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MACHINE LEARNING DEVELOPMENT & MLOPS

Turn Your Data Into Decisions You Can Act On

Tech.us designs, trains, integrates, and operates machine learning systems for forecasting, detection, classification, recommendation, computer vision, and language-based workflows.

We build for live data—not only the training environment—with data pipelines, explainability, security, deployment, monitoring, and retraining designed into the system.

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Projects Delivered

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Years of Engineering

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Industries Served

Trusted by Organizations That Depend on Technology

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FROM HISTORY TO FORESIGHT

Find the Signal Your Existing Reports Miss

Most organizations collect more data than people can review manually. The patterns that can indicate demand, churn, fraud, equipment failure, customer intent, operational risk, or the next best action may already exist—but remain buried across systems and historical records.

Machine learning can identify those patterns and make them available at the point of decision. The value does not come from the algorithm alone. It comes from choosing the right decision to improve, preparing suitable data, validating performance, integrating the output into the workflow, and keeping the model effective as conditions change.

  1. Forecast What Comes Next

    Estimate demand, revenue, volume, capacity, churn, risk, or other time-dependent outcomes so teams can act earlier.

  2. Detect What Does Not Fit

    Identify anomalous transactions, equipment behavior, process conditions, or data patterns that rules and manual review may miss.

  3. Classify at Scale

    Sort documents, images, messages, cases, products, or events into useful categories without requiring people to review every item.

  4. Recommend the Next Best Option

    Use behavior, context, and historical outcomes to rank products, content, actions, or interventions for a specific user or situation.

  5. Interpret Images, Video, and Language

    Extract useful signals from drawings, photographs, video, calls, tickets, contracts, reports, and other unstructured information.

BEYOND THE NOTEBOOK

A Model Is Only Useful If It Holds Up in Production

Model performance can change when real data differs from the training set, business behavior shifts, upstream systems change, or users interact with the output in unexpected ways. We engineer the surrounding system to reveal those changes and respond deliberately.

Validated Against the Right Metric

We choose evaluation criteria that reflect the business decision—not only a generic accuracy score—and compare candidate approaches against real data.

Tested for Bias and Uneven Performance

Where the use case requires it, we evaluate performance across relevant groups, segments, and conditions to identify skew before and after deployment.

Explainable Where Decisions Require It

We balance performance with interpretability based on the consequence of the decision, regulatory expectations, and the need for users to understand the output.

Monitored for Drift and Degradation

We track data and model behavior so teams can detect when inputs, distributions, or performance move outside expected conditions.

Secured Across the ML Lifecycle

We design access, environments, data handling, model artifacts, APIs, logging, and deployment controls around the sensitivity of the use case.

Connected to Human Decisions

We define how the prediction appears in the workflow, what people should do with it, and where human review or override remains necessary.

MACHINE LEARNING SERVICES

Everything Required to Move From Data to Production Decisions

ML Strategy & Feasibility Assessment

We identify decisions worth modeling, evaluate the available data, define success metrics, and determine whether machine learning is technically and economically appropriate.

Custom Model Development

We develop and compare models using your data and the criteria that matter to the use case, selecting the simplest approach that meets the required performance.

Predictive Analytics & Forecasting

We build systems that forecast demand, risk, churn, revenue, volume, maintenance needs, and other future outcomes using historical and real-time signals.

Anomaly, Fraud & Risk Detection

We model normal and abnormal behavior to surface transactions, events, equipment conditions, or process changes that require attention.

Recommendation & Ranking Systems

We build models that rank products, content, offers, actions, or interventions using user behavior, context, constraints, and business goals.

Computer Vision

We develop systems that classify, detect, segment, measure, and extract information from images, video, drawings, scans, and other visual data.

Natural Language Processing

We structure and analyze the language found in documents, messages, calls, tickets, contracts, and reports for classification, extraction, routing, sentiment, and topic analysis.

Data Engineering & Feature Development

We build the pipelines, transformations, features, validation, and lineage required to turn raw operational data into reliable model inputs.

