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MicroAlgo Inc. Develops Classifier Auto-Optimization Technology Based on Variational Quantum Algorithms, Accelerating the Advancement of Quantum Machine Learning

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SHENZHEN, China, May 2, 2025 /PRNewswire/ — MicroAlgo Inc. (the “Company” or “MicroAlgo”) (NASDAQ: MLGO) announced today the launch of their latest classifier auto-optimization technology based on Variational Quantum Algorithms (VQA). This technology significantly reduces the complexity of parameter updates during training through deep optimization of the core circuit, markedly improving computational efficiency. Compared to other quantum classifiers, this optimized model has lower complexity and incorporates advanced regularization techniques, effectively preventing model overfitting and enhancing the classifier’s generalization capability. The introduction of this technology marks a significant step forward in the practical application of quantum machine learning.

Traditional quantum classifiers can theoretically leverage the advantages of quantum computing to accelerate machine learning tasks, but they still face numerous challenges in practical applications. Firstly, current mainstream quantum classifiers often require deep quantum circuits to achieve efficient feature mapping, which results in high optimization complexity for quantum parameters during training. Additionally, as the volume of training data increases, the computational load for parameter updates grows rapidly, leading to prolonged training times and impacting the model’s practicality.

MicroAlgo’s classifier auto-optimization technology significantly reduces computational complexity through deep optimization of the core circuit. This approach improves upon two key aspects: circuit design and optimization algorithms. In terms of circuit design, the technology adopts a streamlined quantum circuit structure, reducing the number of quantum gates and thereby lowering the consumption of computational resources. On the optimization algorithm front, this classifier auto-optimization model employs an innovative parameter update strategy, making parameter adjustments more efficient and substantially accelerating training speed.

In the training process of classifiers based on variational quantum algorithms (VQA), parameter optimization is one of the most critical steps. Generally, VQA classifiers rely on Parameterized Quantum Circuits (PQC), where updating each parameter requires computing gradients to adjust the circuit structure and minimize the loss function. However, the deeper the quantum circuit, the more complex the parameter space becomes, requiring optimization algorithms to perform more iterations to achieve convergence. Furthermore, uncertainties and noise in quantum measurements can also affect the training process, making it difficult for the model to optimize stably.

Traditional optimization methods often employ strategies such as Stochastic Gradient Descent (SGD) or Variational Quantum Natural Gradient (VQNG) to find optimal parameters. However, these methods still face challenges such as high computational complexity, slow convergence rates, and a tendency to get trapped in local optima. Therefore, reducing the computational burden of parameter updates and improving training stability have become key factors in enhancing the performance of VQA classifiers.

MicroAlgo’s classifier auto-optimization technology, based on variational quantum algorithms, significantly reduces the computational complexity of parameter updates through deep optimization of the core circuit. It also incorporates innovative regularization techniques to enhance the stability and generalization capability of the training process. The core breakthroughs of this technology include the following aspects:

Depth Optimization of Quantum Circuits to Reduce Computational Complexity: In traditional VQA classifier designs, the number of layers in the quantum circuit directly impacts computational complexity. To lower computational costs, MicroAlgo employs an Adaptive Circuit Pruning (ACP) method during optimization. This approach dynamically adjusts the circuit structure, eliminating redundant parameters while preserving the classifier’s expressive power. As a result, the number of parameters required during training is significantly reduced, leading to a substantial decrease in computational complexity.

Hamiltonian Transformation Optimization (HTO): Additionally, MicroAlgo introduces an optimization method based on Hamiltonian transformations. By altering the Hamiltonian representation of the variational quantum circuit, this technique shortens the search path within the parameter space, thereby improving optimization efficiency. Experimental results demonstrate that this method can reduce computational complexity by at least an order of magnitude while maintaining classification accuracy.

Novel Regularization Strategy to Enhance Training Stability and Generalization Capability: In classical machine learning, regularization methods are widely used to prevent model overfitting. In the realm of quantum machine learning, MicroAlgo introduces a novel quantum regularization strategy called Quantum Entanglement Regularization (QER). This method dynamically adjusts the strength of quantum entanglement during training, preventing the model from overfitting the training data and thereby improving the classifier’s generalization ability on unseen data.

Additionally, an optimization strategy based on the Energy Landscape is incorporated, which adjusts the shape of the loss function during training. This enables the optimization algorithm to more quickly identify the global optimum, reducing the impact of local optima.

