Business

Understanding The Basics of Tequila

Published

3 years ago

July 17, 2023

The world of tequila contains more than a few surprises. Luckily, there are no better guides than Alec and Ana Tesa, founders of award-winning distillery Eleven20 Tequila.

“The first thing to understand is that there are a lot of fakes out there,” says Alec Tesa. Given the Tesas’ passion for traditional distilling techniques and Mexican culture, they’re the perfect people to explain the fundamentals of this amazing beverage, from what constitutes true tequila to identifying superior varieties and finding the best one for you.

Not all tequila is real

For a beverage to count as true tequila, it needs to meet certain criteria. “Most importantly, it must be made from blue agave in certain places, such as Jalisco, Guanajuato, and Michoacan,” Alec says. “That’s why we craft our artisanal tequila in the heart of tequila country.”

“While you might be able to buy spirits distilled from alternative kinds of agave in California or other places, these beverages taste different,” Ana Tesa adds. They also can’t be called tequila, which by definition must come from blue agave in Mexico.

“Terroir is important, just like with wine,” Alec explains. “Champagne only comes from Champagne, which is near Paris, and Bordeaux only comes from Bordeaux in the southwest of France. There’s something unique and magical about these places that lead to a truly special drink. It has to do with the soil, the latitude, the altitude — everything in the environment comes together perfectly to make lovely elixirs that are exclusive to those particular places.”

“The distillation of tequila from true Mexican blue agave is a beautiful and historic cultural tradition that can’t be replicated elsewhere,” Ana continues. “Keep in mind this knowledge has been handed down through generations. Proper tequila is made in Mexico by Mexicans, using methods they have refined for thousands of years.”

Unfortunately, illicit producers of fake tequila continue to try to fool consumers and often to great lengths to hide the provenance of their products, even reusing bottles from legitimate tequila distilleries to masquerade as the real thing. In addition to scamming people with lesser quality beverages, sometimes they also bottle their products with toxic substances that can have dangerous effects on those who drink them.

How to identify real tequila

To spot true tequila, the Tesas recommend reading labels carefully. “It might seem obvious, but the first rule to follow is to look for the word tequila specifically,” Alec remarks. “Not agave liqueur, not agave eau de vie, and not agave distillates — none of those are the real thing. Also, remember that mezcal is different from tequila as well, since it can be made from other agaves, not pure blue agave like tequila.”

“The bottle should be in pristine condition,” Ana says. “If it looks like someone might have taken the lid off and refilled it, then keep in mind that dubious beverage companies actually do that, so that might be what you’re actually looking at.”

“Don’t buy anything that doesn’t have a label,” Alec adds. “You really shouldn’t even accept a drink for free from a bottle like that!”

The Tesas also recommend purchasing tequila only from established, reputable retailers. “Avoid dodgy situations,” Ana continues. “Don’t try to buy it off the street or at a flea market. It might look like a good deal, but you’re really just getting ripped off.”

According to the Tesas, an even better way to verify authenticity is to look the beverage company up on the official list of producers, which the Consejo Regulador del Tequila (CRT) makes available online. “If the company isn’t listed, it’s not what you want,” Alec says.

Even among real tequilas, a range of different qualities is available.

Identifying the best tequila

“The best tequila is made entirely from blue agave,” Alec says, “so look for ‘100 percent’ on the label.”

“You should also look for brands that don’t have any additives,” Ana adds. “The best tequila is pure. Similarly, if a label says ‘Mixto,’ that means it’s tequila mixed with up to 49 percent other things. For some brands, that means nearly half the drink is sugar.”

Ultimately, what makes a great tequila is its great taste. “Fancy packaging might look impressive, but you can’t drink it,” Alec says. “What’s more important is what that spirit tastes like neat.”

Tequila can also be aged in barrels for different periods of time, which changes the drink’s flavor profile. The youngest variety is called blanco, which goes straight to store shelves after bottling. Reposado comes next, having been aged for two months up to a year, while Añejo is the oldest, aged for one to three years.

“While many people favor aged tequilas, you’ll need to taste the different options yourself to see what you prefer,” Ana says. “Blanco tends to be flashier, which lots of people like best. Whiskey drinkers tend to prefer the older versions, which can take on hints of spices or vanilla from the barrels.”

Experience a Mexican tradition

Finally, the Tesas recommend buying tequila from producers that use traditional Mexican production techniques like Eleven20. “The best experience is an authentic one,” Alec says. “These processes might be slower, but the taste is more than worth the effort.”

