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DATA ANALYTICS SERVICES

Understand Performance. Spot Patterns. Plan With Better Data.

Tech.us helps organizations turn raw and disconnected data into clear analytical insights by bringing information together and identifying the patterns that matter.
Use those insights to understand performance, identify risks and opportunities, and support more informed planning and business decisions.

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TRUSTED BY INDUSTRY LEADERS

DATA OUTCOMES

Get Clearer Answers From the Data Across Your Business

Useful analytics starts with bringing the right information together and then applying the type of analysis that fits the question.

Bring Disconnected Data Together

Combine information from different operational systems so analysis is not limited to one application or isolated dataset.

Make Reporting Easier to Interpret

Turn complex datasets into reports, graphs, and visual outputs that make performance easier to understand.

Understand Why Results Changed

Look beyond the numbers to identify patterns, relationships, and possible causes behind past and current outcomes.

Add Evidence to Planning

Use trends and correlations within historical data to support decisions around future activities and business priorities.

Understand Customer Behavior

Analyze digital customer interactions to identify behavioral patterns across websites and mobile applications.

Improve Visibility Into Risk

Use analytics to identify anomalies and potential risk signals across operational, financial, network, and security data.

OUR SERVICES

Data Analytics for Different Business Questions and Data Types

Tech.us works across the analytics process, from collecting data across systems to explaining past results, examining causes, identifying patterns, and presenting information in a form businesses can use.

Data Collection

Bring data together from sources such as ERPs, CRMs, IoT devices, and servers and consolidate it into a more unified environment for analysis.

Predictive Analytics

Analyze historical trends, correlations, and other patterns to estimate potential future conditions and support planning around activities such as campaigns, launches, and operations.

Prescriptive Analysis

Use analytical results to evaluate possible courses of action and provide additional context for deciding what steps to consider next.

Diagnostic Analysis

Apply data mining, discovery, and drill-down techniques to examine historical information and identify patterns or relationships that can help explain why particular outcomes occurred.

Descriptive Analysis

Summarize historical and current information through reports, graphs, visualizations, and ad hoc analysis so stakeholders can understand what the data is showing.

Big Data Analytics

Analyze larger volumes of structured and unstructured data and organize complex datasets so useful patterns and information can be extracted.

Customer Analysis

Analyze customer behavior across websites and mobile applications to better understand how people interact with digital products and services.

Security & Risk Analysis

Analyze data from operational systems, networks, financial platforms, and cybersecurity environments to identify anomalies and potential areas of risk.

Selected Work

Work That Solves Real Business Challenges

Explore selected projects where Tech.us has turned complex requirements into practical technology designed around business needs.

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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.

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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.

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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.

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Tony Robbins Wealth Mastery app shown on tablet and phone

WHY TECH.US

Support Across More of the Analytics Process

Tech.us works across data collection, analysis, visualization, security, and presentation, allowing the analytics work to account for both where the data comes from and how the resulting information needs to be used.

Coverage Across Analytics Methods

Work with descriptive, diagnostic, predictive, and prescriptive approaches instead of limiting analysis to a single type of reporting.

Data From Business Systems

Bring together information from sources such as ERP platforms, CRM systems, IoT devices, servers, websites, and applications.

Structured and Unstructured Data

Analyze conventional structured datasets as well as larger and less structured sources when the business problem requires both.

Outputs Built to Be Understood

Present complex information through reports, graphs, visualizations, and other formats that make findings easier for stakeholders to interpret.

Controls for Sensitive Data

Apply measures such as encryption, multi-factor authentication, and role-based access when analytics involves sensitive information.

Analysis Tied to the Business Question

Define the business objective first, then plan the data, analysis, and outputs around the question the organization is trying to answer.

why-choose-techus

HOW WE WORK

Define the Question Before Starting the Analysis

The analytics approach depends on what the business needs to understand, which data is available, and how the findings will ultimately be used.

1

Understand

Clarify the business objective, the questions that need to be answered, and the data available to support the analysis. 

2

Cost Estimate

Define the expected investment based on the scope of data, analytical work, and resources required. 