Enhanced Noise Robustness for Real Quantum Computing Environments: Given that current Noisy Intermediate-Scale Quantum (NISQ) devices still exhibit significant noise levels, a model’s noise resilience is critical. To improve the classifier’s robustness, MicroAlgo proposes a technique based on Variational Quantum Error Correction (VQEC). This method actively learns noise patterns during training and adjusts circuit parameters to mitigate noise effects. This strategy markedly enhances the classifier’s stability in noisy environments, making its performance on real quantum devices more reliable.

MicroAlgo’s classifier auto-optimization technology, based on variational quantum algorithms, successfully reduces the computational complexity of parameter updates through deep optimization of the core circuit and the introduction of novel regularization methods. This approach significantly boosts training speed and generalization capability. This breakthrough technology not only demonstrates its effectiveness in theory but also exhibits superior performance in simulation experiments, laying a crucial foundation for the advancement of quantum machine learning.

As quantum computing hardware continues to advance, this technology will further expand its application domains in the future, accelerating the practical implementation of quantum intelligent computing and propelling quantum computing into a new stage of real-world utility. In an era where quantum computing and artificial intelligence converge, this innovation will undoubtedly serve as a significant milestone in advancing the frontiers of technology.

About MicroAlgo Inc.

MicroAlgo Inc. (the “MicroAlgo”), a Cayman Islands exempted company, is dedicated to the development and application of bespoke central processing algorithms. MicroAlgo provides comprehensive solutions to customers by integrating central processing algorithms with software or hardware, or both, thereby helping them to increase the number of customers, improve end-user satisfaction, achieve direct cost savings, reduce power consumption, and achieve technical goals. The range of MicroAlgo’s services includes algorithm optimization, accelerating computing power without the need for hardware upgrades, lightweight data processing, and data intelligence services. MicroAlgo’s ability to efficiently deliver software and hardware optimization to customers through bespoke central processing algorithms serves as a driving force for MicroAlgo’s long-term development.

Forward-Looking Statements

This press release contains statements that may constitute “forward-looking statements.” Forward-looking statements are subject to numerous conditions, many of which are beyond the control of MicroAlgo, including those set forth in the Risk Factors section of MicroAlgo’s periodic reports on Forms 10-K and 8-K filed with the SEC. Copies are available on the SEC’s website, www.sec.gov. Words such as “expect,” “estimate,” “project,” “budget,” “forecast,” “anticipate,” “intend,” “plan,” “may,” “will,” “could,” “should,” “believes,” “predicts,” “potential,” “continue,” and similar expressions are intended to identify such forward-looking statements. These forward-looking statements include, without limitation, MicroAlgo’s expectations with respect to future performance and anticipated financial impacts of the business transaction.

MicroAlgo undertakes no obligation to update these statements for revisions or changes after the date of this release, except as may be required by law.

 

 

 

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Anrok launches MCP server, putting your tax data inside the AI tools your team already uses

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October 21, 2026

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Ask in plain language, get an answer that spans your billing system and Anrok, and act on it without opening a dashboard.

SAN FRANCISCO, Oct. 21, 2026 /PRNewswire/ — Anrok, the AI-native tax infrastructure for global commerce, today launched the Anrok MCP. It is a direct connection between your Anrok account and the AI assistants your team already works in, including Claude, ChatGPT, Cursor, and VS Code. Your team can read transactions, filings, exposure, customers, exemption certificates, and products, then act on what it finds without leaving the conversation.

Finance teams adopted AI assistants faster than their finance systems. A controller can ask an assistant to draft a memo or model a scenario. But to answer, “did we collect the right tax in July?” they close the assistant, open a dashboard, run an export, and build a spreadsheet. Anrok believes your tax data shouldn’t stay behind a login.

Anrok MCP puts tax data in the same thread as everything else your assistant can reach, including your billing system, your inbox, and your spreadsheets.

“Finance teams are increasingly doing their work directly with AI agents and their tax data should be in there with them,” said Brad Silicani, CEO at Anrok. “Ask it to reconcile your billing system and Anrok, and you get the reconciliation in minutes for your close. The AI does the thinking, and Anrok MCP exposes the tools. It’s the same platform either way, and it’s accurate underneath.”

