The market for due diligence investigations is expected to grow significantly in the coming years. In 2024, it was valued at about USD 8.5 billion, but by 2034, it was predicted to reach around USD 16.7 billion. This means the market could nearly double, growing at an average rate of 7.4% each year from 2025 to 2034.
In 2024, North America was the top region for due diligence services, making over USD 3 billion and holding more than 37% of the total market. The United States played the most significant role in this, with a market value of USD 2.9 billion and steady growth expected at a rate of 5.8% per year. This shows North America, especially the U.S., is a strong and growing hub for these services.
When evaluating a company’s technology assets, a technical due diligence report provides critical insights to determine the success or risk of an investment, merger, or acquisition. This report assesses not only the technology stack and infrastructure but also the strategic alignment of IT systems with business goals.
Understanding the Key Elements of a Technical Due Diligence Report
When acquiring or merging with a technology-driven company, a technical due diligence report is critical to uncovering risks, validating opportunities, and ensuring a smooth transition. At Zartis, our reports dive deep into nine core areas to provide actionable insights to enhance clarity and impact. Let’s explore what each entails:
1. Strategic Considerations in a Technical Due Diligence Report
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Assessing IT Costs and Budget Efficiency
A well-structured technical due diligence report includes a thorough analysis of IT expenditures, covering everything from software licenses and cloud services to infrastructure maintenance and development costs. Evaluators should look for cost transparency, budget forecasting accuracy, and alignment of IT spending with the company’s growth stage and operational scale. High or unclear costs may indicate inefficiencies, technical debt, or a lack of financial oversight.
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Evaluating Scalability for Future Growth
Scalability refers to a system’s ability to handle increased workloads without compromising performance. This part of the report assesses whether the existing infrastructure and architecture can support user growth, geographic expansion, or increased data throughput. Cloud-readiness, modular architecture, and elasticity are key indicators of future scalability. A company positioned for growth should demonstrate proactive capacity planning and flexible systems design.
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Measuring the Innovation Potential of the Tech Stack
The innovation potential addresses how adaptive and forward-thinking the current technology landscape is. This involves evaluating the use of modern technologies (e.g., AI, machine learning, automation), the agility of the development process, and the company’s commitment to continuous improvement. If the company is locked into legacy systems with limited integration capabilities, it may struggle to keep up with evolving market demands.
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Reviewing Business Continuity and Disaster Recovery Plans
A sound technical strategy includes comprehensive business continuity and disaster recovery plans. The report should examine whether there are documented procedures for handling outages, cyberattacks, or data loss events. This includes backup frequency, failover mechanisms, incident response protocols, and recovery time objectives (RTOs). The absence of robust contingency planning may pose significant operational risks.
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Understanding Third-Party Dependencies and Risks
The technical due diligence report should also highlight any critical third-party services or integrations the company relies on, such as payment gateways, cloud providers, or external APIs. Assessing vendor lock-in risks, contract flexibility, and service-level agreements (SLAs) is crucial for determining how external dependencies could impact operations, scalability, or compliance in the future.
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Evaluating IT Governance and Internal Policies
Strong IT governance ensures that technical operations align with corporate strategy and industry regulations. This includes software development lifecycle (SDLC) policies, change management protocols, access control measures, and data privacy standards. A lack of formal IT policies may lead to inconsistent practices, security vulnerabilities, and poor accountability.
2. Architectural Foundations and Scalability
A company’s technology architecture forms the backbone of its product delivery, scalability, and operational efficiency. In a technical due diligence report, the architecture section helps investors and stakeholders assess the enterprise and software systems’ maturity, coherence, and future-readiness. This evaluation spans enterprise frameworks, software component design, integrations, and performance planning, directly impacting innovation, cost, and business agility. Here’s how this critical section breaks down:
i) Enterprise Architecture
A strategic enterprise architecture creates alignment between IT and business goals. It includes:
- Frameworks such as TOGAF or Zachman to guide architectural planning and standardization. These frameworks help define governance, methodologies, and structured workflows for complex environments.
- Documentation to clearly outline systems, processes, and data relationships across departments. Accurate documentation ensures traceability, supports compliance, and aids onboarding of new team members.