3

Plan

Create a roadmap covering the relevant data sources, analytics activities, and expected outputs before the work begins. 

4

Deliver

Carry out the analysis and present the resulting information in the agreed reporting or visualization format. 

Get Clearer Insights From the Data You Already Have

Use analytics to understand performance, identify what is changing, and support the decisions that come next.

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DATA CHALLENGES

Address the Problems That Can Weaken the Analysis

Analytics can produce poor answers when the underlying data is incomplete, disconnected, inaccurate, or inadequately protected.

Low Data Quality & Accuracy

Validate and enrich data before analysis and use data-governance practices and anomaly detection to identify quality issues that could affect the result.

Data Silos & Integration

Connect data from different internal and external sources using approaches such as unified data platforms, data lakes, cloud architectures, and ETL processes.

Data Privacy

Protect sensitive information with measures such as role-based access, multi-factor authentication, and other appropriate security controls throughout the analytics process.

Limited Analytics Expertise

Support organizations that need additional capability to organize and analyze complex structured or unstructured datasets and interpret the resulting information.

DATA PROCESS

Move From Disconnected Sources to Usable Analysis

The quality of the final insight depends on what happens before the report or visualization is produced.

Consolidation

Bring organizational data from different sources into a more centralized environment so analysis is not restricted by isolated datasets.

Collaboration

Reduce data silos and make relevant information available across the people who need to work with it while maintaining controls around sensitive data.

Visualization

Analyze patterns and relationships within the data and represent important findings visually where that makes them easier to interpret.

Presentation

Present analytical findings in reports and other understandable formats so stakeholders can use the information in business discussions and decisions.

TECH STACK

Technology Across the Data Analytics Environment

Data analytics can involve data sources, analytical models, databases, applications, visualization layers, and cloud infrastructure.
Tech.us works across AI/ML, application, database, and cloud technologies so the stack can be selected around the data being analyzed and how the results need to be delivered.

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 Technology Excellence

Explore the awards and industry recognition associated with Tech.us.

Mobile App Daily — Top Web Development Company
DesignRush — Top AI Development Company
Mobile App Daily — Top AI Company
TechReviewer — Top Mobile App Developers
TechReviewer Award

FAQ

Data Analytics Questions

Tech.us provides data collection, predictive analytics, prescriptive analysis, diagnostic analysis, descriptive analysis, big data analytics, customer analysis, and security and risk analysis. 

Descriptive analytics explains what happened. Diagnostic analytics examines why it happened. Predictive analytics estimates what may happen based on historical patterns. Prescriptive analytics helps evaluate what actions could be considered in response. 

The source specifically identifies data from ERP systems, CRM platforms, IoT devices, servers, websites, mobile applications, networks, financial platforms, and other organizational systems. 

Analytics turns raw information into findings that can help organizations understand trends, customer behavior, operational performance, risks, and other factors relevant to a business decision. 

Big data analytics focuses on large, high-volume, or high-velocity datasets. It can involve both structured and unstructured information and requires methods for organizing, segmenting, and analyzing data at greater scale. 

Yes. The source specifically states that Tech.us works with structured and unstructured data as part of its big-data analytics services. 

Tech.us consolidates information from different sources and can use unified data platforms, data lakes, cloud architectures, and ETL processes to bring that data together for analysis. 

Tech.us uses controls that include data encryption, multi-factor authentication, and role-based authorization to protect sensitive information used during analytics. 

Customer analytics examines behavioral information from customer interactions, including activity across websites and mobile applications, to identify patterns in how customers engage with digital products or services. 

Analytics can help identify anomalies and patterns that may indicate areas requiring further investigation. The source includes operational systems, networks, financial platforms, and cybersecurity environments within its security and risk analysis capability. 

The source describes validating and enriching data, applying data-governance practices, and using analytical or machine-learning methods to identify anomalies that could affect data quality. 

Analytics initiatives can bring information from separate systems into a centralized or unified environment, making it easier to analyze information across sources rather than treating each dataset independently.

Put Your Business Data to More Practical Use

Build analysis around the questions your organization actually needs to answer.

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