What your team can do

Anrok MCP includes 30+ tools across transactions, filings, exposure, customers, exemption certificates, and products. Your team can:

Reconcile Anrok against your billing system and find the gap before you remit.Pull a revenue or transaction answer without building a report.Review a filing before Anrok submits it and avoid the amended return.Debug a single transaction without opening a support ticket.Get a recommended tax category for a new SKU, then map it so it never falls through again.Catch exemption certificates before they lapse.

The MCP also writes. It can create a product, create a customer, create or update a transaction, and upload a certificate. Every write shows what moved, so you can check it.

One platform behind one connection

Anrok MCP is part of Headless Tax, the way into Anrok’s platform through the Anrok APIs and MCP. Behind it, one platform runs all five tax jobs: monitor and manage, register, calculate, file and remit, and reconcile and audit. That includes reconciliation, which no other tax platform has built in. One source of truth means one connection reaches your transactions, filings, exposure, customers, and certificates. The same intelligence powers Anrok across the dashboard, the API, and your agents.

“Anrok’s MCP cut our monthly Stripe-to-Anrok reconciliation from 4 hours to 30 minutes.” —Christie Perez, Indirect Tax Manager at Webflow

Availability

Anrok MCP is available now to Anrok customers. Connect from your AI tool’s connector settings, approve the scopes, and start asking. Documentation is at https://apidocs.anrok.com/mcp/.

About Anrok

Anrok is the AI-native tax infrastructure for global commerce. More than 5,500 users run sales tax and VAT/GST compliance on Anrok across 150+ countries, with more than $100B in processed volume. One platform runs all five compliance jobs, monitoring, registration, calculation, filing and remittance, and reconciliation, which no other tax platform has built in. Teams reach it however they work, in the product with Anrok Atlas, through the Anrok APIs, or by handing the job to an agent. The result is every tax dollar owed, collected, so tax becomes a pass-through instead of an expense.

Media contact

Emilie Murphy
emilie.murphy@anrok.com

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Alternative Data Market to Reach USD 40.35 Billion by 2035 as AI Developers and Corporations Expand Data Buying, Credence Research Finds

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October 21, 2026

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LONDON, Oct. 21, 2026 /PRNewswire/ — Credence Research Inc. has added a new global Alternative Data Market study to its market intelligence portfolio, examining the competitive, technological and regulatory factors shaping demand through 2035.

The global alternative data market was valued at USD 6.32 billion in 2025 and is projected to increase to USD 7.56 billion in 2026 before reaching USD 40.35 billion by 2035, according to Credence Research. The forecast represents a 20.37% compound annual growth rate from 2025 to 2035. All market sizes, segment shares and regional shares are Credence Research estimates.

Key Takeaways

Credence Research estimates 2025 revenue of USD 6.32 billion and a 2035 market value of USD 40.35 billion, equivalent to 20.37% annualized growth over 10 years.North America represented 57.3% of modeled global value in 2025.Card and receipt transactions led the data type category with a 22.4% share.Hedge funds accounted for 24.6% of the end user category.Web-scraped data is forecast to grow at 24.00% annually, while Technology and AI developers is projected at 28.50%.The analysis distinguishes market revenue from adjacent products and services to avoid treating unrelated spending as part of the addressable market.

Scope & Segmentation – Alternative Data Market

The study measures licensed nontraditional datasets used for investment research, commercial intelligence and AI applications, including card and receipt panels, web traffic, app use, web-scraped information, geolocation, satellite data and sentiment feeds. Conventional market prices, ratings, cloud storage and consulting fees are excluded. Revenue is allocated to the buyer geography and is expressed in current U.S. dollars. The report evaluates the commercial drivers, market restraints, supplier positions and technology transitions affecting this defined market.

By data type: Card and receipt transactions; Web traffic and app usage; Web-scraped data; Geolocation and foot traffic; Satellite and weather data; Social and sentiment data; Other ESG, employment and B2B data.By end user: Hedge funds; Asset managers and investment institutions; Retail and consumer companies; Technology and AI developers; Other corporates and public sector.By region: North America; Europe; Asia Pacific; Latin America; Middle East and Africa.

Explore the full report: https://www.credenceresearch.com/report/alternative-data-market 

Why This Report Matters

Defines the revenue boundary around purchased information rather than bundling unrelated analytics software and financial reference data.Compares investment-firm demand with newer corporate and AI developer use cases, including the privacy and licensing requirements that shape adoption.Provides a framework for evaluating vendor quality, consent, geographic coverage and data reliability before buyers commit to long-term contracts.

