Noha Gad

Businesses do not all operate the same way throughout the year. Some enjoy steady demand month after month, while others experience clear peaks and quieter periods driven by seasons, holidays, or industry cycles. Understanding these patterns is essential for owners, managers, and investors who want to plan wisely and avoid cash-flow surprises. From tourism resorts and landscaping companies to holiday retail and travel services, seasonal companies can be highly profitable when managed well; however, they also face distinct challenges in finance, staffing, and marketing. 

What are seasonal businesses?

Seasonal business refers to fluctuations in business that correspond to seasonal changes. This does not mean they operate only in one season for the most part, with a few exceptions. Key examples of seasonal businesses include alternative holiday retailers, moving services, tour guides, holiday clubs, and more. There are few steps founders and business owners must follow to start a seasonal business:

• Understand the market. As an owner, you must be sure there is enough demand for the products or services that can generate enough income during the peak season. To gain knowledge, you can conduct simple market research, asking potential customers whether they would buy from you at the prices you are considering charging.
• Develop a marketing plan. Seasonal businesses must often work harder to promote themselves, often to simply remind customers they are there. To hit the ground running, you should leave enough time for your publicity and advertising to attract customers. 
• Manage cash flow. Successful cash flow management can represent a significant challenge for seasonal businesses because they receive most of their income in a set period, but may have outgoings at other times. The temptation can be to spend too much when cash is plentiful, creating cash flow issues when revenue is down.
• Purchase essentials. You must accurately estimate demand by using your market knowledge/research. Getting favorable terms from suppliers can be more difficult when buying within a limited period, but there's no harm in trying by using your business relationship with them. 
• Diversify products. If offering discounts and holding promotions doesn't help you to make sales when sales slow down, maybe you could modify your offer to give it wider and longer-lasting appeal. 
• Improve offering and analyze results during quiet period. Use quiet periods to analyze your results and think of ways you can improve the business for when it becomes active again.  

Key challenges seasonal businesses face

Seasonal businesses share several recurring difficulties that stem from their uneven revenue patterns. These challenges affect cash flow, staffing, inventory, and overall planning.

• Cash-flow volatility: revenue concentrates in a few busy months, while many costs, such as rent, loan payments, insurance, and subscriptions, continue year-round. This mismatch can create liquidity gaps during the off-season.
• Staffing and training pressures: Owners must hire and train temporary staff quickly for peak periods, then manage layoffs or reduced hours when demand falls. High turnover and repeated onboarding can raise costs and affect service quality.
• Inventory and capacity planning risks
  Over-ordering before a slow period ties up cash in unsold stock, while under-ordering before a peak can lead to missed sales and dissatisfied customers. Balancing inventory levels with uncertain demand is a constant challenge.
• Marketing timing inefficiencies. Spending on advertising too late or too early reduces return on marketing investment. Seasonal businesses must align promotion with the demand curve to maximize impact.

To sum up, seasonal businesses can deliver strong profits, but only when owners plan for the full annual cycle, not just the busy months. Success depends on understanding demand patterns, preparing a focused marketing plan, and, above all, managing cash flow so that peak-season earnings cover off-season costs.

The main challenges, such as cash-flow volatility, staffing swings, inventory risks, and mistimed marketing, are predictable and manageable with the right discipline. Founders who research their market, negotiate smartly with suppliers, diversify offerings, and use quiet periods to analyze results and improve operations are better positioned to turn seasonality from a risk into a strategic advantage.

Ghada Ismail

When people talk about venture capital and private equity, two terms appear repeatedly: Limited Partners (LPs) and General Partners (GPs). While both are essential to an investment fund, they play very different roles.

In simple words, LPs provide the capital, while GPs manage and invest it. Understanding this relationship is key to understanding how venture capital and private equity funds work.

What is a Limited Partner?

A Limited Partner is an investor who commits money to an investment fund but generally does not participate in its day-to-day management.

LPs can include pension funds, sovereign wealth funds, family offices, insurance companies, endowments, banks, and high-net-worth individuals. In the venture capital ecosystem, they provide the majority of the capital that funds use to invest in startups.