- Visualization tools to map infrastructure, identify bottlenecks, and ensure alignment across teams. Tools like ArchiMate or Lucidchart help stakeholders grasp system complexity at a glance.
ii) Solution Architecture
Solution architecture focuses on delivering technically sound systems based on business needs. Key aspects include:
- System-level design that defines modules, components, and interactions.
It provides a blueprint for developers, ensuring consistency and technical direction across the solution. - Alignment with enterprise architecture for consistency and governance. This ensures the solution integrates well within the broader IT landscape and supports organizational goals.
- Focus on scalability and flexibility to support future growth and evolving needs. Designing with scalability in mind helps prevent costly redesigns and system limitations later.
iii) API Design and Integration
Robust APIs and integrations ensure smooth communication between services, devices, and platforms. This includes:
- API design that is consistent, secure, and follows best practices (e.g., REST, GraphQL). Good design improves usability, encourages reuse, and reduces integration errors.
- API documentation and contracts to define expected behavior and facilitate easy integration. Well-documented APIs shorten development cycles and improve developer experience.
- Integration with physical devices like IoT sensors or embedded systems using appropriate communication protocols. These integrations require careful handling of latency, connectivity, and real-time data processing.
- Mobile compatibility by optimizing APIs for mobile networks, latency, and limited device resources. Mobile-friendly APIs help ensure reliable and responsive user experiences on smartphones and tablets.
iv) Performance and Scalability
Systems must be optimized for performance to meet growing demand and maintain responsiveness. Consider:
- Error handling and logging to detect, record, and respond to system failures effectively. Detailed logs and alerts enable rapid troubleshooting and support incident resolution.
- Performance characteristics such as throughput, response time, and load capacity. Regular performance benchmarking helps maintain system quality as usage scales.
- Scaling strategies, including horizontal scaling (adding more instances) or vertical scaling (upgrading hardware). Choosing the right strategy depends on the system architecture, expected growth, and cost constraints.
v) Software Architecture
A thoughtful software architecture ensures clean interactions between components and long-term adaptability. Key points:
- Component interactions and data flow to manage internal communication and data movement. Clear separation of concerns improves maintainability and reduces complexity.
- Storage design that supports both current and future data requirements.
Scalable storage solutions ensure reliable performance as data volume increases. - Extensibility and flexibility through modular design, which allows future features to be added easily. This approach supports agile development and reduces the risk of codebase rigidity over time.
- Buy vs. build decisions that weigh time-to-market, customization needs, and cost. Buying can save time and resources, while building offers better alignment with specific business workflows.
- Cost of maintenance, including updates, support, and infrastructure over the system’s lifecycle. A clear understanding of ongoing costs helps prevent budget overruns and supports sustainability.
3. Software Engineering
i) Operational Efficiency
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Process Automation and Tooling
Evaluating how the engineering team automates repetitive tasks is crucial for understanding their operational efficiency. The use of scripts, automation tools, and platforms for managing builds, deployments, and testing directly impacts development velocity and error reduction.
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Technologies in Use
An assessment of the team’s core technologies, frameworks, and development environments reveals their alignment with modern standards and scalability needs. Legacy tools or outdated stacks can indicate potential technical debt or upcoming refactoring needs.
ii) Innovation and Adaptability
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Research and Development Activities
Analyzing the company’s investment in R&D helps determine their commitment to technological advancement. This includes budget allocation, time set aside for experimental projects, and the presence of dedicated R&D roles.
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Capacity for Innovation
This involves assessing how readily the engineering team adopts new technologies and how responsive they are to change. Indicators include recent technology shifts, integration of emerging trends, and success in pivoting product features when needed.
iii) Knowledge Sharing and Continuous Learning
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Culture of Collaboration
An open and communicative engineering culture facilitates problem-solving and accelerates development. This section evaluates code review practices, pair programming, team retrospectives, and the use of collaboration platforms.
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Continuous Improvement Practices
Examining the feedback loops, technical postmortems, and learning initiatives (like internal workshops or knowledge bases) shows whether the team is committed to evolving its processes and skills over time.
iv) Team Structure and Roles
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Team Size and Composition
Understanding the size of the engineering team, including front-end, back-end, DevOps, QA, and full-stack roles, provides insight into the depth and breadth of the company’s engineering capabilities.
