machine learning consulting tools and existing infrastructure. New capabilities enhance current workflows instead of forcing complete system overhauls and expensive migrations.
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Data Quality and Preprocessing
Customer records have blank fields, orders contain duplicate entries, and dates follow inconsistent formats. We implement automated checks that spot these issues before training begins. Fixed data means models learn actual patterns instead of memorizing errors and noise throughout development.
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Scalability of Solutions
Pilot systems handle 500 daily predictions perfectly but crash at 5,000 requests. We build infrastructure that automatically adds computing power during traffic spikes and reduces costs during quiet periods. Performance stays consistent whether serving hundreds or millions of predictions.
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Model Monitoring and Maintenance
A recommendation engine suggests products customers bought two months ago instead of trending items they actually want now. We track when predictions stop matching current behavior, pinpoint exactly what changed in customer patterns, and update models before revenue drops.
Our machine learning consulting delivers custom solutions that solve real operational challenges across sectors. Every industry faces unique data patterns, compliance requirements, and business pressures demanding specialized approaches.
From diagnosis accuracy to patient care optimization, we help healthcare organizations use machine learning to improve clinical outcomes while reducing operational costs. Our solutions handle sensitive medical data with strict HIPAA compliance, integrating with existing EHR systems and clinical workflows. Here is what we offer:
From fraud prevention to algorithmic trading, our machine learning consulting helps financial institutions detect threats, optimize portfolios, and automate compliance processes. We build systems that process millions of transactions in real-time while meeting strict regulatory requirements like PCI DSS standards and voluntary requirements like SOC 2. Here is what we offer:
From predictive maintenance to quality control, we help manufacturers reduce downtime, eliminate defects, and optimize production lines through intelligent automation. Our machine learning consulting integrates with IoT sensors, SCADA systems, and existing manufacturing execution systems to deliver actionable insights. Here is what we offer:
From route optimization to warehouse management, we help logistics companies reduce delivery times, cut fuel costs, and improve customer satisfaction through intelligent planning. Our solutions process real-time traffic data, weather patterns, and historical delivery records to make smarter operational decisions. Here is what we offer:
From network optimization to customer experience, our machine learning consulting helps telecom providers reduce churn, predict equipment failures, and manage network capacity efficiently. We analyze call data records, network logs, and customer behavior patterns to deliver performance improvements across infrastructure and services. Here is what we offer:
From grid management to consumption forecasting, we help energy companies balance supply and demand, reduce waste, and integrate renewable sources into existing infrastructure. Our machine learning consulting processes smart meter data, weather forecasts, and historical usage patterns to optimize energy distribution across networks. Here is what we offer:
Hiring machine learning consultants accelerates implementation, reduces costly mistakes, and delivers production-ready solutions faster than building internal teams from scratch. Expert guidance transforms complex AI challenges into measurable business outcomes.
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Faster Time-to-Market
Our machine learning consulting eliminates months of trial-and-error experimentation by applying proven frameworks from hundreds of deployments. We identify optimal algorithms quickly, bypass common pitfalls, and deliver working models in weeks instead of quarters while maintaining accuracy standards.
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Reduced Infrastructure Costs
Machine learning consulting experts architect cost-efficient systems from day one, preventing expensive mistakes like over-provisioned cloud resources or inefficient model serving. Clients typically save 40-60% on infrastructure spending compared to building solutions without specialized guidance and optimization experience.
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Lower Project Risk
Sixty percent of ML projects fail due to poor problem selection or technical complexity. Our consultants validate use cases before development begins, ensuring models solve actual business problems with available data. This drastically reduces wasted investment on unviable initiatives.
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Instant Expert Access
Building an internal ML team costs $500K-$1M annually in salaries alone, plus months recruiting scarce talent. Machine learning consulting provides immediate access to senior engineers, data scientists, and MLOps specialists for specific projects, eliminating recruitment costs and training time completely.
Most consulting firms sell generic frameworks that need customization. We've built production systems across industries. Your challenges aren't new to us as we've already solved them multiple times.