LPs typically commit a specific amount to a fund, but they do not necessarily transfer the entire amount upfront. Instead, the GP can make capital calls when investments or other fund expenses require funding.

In return, LPs receive a share of the fund's returns. Their potential liability is generally limited to the amount they have committed to the fund, which explains the term "limited" partner.

What is a General Partner?

General Partners are responsible for running the investment fund.

The GP is typically the venture capital or private equity firm managing the fund. Its responsibilities include identifying investment opportunities, conducting due diligence, negotiating deals, supporting portfolio companies, and deciding when to exit investments.

GPs also manage the fund's relationship with LPs, provide performance updates, and oversee the fund's overall strategy.

Unlike LPs, GPs are actively involved in investment decisions and typically commit some of their own capital to the fund.

The basic financial structure behind LP and GP partnerships

LPs and GPs usually make money in two main ways: management fees and carried interest.

GPs typically charge a management fee to cover the costs of running the fund, such as salaries, office expenses, and other operating costs. They can also earn carried interest, or “carry,” which is a share of the profits made from the fund’s investments.

For example, if a venture capital fund invests in several startups and those investments become highly successful, the GP can receive a percentage of the profits once certain conditions are met.

LPs receive most of the profits generated by the fund after management fees and carried interest are deducted. In simple terms, LPs provide most of the capital, while GPs manage the fund and earn fees plus a share of the profits if the investments perform well.

LP vs. GP: The Key Difference

The easiest way to remember the distinction is:

LP = supplies capital
GP = manages capital

LPs typically do not choose individual startups or companies for investment. Instead, they select funds based on factors such as the GP's track record, investment strategy, team, geographic focus, and expected returns.

GPs then deploy the capital according to the fund's investment strategy.

Why the Relationship is Important

A strong LP-GP relationship can be critical to a fund's success.

LPs want GPs to generate attractive returns while managing risk responsibly. GPs, meanwhile, rely on LPs for the capital needed to execute their investment strategy and often seek to build long-term relationships that can support future funds.

For startups, this relationship may seem distant, but it can have a direct impact. A well-capitalized VC fund has the resources to back promising startups through multiple funding rounds and potentially provide additional support as they scale.

To Wrap Things Up…

LPs and GPs are two sides of the same investment structure. LPs provide the financial firepower, while GPs provide the investment expertise and management.

The model allows institutions, family offices, and other investors to gain exposure to private markets without managing individual investments themselves, while giving professional fund managers the capital needed to identify and build the next generation of companies.

For anyone looking to understand how venture capital works, knowing the difference between LPs and GPs is one of the best places to start.

Shaimaa Ibrahim

Arabic voice AI technologies are at the forefront of digital transformation in the GCC region, driven by growing demand for intelligent solutions that understand local dialects and interact with users spontaneously and instantly, as well as the increasing need for data sovereignty and compliance. Against this backdrop, Hamsa, a US-listed company headquartered in Amman, stands out as an AI company specializing in developing advanced models that understand Arabic language and dialects; an integrated voice AI system; and intelligent agents capable of interacting with users, implementing tasks, and integrating with enterprise systems.

In an exclusive interview with Sharikat Mubasher, Ibrahim Jabarin, CEO of Hamsa, discussed the company’s strategy, its vision for the future of voice AI in the region, its competitive position among international peers, and its expansion plans across Saudi Arabia, the UAE, and other Gulf and Arabian markets.

Jabarin highlighted major pitfalls in the sector and unveiled Hamsa’s roadmap that includes supporting more than 16 languages, developing a new generation of intelligent agents, and enhancing security and compliance, thereby strengthening its presence regionally and globally.

First, tell us more about Hamsa, what distinguishes it in the Arabic AI technologies market, and the key solutions and services that the company provides for enterprises?

Hamsa is a voice AI company that develops its proprietary models capable of understanding and processing the Arabic language. We developed our Arabic model from scratch rather than relying on models originally developed for English and subsequently adapted for Arabic. This approach positively impacted performance; the accuracy of Hamsa’s models reached about 94% in transcribing Saudi and Gulf dialects and about 92% in standard Arabic. 