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Roles and Responsibilities
A clear definition of team member responsibilities ensures accountability and alignment with product goals. This includes how tasks are assigned, managed, and measured.
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Communication Channels
Reviewing internal communication practices, such as meetings, standups, and documentation, helps gauge how efficiently the team collaborates across functions.
v) DevOps and QA Models
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Centralized vs Distributed Approach
The structure of the DevOps and QA functions, whether centralized in a dedicated team or distributed across developers, can impact scalability, consistency, and deployment speed. Each model has trade-offs that must align with the company’s size and growth strategy.
4. Infrastructure & DevOps
i) Infrastructure Management
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Use of Infrastructure-as-Code (IaC)
IaC tools like Terraform or AWS CloudFormation are essential for reproducible, scalable, and secure infrastructure. Evaluating the maturity of IaC practices helps understand deployment efficiency and risk mitigation.
ii) Cloud Strategy
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Hosting Environment and Cloud Provider
An overview of the current cloud architecture, providers (AWS, Azure, GCP, etc.), and hosting configurations gives insight into performance, cost management, and vendor lock-in risks.
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Scalability and Flexibility
This section looks at whether the cloud strategy supports scaling, redundancy, and multi-region availability in alignment with the product roadmap.
iii) Configuration and Environment Management
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Environment Consistency
Assessing how development, staging, and production environments are configured and maintained ensures consistency and reduces deployment errors. Environment drift is a red flag in fast-paced engineering teams.
iv) CI/CD and Deployment
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Automation Pipelines
A mature CI/CD setup indicates streamlined testing, integration, and deployment processes. The report evaluates tools used, pipeline stability, rollback mechanisms, and approval flows.
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Deployment Frequency and Downtime
Deployment metrics such as frequency, average downtime, and time to recover from failure are good indicators of DevOps maturity and operational resilience.
v) Backup and Disaster Recovery
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Data Backup Strategy
Understanding how often backups are taken, what systems are covered, and how long data is retained is critical for business continuity.
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Disaster Recovery Planning
Assessing whether the company has tested and documented disaster recovery protocols ensures preparedness for major system failures or cyber incidents.
vi) Monitoring and Alerts
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System Monitoring Practices
Monitoring tools and dashboards (e.g., Prometheus, Grafana, Datadog) are reviewed for their effectiveness in providing real-time visibility into infrastructure and application health.
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Alerting Mechanisms
Effective alerting configurations, including thresholds, escalation paths, and on-call rotations, help prevent outages and reduce incident response time.
5. Quality
i) Testing Methodologies
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Testing Pyramid and Diamond Models
A strong testing architecture is essential for maintaining product reliability. The testing pyramid emphasizes a foundation of unit tests, followed by service-level and UI-level tests. This approach ensures a cost-effective and efficient testing process. Alternatively, some teams adopt the testing diamond model, which emphasizes integration tests more while maintaining a healthy layer of unit and UI tests.
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Testing Quadrants
Modern teams refer to the four testing quadrants to ensure comprehensive test coverage. These quadrants cover functional and non-functional testing, ranging from automated unit tests to manual exploratory testing and performance/security assessments. An analysis of the team’s use of these quadrants reflects the maturity of their testing strategy.
ii) Testing Practices and Tools
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Continuous Testing Implementation
Continuous testing is a key part of agile and DevOps environments. This involves running automated tests at every stage of development, from code commits to staging deployments. Evaluating how well continuous testing is integrated into CI/CD pipelines gives insight into potential release bottlenecks or risks.
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Frameworks and Tools in Use
The report should evaluate the test automation frameworks and tools currently in place (e.g., Selenium, Cypress, JUnit, TestNG). It’s important to assess whether these tools align with the technology stack and whether they support scalability and reliability in the testing process.
iii) Customer Support and Satisfaction
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Support Infrastructure
Reviewing the technical support systems reveals how well the company manages customer issues. The ticketing system, incident response workflows, escalation procedures, and knowledge bases are key to assess. A robust support infrastructure often correlates with reduced downtime and higher customer retention.