Market Overview

Alternative data vendors increasingly sell the same underlying information to different buyer groups. Quantitative funds use transaction, web and location panels to estimate business performance before quarterly filings. Retailers apply those signals to site selection and competitor analysis, while AI developers license web and app information for model training and retrieval workflows.

The forecast reflects a modeled change in buyer adoption, supplier economics and product mix rather than a guarantee of future transactions. Credence Research evaluates these mechanisms alongside recent regulatory and corporate developments. Decision-makers should distinguish disclosed company announcements from estimates of total market revenue.

Market Insights

Base year: 2025; forecast horizon: 2035; modeled CAGR: 20.37%.Largest modeled geography: North America (57.3% of 2025 value).Leading categories: Card and receipt transactions (22.4%) and Hedge funds (24.6%).Fast-growth categories: Web-scraped data (24.00% CAGR) and Technology and AI developers (28.50% CAGR).Geographic and segment shares are proprietary Credence Research model outputs, not audited vendor market-share disclosures.

Key Attributes

Attribute

Details

Historical period

2018–2025, as listed in the report

Base year

2025

Forecast period

2025–2035

Market size, 2025

USD 6.32 billion

Estimated market size, 2026

USD 7.56 billion

Market size forecast, 2035

USD 40.35 billion

Revenue CAGR, 2025–2035

20.37 %

Coverage

Global

Segments covered

Data type; End user

Geographies

North America; Europe; Asia Pacific; Latin America; Middle East and Africa

Market Segmentation Analysis

By data type

Card and receipt transaction panels led data types with 22.4% of 2025 spending because they can help estimate consumer revenue before corporate reporting. Web-scraped information is the fastest-growing data type in the model, supported by price monitoring, job-posting analysis and licensed AI data feeds.

By end user

Hedge funds accounted for 24.6% of 2025 end-user spending. Technology and AI developers have the faster modeled growth rate as demand expands from trading signals into model training, retrieval systems and agent-ready data services.

Card and receipt transactions held 22.4% of the data type segment in 2025, while Hedge funds led the end user split at 24.6%. Credence Research forecasts faster growth for Web-scraped data (24.00% a year) and Technology and AI developers (28.50% a year). These figures represent market estimates and are not company-reported results.

Market Drivers and Opportunities

Faster commercial signals

Card-spending, store-visit and app-usage panels can reveal operating trends before financial results are released. Funds buy such feeds to build forecasts, while retailers use them to compare competitors and local markets. Data coverage, representativeness and historical consistency determine whether these signals justify recurring subscription fees.

AI training and agent-ready feeds

Model developers are seeking licensed and documented sources of web, app and commerce data. Vendors that deliver clean feeds through APIs and data marketplaces can reach customers outside the hedge fund sector. Contracts for model training also require clear rights to collect, transform and reuse source information.

Distribution through existing research platforms

Financial-information terminals and cloud marketplaces reduce the engineering work required to test a new dataset. Bloomberg’s 2025 integration of Similarweb traffic data is one example of this route to market. Distribution can broaden adoption, although vendors may surrender part of the economics to aggregators.

Regional Analysis

Credence Research estimates the following shares of global 2025 revenue: North America 57.3%; Europe 22.1%; Asia Pacific 14.6%; Latin America 3.1%; Middle East and Africa 2.9%. These allocations sum to 100% and reflect buyer location rather than the location of manufacturing or company headquarters.

North America led the market in 2025, supported by its established buyer base, supplier relationships and industry infrastructure. Europe and Asia Pacific are also material demand centers, although the competitive mix and procurement requirements vary by country. The smaller regions may record high percentage growth from a limited starting base, and such rates should not be mistaken for leadership in absolute spending.

North America benefits from a dense concentration of quantitative funds, financial data platforms and AI developers. European buyers must navigate stringent personal-data rules, which increases demand for licensed and aggregated feeds. Asia Pacific adds demand from financial centers and domestic digital platforms but faces varying localization requirements.

Market Challenges

Sensitive geolocation and transaction data create privacy, consent and national-security concerns. U.S. enforcement actions and cross-border data restrictions make diligence a core purchase requirement. Panel drift, incomplete histories and nonrepresentative samples can undermine the value of apparently timely signals; a faster feed is not necessarily a better forecast.