The company is also developing an integrated ecosystem that features speech recognition, voice synthesis, noise cancellation, speaker recognition, and integration with enterprises’ communication systems and operational infrastructure. This provides a quick response of up to 280 milliseconds to the first audio byte, with intelligent agents’ response time ranging from 0.8 to 1.2 seconds.

For enterprises, Hamsa provides a wide spectrum of comprehensive solutions, including real-time voice processing for calls and web applications; a Low-Code platform dedicated to designing chat agents and executing operations; APIs that help developers build their own solutions; and the ‘Hamsa Media’ product that processes voice content at large scale, including transcription, voice-over, and dubbing.

All these solutions can be deployed within customer data centers or via a private cloud hosted within the country to meet enterprises’ need for data sovereignty and compliance. 

To what extent have the strategic partnerships forged by Hamsa contributed to expanding the company’s business, deepening its regional presence, and attracting new customers?

For Hamsa, partnerships are not merely an additional sales channel; they represent a fundamental pillar for entering markets and accelerating the adoption of voice AI solutions, particularly in regulated sectors, such as banking and government entities that choose trustworthy suppliers with established experience and relationships. 

We adopt four main partnership tracks: systems integration and consulting firms, infrastructure and hardware partners, customer experience platforms and contact centers, as well as telecommunications operators

These partnerships help accelerate sales cycles, strengthen Hamsa’s ability to implement projects and expand in the market without a significant increase in the teams, and unlock access to strategic enterprises and accounts that are otherwise difficult to reach directly.

The company also relies on integration with customers’ existing technical infrastructure through open protocols and standards that reduce transformation complexities and shorten implementation time. Therefore, Hamsa’s strategy for entering any new market begins with searching for the right partner before the first customer. This underscores our belief that a strong partnership is the cornerstone for building a sustainable presence and accelerating growth.

Hamsa recently concluded a strategic agreement with OmniOps. In your opinion, how will this partnership accelerate the adoption of voice AI technologies within government and private organizations?

The significance of this partnership lies in its ability to address the most prominent barriers to voice AI adoption in the Kingdom, which are no longer related to model quality, but rather revolve around three key questions: where is the data stored? Who operates the solutions within the Kingdom? And how are they integrated with existing systems? The partnership provides comprehensive answers to all these requirements by keeping sensitive voice data within the Kingdom, with an accredited local authority responsible for operations, integration, and support, in compliance with the Personal Data Protection Law (PDPL) and data localization requirements.

This ecosystem enables enterprises to transition from limited pilot phases to full-scale production deployment by providing models, infrastructure, integration, and support within an integrated framework and a single accountable entity, rather than dealing with multiple suppliers and technologies.

Based on Hasma’s experience, this approach could shorten project implementation timelines to between six and nine months, while delivering intelligent Arabic voice services all day long, with all data remaining within the Kingdom's borders.

Why does Saudi Arabia represent a priority in Hamsa’s expansion strategy, and where do you see growth opportunities you are targeting over the upcoming period?

Saudi Arabia is the top market for Hamsa for several reasons. First, language and dialects. The company’s technologies have been built from the ground up to understand Arabic and its dialects, particularly the Saudi dialect, rather than adapting a global product to meet local market needs.

Second, the market size. The Kingdom hosts the largest call center operations in the region, especially in the banking, telecommunications, and healthcare sectors, which handle millions of calls per month. This offers significant opportunities to automate repetitive tasks using intelligent voice agents.

Third, the regulatory and strategic environment. Vision 2030 and the National Data and AI Strategy have made AI adoption a national priority, accelerating transformation and uptake.

Fourth, data sovereignty requirements. Though these requirements represent a challenge for many solution providers worldwide, they represent a strength for Hamsa. We designed our solutions to operate within customers’ data centers or via a private cloud hosted within the Kingdom, in line with compliance and data localization mandates.