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Customer Feedback and Insights
Gathering and analyzing customer feedback helps measure user satisfaction and identify recurring pain points. NPS surveys, reviews, support tickets, and social media sources can uncover valuable insights into the product’s performance and user experience.
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Opportunities for Improvement
This section highlights areas where service delivery can be enhanced based on support metrics and customer feedback. Common recommendations include reducing response times, improving documentation, or adding self-service options for common issues.
6. Data
i) Analytics Capabilities
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Data-Driven Decision Making
It is crucial to assess how the organization uses data for decision-making. This includes the availability of real-time dashboards, key performance indicators (KPIs), and business intelligence tools (e.g., Tableau and Power BI). The report should evaluate how these tools inform product development, customer segmentation, and strategic planning.
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Tools and Platforms
Identifying the analytics stack helps determine its flexibility and scalability. Tools such as Google Analytics, Mixpanel, or Looker offer varying degrees of insight. Reviewing integration across systems is essential to ensure cohesive reporting.
ii) Data Governance and Management
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Governance Policies
Strong data governance is a marker of operational maturity. This includes clearly defined data ownership, access controls, and internal data policies. These frameworks ensure that data is accurate, secure, and used ethically.
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Data Quality Assurance
This part of the analysis should examine mechanisms for validating data accuracy, completeness, and consistency. Techniques include data audits, cleansing processes, and automated validation rules.
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Compliance and Security
Where applicable, review the organization’s compliance with GDPR, CCPA, or HIPAA regulations. A thorough audit should include how personal and sensitive data is stored, encrypted, and accessed.
iii) AI and Automation
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Use of AI in Data Systems
Explore how artificial intelligence or machine learning models are integrated into data operations. This could include predictive analytics, user behavior analysis, or intelligent automation. The report should note whether these applications are experimental or core to operations.
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Impact on Operations and Strategy
Evaluate the tangible outcomes of AI integration. Are data insights leading to measurable process improvements? Has automation replaced or enhanced manual reporting tasks? This section should highlight the strategic value AI brings to the data ecosystem.
7. Product
i) Product Roadmap and Vision
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Strategic Development Planning
A robust product roadmap reflects the company’s long-term vision and strategic intent. It should clearly outline feature development timelines, milestone tracking, and scalability goals. This roadmap helps assess the alignment of development priorities with broader business objectives.
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Market Fit and Customer-Centric Design
Evaluating how well the product responds to market demands and evolving customer expectations is crucial. This includes analyzing product adaptability to market trends, responsiveness to user feedback, and any mechanisms in place for continuous product improvement based on market research.
ii) Integration Capabilities
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API and External Integration Points
An effective product should be capable of seamless interaction with third-party platforms. This section reviews APIs’ availability, documentation, robustness, and their role in the product’s overall architecture.
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System Compatibility and Interoperability
Compatibility assessments explore how well the product interacts with various operating systems, devices, databases, and partner tools. Special attention is given to integration flexibility and architectural decisions that could hinder or support interoperability.
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Anticipated Integration Challenges
Common bottlenecks such as legacy systems, data format inconsistencies, or version control conflicts are analyzed. This includes identifying areas requiring rework or restructuring during future integrations or scalability phases.
iii) Product Collaboration Models
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Bottom-Up vs. Top-Down Development Approaches
The approach to product innovation, whether driven by developer insights (bottom-up) or executive strategy (top-down), can impact creativity, agility, and technical scalability. A balanced structure often yields better cross-team alignment.
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Collaboration Between Cross-Functional Teams
Collaboration efficiency between design, engineering, QA, marketing, and product management teams is evaluated. This includes communication practices, feedback loops, and shared tooling environments that enhance product delivery cycles.
8. Software Development Life Cycle (SDLC)
i) Operational Efficiency
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Process Optimization Techniques
Streamlining development workflows is essential for reducing cycle time and boosting quality. This involves identifying inefficiencies in CI/CD pipelines, backlog grooming practices, and release management protocols.