MLOps, Deployment & Monitoring

We deploy, version, observe, and maintain models with repeatable pipelines for testing, release, monitoring, retraining, rollback, and governance.

ML Operations & Optimization

We review performance, investigate drift, maintain integrations, retrain when justified, and improve the system as the business and data change.

A Controlled Path to Production

Prove the Decision Before Scaling the System

1. Define the Decision

We identify what the business must predict, classify, detect, or recommend—and what action will follow from the output.

2. Assess the Data

We evaluate availability, quality, history, labels, access, privacy, representativeness, and the cost of preparing the data.

3. Establish the Baseline

We measure the current process and compare model performance against simple rules, existing methods, or human decision-making where appropriate.

4. Validate on Real Data

We test candidate approaches against defined metrics, edge cases, segments, and operating constraints before committing to production architecture.

5. Integrate and Deploy

We connect the model to the applications, APIs, data pipelines, interfaces, and workflows where the output will be used.

6. Monitor and Improve

We track model and data behavior, investigate changes, and retrain or revise the system when evidence shows it is necessary.

WHY TECH.US FOR MACHINE LEARNING

The Hardest Work Usually Sits Around the Model

We Start With the Business Decision

The goal is not to build a more sophisticated model. It is to improve a specific decision or process with evidence that the model performs better than the current approach.

We Work With the Data You Actually Have

Real business data is incomplete, inconsistent, distributed, and shaped by past processes. We assess that reality early so feasibility, scope, and expectations remain credible.

We Combine ML With Software Engineering

A model needs applications, APIs, workflows, infrastructure, interfaces, permissions, and support. Tech.us can engineer the complete production system instead of handing over a model artifact.

We Require Human Review of AI-Generated Code

AI can accelerate development, but it does not own the engineering result. Human review is required before AI-generated code is accepted into a client system.

We Design for Ongoing Performance

Production monitoring, evaluation, versioning, retraining, and rollback are part of the operating model—not an afterthought added when performance declines.

We Remain Model- and Tool-Neutral

We select methods, frameworks, platforms, and cloud services based on the use case, data, performance, interpretability, security, maintainability, and deployment requirements.

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Flexible Ways to Engage

ML Discovery & Feasibility

A structured assessment to define the decision, evaluate data readiness, establish success metrics, test feasibility, and recommend the next step.

Fixed-Scope ML Implementation

A defined project covering agreed model, data, integration, deployment, and evaluation deliverables.

Dedicated ML & Data Team

A stable team of data scientists, ML engineers, data engineers, software engineers, and delivery roles working as an extension of your organization.

Ongoing MLOps & Optimization

Continued monitoring, maintenance, retraining, integration support, evaluation, and model improvement after launch.

Discuss Your ML Initiative

INDUSTRY EXPERIENCE

Models Built Around the Decisions That Matter in Your Industry

The technology must fit the environment. We account for the workflows, systems, data, regulations, and human decisions that determine whether a solution succeeds in practice.

Explore All Industries
Healthcare-2 Healthcare Support risk identification, capacity planning, operational forecasting, document classification, and other decisions where performance, privacy, and explainability matter. Explore Healthcare ind-insurance Financial Services & Insurance Assist with fraud detection, risk scoring, claims and underwriting analysis, document classification, forecasting, and case prioritization—with controls aligned to the consequence of the decision. Explore Financial Services & Insurance ind-retail-1 Retail & Supply Chain Forecast demand, optimize inventory decisions, rank products and offers, identify customer behavior patterns, and surface supply-chain exceptions. Explore Retail & Supply Chain Manufacturing-2 Manufacturing Predict maintenance needs, detect quality issues, analyze production conditions, forecast throughput, and identify operational anomalies. Explore Manufacturing Preconstruction-2 Construction Extract information from drawings and documents, forecast project cost or risk, classify project records, and identify patterns across estimates and execution data. Explore Construction ind-transportation-1 Transportation & Logistics Support route and demand forecasting, fleet analysis, asset monitoring, customer-feedback classification, risk detection, and operational planning. Explore Transportation & Logistics

Selected Work

Success Stories

Machine Learning Applied to Real Operations

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AI-Powered Takeoffs From Complex Construction Drawings

A precast concrete manufacturer partnered with Tech.us to bring AI into the estimating workflow. The system uses OCR, computer vision, and generative AI to identify, extract, and visualize structures, pipes, and components from complex construction drawings.