We see significant growth opportunities in the banking and financial sector, particularly in customer services, card management, collections, and identity verification; in telecommunications, government services, and healthcare, in areas such as patient follow-up and preliminary screening; as well as retail and e-commerce, in order management and delivery services.

Beyond Saudi Arabia, which other GCC markets does Hamsa target, and what are your expansion plans for the next few years?

The United Arab Emirates is the second most important strategic market for Hamsa, as it is one of the fastest countries globally in AI adoption, particularly within the government sector, along with its position as a regional innovation hub. Hamsa enables the deployment of its solutions within the country, in line with the regulatory requirements and data sovereignty mandates.

Qatar represents another significant market for the company, notably in the healthcare and government services sectors, while Bahrain and Oman are considered promising markets, where Hamsa relies on local partnerships to reach customers and implement projects efficiently.

Beyond the GCC, Hamsa aims to expand in Egypt, Jordan, and Morocco, given the substantial operational scales these markets offer in communications centers, government services, and the financial sector. The next phase will focus on expanding into global markets by strengthening the platform to support more than 16 languages, leveraging the company’s expertise in developing models that can understand Arabic dialects and switch between languages despite limited data availability.

In all markets it enters, Hamsa adopts a unified approach that depends on three main principles: a local partner with deep market knowledge and established relationships; hosting solutions within the country to ensure compliance with sovereignty and data protection requirements; and providing technical and operational support in accordance with local time.  

Amidst the growing competition with global companies, where does the competitive advantage of Hamsa’s Arabic voice AI solutions lie?

It is important to acknowledge that global companies have extensive expertise and substantial budgets to develop AI technologies; however, our competition is not built on scale, but on delivering value that resonates with the needs of the Arab market. We believe Hamsa excels in four key areas: 

1. Building Arabic models from the ground up. Most global solutions rely on models originally developed in English, with Arabic support added as an afterthought. This limits their ability to understand local dialects and switch between Arabic and English. At Hamsa, we trained our models from the beginning on this linguistic reality.
2. Owning the full technology stack. Hamsa develops core components of the technology stack through a single platform, from speech recognition and voice synthesis to telecommunications, which ultimately reduces complexity and costs. This enables us to optimize performance, adjust response time, and deliver a stable, reliable experience.
3. Data sovereignty and compliance. Hamsa’s solutions are designed to operate within customers’ data centers or via a private cloud hosted within the Kingdom, fulfilling the requirements of banks and government entities. Our solutions comply with personal data protection laws in Saudi Arabia and the UAE.
4. Deep market knowledge. Our teams across the region deeply understand enterprises' needs, procurement dynamics, and regulatory requirements. This enables us to develop solutions tailored to the local market, including models specifically designed for local dialects.

How do you see the future of AI Agents in the GCC region?

The voice AI market in the region is moving toward three major shifts, the first of which has already begun:

1. From pilot phases to full-scale production: Organizations are moving beyond exploring potential and are now seeking scalable, production-ready solutions with high reliability, compliance, and auditability. 
2. From providing answers to executing procedures: The current generation of intelligent assistants can complete transactions, such as checking balances, booking appointments, opening tickets, and implementing procedures through integration with enterprise systems.
3. From voice-only to multi-interface experiences. The future points toward intelligent agents that combine voice conversation with visual interfaces, offering option display, sending confirmations, and visualizing order or transaction status. I expect government entities to lead this shift ahead of the private sector, given their focus on improving service quality and enhancing accessibility. The biggest challenge will not be developing the models themselves, but rather integrating them with legacy systems, ensuring compliance with regulatory frameworks, and measuring their business impact through clear, measurable metrics.

Based on your experience, what are the key challenges facing Arab AI companies today, and what does the sector need to accelerate its growth and enhance competitiveness regionally and internationally? 

Voice AI companies in the region face five main challenges. The first is the limited availability of high-quality voice data, especially for Arabic dialects, which forces companies to build their own database from scratch, ultimately slowing model development. Second, the high cost of graphics processing units (GPUs) and sovereign infrastructure, which imposes financial burdens on local companies.