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90%+ Senior Engineers in Montreal
Over 90% of our developers are senior engineers with various areas of expertise, not junior developers outsourced offshore. You work directly with Montreal-based engineers who've solved production problems across multiple industries. No communication gaps or time zone delays slow down critical decisions.
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Full-Stack Web2 and Web3 Expertise
We maintain internal specialists across every layer of the stack, from Python and Django backend systems to React frontends, blockchain protocols, and cloud infrastructure. Our powerhouse team brings unmatched expertise across a wide range of tech stacks. Your ML models integrate seamlessly whether you're building traditional web applications or decentralized systems.
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Product-Centric Development Approach
We provide comprehensive DevOps services that oversee the entire project lifecycle, from conception through deployment and maintenance. We don't hand you documentation and disappear. We build production systems, manage deployment pipelines, and maintain models post-launch. Your ML solution stays operational while competitors struggle with abandoned pilots.
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Production-Scale Financial Systems Experience
We've implemented secure and efficient monetary systems on blockchain, revolutionizing financial transactions. Our team has built systems processing real transactions under regulatory scrutiny. You're working with engineers who understand compliance requirements, security protocols, and scalability constraints that kill most ML projects in regulated industries.
Different projects require different types of support, and your machine learning initiative should not be forced into a single model. Webisoft offers flexible engagement choices so every organization can develop at a pace and structure that suits their goals.
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Dedicated ML Development Team
Webisoft provides a focused team that works exclusively on your machine learning project. This model supports long-term builds and gives you steady progress with consistent engineering attention across data pipelines, model development, and production deployment.
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Team Extension Support
If you already have internal data scientists, Webisoft can complement your team with specialized machine learning expertise. This helps fill technical gaps in MLOps, model optimization, or infrastructure and keeps your project moving without delays or knowledge bottlenecks.
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Fixed Scope Delivery
For well-defined machine learning projects, we offer a structured fixed scope model. You receive clear timelines, predictable output and controlled delivery across all phases from data preparation through model deployment and monitoring setup.
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Hybrid Engagement Packages
For complex requirements, we combine multiple engagement styles. This gives you a balanced structure that adapts as your machine learning project grows, scales to production, and evolves with changing business needs and data patterns.
Beginning your machine learning project with Webisoft is simple and clear. We use a straightforward approach that helps your team understand every step while keeping all technical work organized and predictable.
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Share Your Requirements
You begin by sharing your project goals, available data sources, and expected business outcomes. Webisoft reviews your details and prepares a structured plan covering timelines, milestones, team roles, technical architecture, and delivery expectations with realistic accuracy targets.
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Meet Your ML Team
Once the plan is approved, you meet the data scientists, ML engineers, and DevOps specialists who will work on your project. This introduction ensures full clarity on expertise, communication channels, and workflow processes before technical work begins.
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Deploy with Ongoing Support
After rigorous testing and validation, Webisoft deploys your machine learning product to production environments and continues providing model monitoring, retraining automation, performance optimization, and guidance to keep your system accurate and stable post-launch.
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Start Discovery and Development
Your project moves through organized phases where data pipelines, model architectures, and infrastructure components are shaped, then developed with consistent reviews, performance testing, and steady communication. Each milestone delivers tangible progress toward production deployment.
Timeline varies based on project complexity, but most deployments take 8-16 weeks from initial consultation to production launch. Simple classification models with clean data can go live in 6-8 weeks, while complex systems requiring custom infrastructure may need 4-6 months.
Consulting provides immediate access to senior-level expertise across multiple specializations, like data engineering, model development, MLOps, and production deployment. A single data scientist typically excels in one area but lacks the cross-functional team needed for production systems.
Absolutely. We specialize in integrating ML solutions with established infrastructure including older databases, ERP systems, and on-premise applications. Our engineers build API layers and data pipelines that connect models to existing systems without requiring complete overhauls.
We implement automated monitoring systems that track prediction accuracy, data distribution changes, and performance metrics continuously. When drift exceeds defined thresholds, our systems trigger retraining workflows using fresh data automatically
Digital architectures for an ever-shifting world.