Read the Case Study
Wellington Hamrick Precast AI takeoff automation tool shown on laptop and tablet

Mobile Fleet Visibility Built for Field Operations

SkyHawk by TELUS needed a streamlined mobile experience for its Connect Anywhere platform. Tech.us built the core experience around real-time asset tracking, fleet activity, secure configuration, and map-based operational visibility.

Read the Case Study
SkyHawk by TELUS mobile app screens showing fleet tracking and login

Personalized Financial Technology Built to Scale

Tech.us helped bring Wealth Mastery to life as a digital platform that puts personalized financial planning tools directly in users' hands while supporting a large and growing audience.

Read the Case Study
Tony Robbins Wealth Mastery app shown on tablet and phone

MACHINE LEARNING TECHNOLOGY

The Method and Stack Follow the Problem

Our teams work across supervised and unsupervised learning, deep learning, time-series forecasting, natural language processing, computer vision, recommendation systems, data engineering, and MLOps.

We choose the approach around the data, decision, performance target, interpretability, deployment environment, operating cost, and long-term maintainability.

Agentic AI & Orchestration

Agentic AI Agentic AI
AI Agents AI Agents
Multi-Agent Systems Multi-Agent Systems
Agentic Workflow Automation Agentic Workflow Automation
Model Context Protocol (MCP) Model Context Protocol (MCP)
Agent-to-Agent Protocol (A2A) Agent-to-Agent Protocol (A2A)
Agent Memory and Reasoning Agent Memory and Reasoning

Recognition

Recognized for AI and Technology Delivery

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FREQUENTLY ASKED QUESTIONS

Questions Buyers Ask About Machine Learning

Machine learning is a branch of AI that identifies patterns in data and uses them to predict, classify, rank, or detect outcomes. Unlike traditional software, the behavior is learned from training data rather than defined entirely through fixed rules.

Use rules when the logic is stable, explicit, and easy to maintain. Consider machine learning when the decision depends on patterns across many variables, changes over time, or cannot be described reliably through hand-written logic.

It depends on the problem, the model type, the quality and representativeness of the data, and the required performance. A feasibility assessment can determine whether the available data is sufficient or whether a simpler method is more appropriate.

We begin with the decision, data, constraints, and evaluation metric. We establish a baseline, test candidate approaches, and select the method that delivers the best practical balance of performance, explainability, operating cost, and maintainability.

We monitor input data, model behavior, and business outcomes for drift or degradation. Retraining is performed when evaluation shows it is justified, using controlled data, validation, versioning, and deployment processes.

Where the use case requires it, we evaluate the training data and model performance across relevant groups, segments, and conditions. We document limitations and use data, feature, model, threshold, workflow, or human-review changes to address unacceptable disparities.

We design access, environments, data handling, model storage, APIs, logging, monitoring, and deployment controls around the sensitivity of the data and the client's requirements. Security architecture is defined before production access is granted.

Timing depends on data readiness, model complexity, integration, validation, security, and deployment requirements. A focused proof of value may take weeks; a production system can require multiple phases. We establish milestones after assessing the actual work.

Investment depends on the decision being modeled, data condition, model complexity, integration, security, deployment, monitoring, and engagement model. Tech.us defines the recommended scope, team, deliverables, phases, and commercial structure before development begins.

Yes. Tech.us can assess, integrate, monitor, maintain, retrain, modernize, or improve an existing machine learning system, including its data pipelines and MLOps environment.

Ready to Put Your Data to Work?

Bring us the decision you want to improve and the data you believe can help. We will assess whether machine learning fits, what it will require, and the most practical path toward production.

Schedule an ML Opportunity Call

Business decision · Data readiness · Feasibility · Practical next step