Third, the scarcity of specialists in deep learning and speech processing technologies. This places regional companies in direct competition with global companies for top-tier talent. Securing finance is the fourth challenge, as model development companies require significant investment before generating revenue. 

Fifth, long procurement cycles and preference for global suppliers, along with the absence of unified Arab references to measure model performance, collectively hinder the expansion of local companies.

To accelerate the sector’s growth, the region needs to:

1. Create common, open Arabic databases and references that support model development.
2. Provide a sovereign computing infrastructure with competitive costs to promote local innovations.
3. Expand the presence of specialized investment funds that understand the nature and cycle of developing AI models.
4. Strengthen regulatory coordination among Gulf countries to reduce the variability of compliance requirements, enabling companies to expand regionally within a unified, more efficient framework.

What are Hamsa’s ambitions for the next few years, either on geographical expansion, launching new products, or establishing partnerships?

Hamsa’s roadmap for the upcoming years is centered on four key pillars. Geographically, we focus on strengthening our presence in Saudi Arabia and the UEA, then expanding into other GCC countries, notably Qatar, Kuwait, and Bahrain. Later, we will enter Morocco before expanding into Europe and the US through our multilingual platform.

At the product level, we are pursuing three strategic tracks: expanding the platform to support over 16 languages while preserving Arabic’s positional excellence; developing intelligent agents that integrate voice capabilities with visual interfaces; and advancing custom voice solutions, advanced analytics, and model fine-tuning tailored to the specific needs of various sectors.

On the compliance and security side, we aim to achieve ISO 27001 certification and transition to SOC 2 Type II compliance, while expanding the deployment of voice agents to web applications, smart kiosks, and other environments where voice-based interaction offers superior efficiency.

Hamsa will continue to forge comprehensive partnerships with infrastructure and digital sovereignty partners, system integrators, and customer experience platforms, thereby accelerating our expansion and ensuring implementation quality.

Our ambition for Hamsa is to become the premier choice for Arabic voice AI and subsequently strengthen its position globally through a multilingual platform.

Translation: Noha Gad

Ghada Ismail

As artificial intelligence becomes increasingly embedded in business, not everything an AI system generates should be taken at face value.

Two concepts often create confusion in this context: synthetic data and AI hallucination. Both involve information generated by AI rather than directly collected from the real world, but their roles could not be more different.

One is a tool that can help businesses overcome data limitations. The other is a reliability problem that can undermine trust in AI systems.

What Is Synthetic Data?

Synthetic data is artificially generated information designed to replicate the characteristics and patterns of real-world data.

Instead of collecting thousands of real customer transactions, for example, a startup could generate synthetic transactions that mimic realistic purchasing behavior. Similarly, an AI developer could create synthetic images, customer profiles or financial scenarios to train and test an AI model.

This can be particularly valuable for startups that lack access to large datasets or operate in areas where data is sensitive.

Synthetic data can help companies reduce data-collection costs, accelerate AI development and limit exposure to sensitive information. It can also allow developers to test AI systems across scenarios that may be difficult or expensive to reproduce in the real world.

However, synthetic data is only useful when it is representative and properly validated. Poor-quality synthetic datasets can reproduce errors, biases or unrealistic patterns.

What Is AI Hallucination?

AI hallucination is something very different.

It occurs when an AI model generates information that sounds convincing but is factually incorrect, unsupported, or completely fabricated.

An AI chatbot, for instance, might invent a statistic, cite a research paper that does not exist, or provide an incorrect explanation with complete confidence.

Hallucinations can occur because generative AI models are designed to predict and generate likely sequences of information. They do not automatically distinguish between what is true and what merely appears plausible.

For businesses, this can become a serious issue. An inaccurate AI-generated answer may be inconvenient in a consumer application but potentially damaging in areas such as financial services, healthcare, legal technology or enterprise decision-making.

Synthetic Data vs AI Hallucination

The simplest way to distinguish the two is intention and purpose.

Synthetic data is deliberately created. AI hallucination is an unintended output.