Buyers should also evaluate the total cost of adoption, interoperability with existing systems, vendor continuity and country-specific compliance obligations. Those factors can lengthen purchasing decisions even where the long-term growth thesis remains intact. The study distinguishes these commercial constraints from the market forecast itself.

Future Outlook

North America holds the largest modeled share because it hosts many hedge funds, data vendors and AI companies. Asia Pacific has the fastest forecast regional growth as investment institutions and local digital platforms expand. The strongest opportunities lie in licensed, traceable and AI-ready datasets that meet regulatory and quality standards.

Through 2035, suppliers that convert product capabilities into repeatable deployments, transparent pricing and reliable support should be better positioned than firms relying only on early demonstrations. Credence Research expects growth to vary across customer types and geographies rather than advance evenly each year.

Competitive Landscape

The market includes established multinational suppliers and specialized technology or service providers. Competition centers on product performance, customer relationships, integration capabilities, delivery reliability and the ability to meet sector-specific standards. The companies listed below participate in different parts of the value chain; their inclusion does not imply identical offerings or a verified ranking by market share.

Competitive analysis should distinguish vendors that directly sell the products or services in the defined scope from adjacent technology providers and end users. Credence Research uses supplier disclosures and industry evidence to assess participation. Comparable revenue for the narrow market definition is rarely disclosed, so this release does not assign unsupported individual company shares.

Key Players

BloombergS&P Global Market IntelligenceLSEGFactSetMoody’sSimilarwebPlacer.aiPlanet LabsYipitDataConsumer EdgeM ScienceThinknum

Recent Developments

June 2025: Bloomberg made Similarweb web-traffic analytics available through the Bloomberg Terminal ALTD function alongside other alternative datasets. 

January 2025: The U.S. Federal Trade Commission finalized its order restricting Gravy Analytics and Venntel from selling sensitive location data. 

Report Coverage

The full study covers market size and forecast, competitive conditions, value-chain structure, product and application segmentation, regional demand, market drivers, restraints, opportunities and regulatory developments. It provides a 2025 baseline and 2035 outlook and identifies areas where supplier disclosures support, but do not independently establish, Credence Research market estimates.

Explore the full report: https://www.credenceresearch.com/report/alternative-data-market  

Table of Content –

Chapter 1 Market Segmentation

Chapter 2 Introduction

Chapter 3 Executive Summary

Chapter 4 Global Alternative Data Market Trends and Developments

Chapter 5 Global Alternative Data Market Production, Supply and Demand Analysis

Chapter 6 Global Alternative Data Market Pricing Analysis

Chapter 7 Global Alternative Data Market Dynamics

Chapter 8 Global Alternative Data Value Chain Analysis

Chapter 9 Global Alternative Data Market Regulations and Policies

Chapter 10 Global Alternative Data Market Hotspots and Opportunities

Chapter 11 Global Alternative Data Market Outlook, 2025-2035

Chapter 12 Global Alternative Data Market Segmentation and Outlook

Chapter 13 Global Alternative Data End-Use and Application Market Outlook

Chapter 14 Global Alternative Data Geographic Market Outlook, 2025-2035

Chapter 15 Global Alternative Data Technology, Innovation and Sustainability Outlook

Chapter 16 Global Alternative Data Market Key Strategic Imperatives

Chapter 17 Competition Outlook

Chapter 18 Company Profiles

Chapter 19 Disclaimer

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About Us

Credence Research Inc. is a global market intelligence and consulting firm founded in 2015. It delivers market insights, quantitative analysis and strategic guidance to business leaders, investors, governments and nonprofit organizations. The company helps clients evaluate market opportunities, understand competitive dynamics and make informed strategic decisions.

Credence Research produces industry studies covering market size, forecasts, growth drivers, market trends and competitive landscapes. Its services also include customized research, commercial due diligence and go-to-market advisory. Findings draw on primary and secondary research, expert input and analytical modeling.

Contact Us

Credence Research Inc.
Tower C-1105, S 25, Akash Tower
Vishal Nagar, Pimple Nilakh, Haveli
Pune – 411027, India
USA: +1-888-600-6441
Email: sales@credenceresearch.com 
Website: www.credenceresearch.com 

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TECHNATION Appoints New Board Leadership and Welcomes Industry Leaders to National Board

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October 21, 2026

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TORONTO, Oct. 21, 2026 /CNW/ — TECHNATION, Canada’s national technology industry association, is pleased to announce the appointment of Sylvie Ouellette as Chair and Colin McIsaac as Vice Chair of its National Board of Directors.