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Use of Automation in Development and Operations
Automation tools used in testing, deployment, monitoring, and rollback are assessed to understand how they contribute to speed, consistency, and reliability across the software lifecycle.
ii) Development Conventions and Standards
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Coding Guidelines and Best Practices
Coding conventions, review processes, and version control standards are analyzed to ensure consistency and maintainability. These practices directly influence long-term technical debt and codebase quality.
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Documentation and Definition Standards
Comprehensive documentation practices, including user guides, API references, system design documents, and architecture diagrams, are reviewed to determine their sufficiency for onboarding, debugging, and system evolution.
iii) Development Methodologies
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Agile, Scrum, and Kanban Implementation
Agile adoption maturity is evaluated through sprint planning, backlog management, and iterative release patterns. Methodology consistency is crucial for predictability, whether the team uses Scrum ceremonies or Kanban boards.
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Role Ownership and Accountability
Clear ownership of components, modules, or product areas enhances delivery speed and reduces risk. This includes how responsibilities are distributed and whether there’s a single point of accountability for each feature or function.
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Tools and Technology Stack
Tooling choices such as JIRA, Git, Jenkins, or containerization platforms (e.g., Docker, Kubernetes) are evaluated based on their contribution to productivity, observability, and automation capabilities.
9. Security & Compliance
i) Advanced Security Measures
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Security Component Integration
Key technologies like firewalls, secure coding practices, encryption standards (e.g., TLS, AES), and access controls (RBAC, IAM policies) are evaluated for adequacy and resilience.
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Intrusion Detection and Threat Monitoring
Deployment of SIEM tools and intrusion detection systems (IDS) helps identify and mitigate threats in real time. This section assesses the effectiveness of such tools in the current infrastructure.
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Automated Security Responses
Automation in vulnerability scanning, patch management, and incident remediation is critical in reducing manual errors and response latency. The degree of automation is a significant factor in threat resilience.
ii) Compliance Monitoring and Auditing
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Adherence to Industry Standards
Compliance with frameworks such as GDPR, HIPAA, SOC 2, ISO 27001, or PCI-DSS is evaluated. This includes checking certifications and internal policies designed to maintain compliance.
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Continuous Auditing Practices
Ongoing compliance monitoring, through automated audits, regular security reviews, and documentation practices, is key to identifying policy violations before they become liabilities.
iii) Security in Business Continuity
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Continuity Plans with Security Layers
Business continuity plans must include security contingencies like redundant infrastructure, failover mechanisms, and secure data backups. The goal is to ensure minimal disruption during crises.
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Incident Response and Recovery Protocols
Defined protocols for handling security incidents, such as ransomware attacks or data breaches, are analyzed for response time, clarity of roles, and post-incident evaluation mechanisms.
Concluding Thoughts
A comprehensive technical due diligence process delves into various facets of a company’s technology landscape, including its infrastructure, software architecture, data management practices, and cybersecurity measures. This thorough examination uncovers existing vulnerabilities and highlights areas for improvement and growth, providing a roadmap for post-acquisition integration and value creation.
Moreover, technical due diligence fosters transparency and builds trust between parties, facilitating smoother negotiations and more informed decision-making. By proactively addressing technical challenges and aligning technological capabilities with business objectives, companies can mitigate risks, avoid costly surprises, and set the stage for a successful merger or acquisition.
In essence, investing time and resources into technical due diligence is not merely a procedural formality but a strategic imperative that can significantly influence the success and longevity of an M&A transaction.
Partnering with Zartis for Smarter Tech Investments
Zartis is a trusted technology consulting partner with years of expertise conducting technical due diligence reports for investors, startups, and scaling companies. Our assessments go beyond surface-level evaluations, offering in-depth analyses of product architecture, code quality, team structure, scalability, and security practices. With a tailored approach, we help stakeholders uncover technical risks early, identify growth opportunities, and align the tech vision with long-term business goals.
Our experienced team of engineers and product experts leverages years of hands-on experience across industries to deliver clear, actionable insights. Whether you’re evaluating a potential acquisition or preparing your startup for investment, Zartis team ensures your technical foundation stands up to scrutiny. Ready to make informed decisions with confidence?