Synthetic data is generated for a specific purpose, such as training, testing, or simulating scenarios. It can be reviewed, measured, and validated before being used.

Hallucinations, by contrast, emerge during an AI system's operation and need to be detected, corrected, or prevented.

In other words, synthetic data can be an AI development asset, while hallucination is an AI reliability risk.

Why Does This Matter for Startups?

The distinction is especially important for startups building AI products.

Early-stage companies often face limited access to high-quality data. Synthetic data can provide a way to experiment and develop models without relying exclusively on costly or sensitive real-world datasets.

At the same time, startups must ensure that their AI products do not generate unreliable information. A hallucination can quickly erode customer confidence, particularly when an AI product is being used to make business or financial decisions.

Importantly, synthetic data does not automatically cause hallucinations. However, if synthetic datasets are poorly designed or contain unrealistic patterns, they can affect the quality of the models trained on them.

That makes data validation, testing, and human oversight critical throughout the AI development process.

One Is a Tool, the Other Is a Risk

Synthetic data and AI hallucination may both involve AI-generated information, but treating them as interchangeable misses a crucial distinction.

Synthetic data can help startups solve one of AI's biggest challenges: access to useful, scalable, and privacy-conscious data.

Hallucinations represent another challenge: ensuring that AI systems remain accurate and trustworthy.

As businesses move beyond experimenting with AI and begin deploying it in real-world operations, knowing the difference between data that was intentionally generated and information that was unintentionally invented will become increasingly important.

Noha Gad

In the investment management world, it is common for fund managers to earn a performance fee when they generate strong profits for their clients, but this arrangement can create an unfair situation if those gains are later lost and then partially recovered. Without additional safeguards, a manager could collect a performance fee during a good year, see the portfolio value drop sharply in the following year, and then earn another performance fee simply by bringing the fund back to its earlier level even though investors have not truly benefited from any new gains.

The high-water mark is a widely used rule in hedge funds and other managed investment products that prevents this outcome by linking performance fees to real, additional value creation rather than temporary swings in portfolio value. This rule sets the highest value that the fund has ever reached as a benchmark, allowing managers to charge a performance fee only on profits that rise above that previous peak.

What is meant by a high-water mark?

This term refers to the highest level that a body of water reaches, but metaphorically, it refers to the peak value of an investment fund or the highest point of achievement.

In the business realm, the high-water mark is a benchmark investment funds use to ensure investors only pay performance fees when a fund’s value reaches a new peak. It ensures that investors do not have to pay performance fees for poor performance, but, more importantly, guarantees that investors do not pay performance-based fees twice for the same amount of performance.

For asset management companies, including a high-water mark in their fee structure can be a strong signal of fairness and alignment with investors, ultimately contributing to attracting and retaining capital in a competitive market.

From a managerial perspective, the high-water mark encourages a focus on sustainable, long-term performance rather than short-term increases that might be followed by sharp declines. As performance fees are only available after the fund exceeds its highest historical value, managers have a clear incentive to avoid strategies that generate volatile returns with large drawdowns.

Why do high-water marks matter?

High-water marks are widely viewed as a key investor protection in hedge funds and other performance-fee-based investment structures, and they bring several clear advantages for both investors and fund managers. This includes:

• Protecting investors from paying twice for the same gains.
• Aligning manager incentives with genuine outperformance.
• Promoting more disciplined risk management.
• Supporting long-term thinking over short-term spikes.
• Enhancing trust and credibility with investors.
• Encouraging clearer communication about performance.

In conclusion, the high-water mark is more than a technical fee detail; it is a core element of fair and transparent performance-based compensation in investment management. Setting the fund’s highest historical value as the threshold for performance fees ensures that managers are rewarded only for creating new gains, not for recovering past losses or simply returning to earlier levels.

For investors, this structure provides a clear safeguard against paying twice for the same performance and helps align the manager’s interests with their own long-term outcomes. For managers and firms, it encourages more disciplined risk-taking, supports a focus on sustainable growth, and can strengthen trust and credibility in a competitive market.