Sylvie Ouellette is President and Co-Founder of Versatil BPI Inc., a Canadian-owned, women-led technology SME based in the National Capital Region, specializing in data, artificial intelligence, and security intelligence. She has more than 30 years of industry experience, including senior executive roles at Electronic Data Systems and Hewlett-Packard. As Chair, she brings both multinational leadership, including representing TECHNATION at the last two G7/TECH7 summits, and firsthand experience building a Canadian technology company.

Colin McIsaac is the Vice-President, North America Commercial Sales at Lenovo, responsible for strategy and performance across the NA VLB Segment and Canada’s Public Sector business. A prominent voice on the North America Senior Leadership Team, he oversees sales, operations, culture, and P&L execution to ensure Lenovo consistently delivers industry‑leading value for customers. Under his leadership, the team brings Lenovo’s complete portfolio of hardware, software, and services to market, fueling growth through innovation, customer partnership, and operational excellence.

Sylvie and Colin have been active members of TECHNATION’s Board of Directors for several years, serving on the Executive Committee and Governance Committee, respectively.

“We are pleased to welcome Sylvie as Chair and Colin as Vice Chair to TECHNATION’s National Board of Directors,” said Kevin d’Entremont, President and CEO of TECHNATION. “As seasoned members of our National Board, they have been engaged in shaping the focus of TECHNATION in supporting the full technology ecosystem in Canada. Their combined expertise, leadership, and commitment to advancing Canada’s technology ecosystem will be invaluable as we continue to advance the priorities that matter most to the sector and to Canada’s digital future.”

In addition to these appointments, TECHNATION is pleased to welcome four industry leaders to its Board of Directors:

Heather Black, Regional Vice-President, Canadian Public Sector Leader, Palo AltoPeter Kelcey, General Manager, Customer Success Lead, Microsoft CanadaErin O’Halloran, Vice President & General Manager, Oracle Health CanadaRaj Thuppal, Chief Digital Officer – Public Sector, ServiceNow Canada

These appointments come at a pivotal moment for Canada, as governments and businesses accelerate the adoption of digital technologies that are transforming how the economy operates and grows.

“I am honoured by the trust my fellow Board members have placed in me to lead TECHNATION at such a defining moment for Canada,” said Ouellette. “As technology reshapes our economy, national security, and global competitiveness, we must strengthen Canada’s capacity to innovate, advance responsible AI, and ensure Canadian businesses across the full ecosystem help shape our digital future. I look forward to working with our members and government partners to turn collaboration into action that delivers meaningful value for Canadians.”

Together, these appointments strengthen TECHNATION’s commitment to bring industry expertise to inform public policy, champion member priorities, and advance initiatives that support Canada’s digital and economic growth. Their leadership will help drive key priorities including digital transformation, procurement reform, workforce development, and Canada’s long-term competitiveness.

TECHNATION also extends its sincere thanks to outgoing Board Chair, Lloyd Switzer, retired Chief Procurement Officer at TELUS, for his leadership and dedication to the board. “Lloyd has been an invaluable leader at TECHNATION, guiding the association through a period of executive transition, while bringing a strong industry perspective through his role as Chief Procurement Officer at TELUS,” said d’Entremont. “While his retirement marks the end of an exceptional chapter, he has ensured a smooth transition and a bright future for our association and Canada’s tech sector.”

TECHNATION’s National Board is comprised of senior executives from across Canada’s technology landscape, representing both Canadian tech firms and global enterprises with significant operations and investments in Canada. A full list of TECHNATION’s National Board of Directors can be found at www.technationcanada.ca.

About TECHNATION

TECHNATION is Canada’s authoritative technology industry association, representing the full spectrum of Canada’s tech ecosystem, from agile innovators to domestic and global tech companies. Trusted by both industry and government, TECHNATION is the neutral connector, convener and catalyst powering a digital future and positioning Canada as a global tech leader. Together with our members, we shape tech policy and insights, deliver forums to drive public-private collaboration, foster inclusive workforce development, and support global expansion. Learn more at www.technationcanada.ca.

SOURCE TECHNATION Canada

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