For a true Mexican experience, make sure you turn to true Mexican tequila like Eleven20.

Business

AI in Asset Management Explained: How Leading Firms Apply It

Published

2 hours ago

April 16, 2026

Matthew Wiles

AI in asset management explained at its most basic level is this: using machine learning, data modeling, and automation to make faster and more accurate investment decisions. The applications vary widely across asset classes, fund strategies, and operational functions. Understanding where AI creates real value separates productive adoption from expensive experimentation.

Asset managers now face a data environment far larger than any human team can process manually. Market signals, company filings, macroeconomic indicators, alternative data sources, and portfolio monitoring all generate information continuously. AI tools process that information at scale. They surface patterns that traditional analysis would miss or find too late.

AI in Asset Management Explained Across Core Investment Functions

AI delivers the most measurable results when applied to specific investment functions rather than deployed as a general capability. The clearest applications sit in portfolio construction, risk management, and credit analysis.

Portfolio Construction and Factor Modeling With AI

Traditional portfolio construction relies on return and correlation assumptions built from historical data. AI-driven portfolio tools go further. They process real-time market data, alternative signals, and macroeconomic inputs simultaneously. This surfaces factor exposures that static models miss.

Machine learning models in portfolio construction can:

Identify non-linear relationships between asset classes that correlation matrices do not capture
Adjust factor weightings dynamically as market conditions shift rather than on a quarterly rebalancing schedule
Flag concentration risks before they appear in standard risk reports
Model tail scenarios using a broader range of historical stress periods than traditional value-at-risk models allow

James Zenni, founder and CEO of ZCG with over 30 years of capital markets experience, has built the platform’s investment approach around the principle that better data and faster analysis produce better outcomes. That view shapes how AI capabilities get deployed across ZCG’s private equity, credit, and direct lending strategies.

Credit Analysis and Private Markets AI Applications

Credit analysis in private markets has historically depended on periodic financial reporting and relationship-based deal intelligence. AI changes that model. Lenders using machine learning tools now monitor borrower health continuously rather than waiting for quarterly covenant tests.

Specific credit applications include:

Cash flow pattern analysis that identifies revenue deterioration weeks before it shows up in reported financials
Supplier and customer relationship mapping that flags single-source dependencies and concentration risks
Covenant monitoring automation that tracks hundreds of credit agreements simultaneously and alerts teams to early warning signs
Loan pricing models that incorporate current market spread data and comparable transaction history

These capabilities compress the time between identifying a problem and taking action. In credit, that time advantage directly affects loss rates and recovery outcomes.

AI in Asset Management Explained Through Risk and Compliance Applications

Risk management and regulatory compliance represent two of the highest-value AI applications in asset management. Both functions involve processing large volumes of structured and unstructured data under time pressure.

How AI Transforms Risk Monitoring in Asset Management

Traditional risk monitoring produces reports at set intervals. AI-powered risk systems run continuously. They flag anomalies in position data and monitor correlated exposures across a portfolio. Alerts fire when market conditions shift beyond defined thresholds.

The practical risk management applications include:

Real-time portfolio stress testing against live market inputs rather than end-of-day snapshots
Liquidity modeling that accounts for position size relative to market depth across multiple scenarios
Counterparty exposure monitoring that aggregates risk across instruments, custodians, and trading relationships
Regulatory reporting automation that reduces manual preparation time and lowers the risk of filing errors

ZCG applies these capabilities across its approximately $8 billion in AUM. The platform was founded 20 years ago. It built its investment infrastructure around systematic data analysis and operational discipline.

AI for Operational Efficiency in Asset Management Firms

Beyond investment decisions, AI delivers significant value in fund operations. Back-office functions like reconciliation, reporting, and compliance documentation consume substantial resources at most asset management firms.

AI tools applied to fund operations include document processing systems. These extract and verify data from offering documents, side letters, and subscription agreements automatically. Reconciliation tools flag breaks between custodian records and internal systems automatically. Investor reporting platforms generate customized materials from structured data inputs, reducing the manual production time significantly.

ZCG Consulting (“ZCGC”) advises operating companies across more than a dozen sectors on operational improvement programs, including technology-driven process redesign. Those operational efficiency principles translate directly to asset management back-office functions.

Applying AI to Asset Management: Limitations Firms Must Address

AI in asset management explained fully must include the limitations. Models trained on historical data perform poorly when market regimes change. Overfitting produces tools that work in backtests but fail in live environments. And AI outputs require experienced interpretation to avoid acting on statistically significant but economically meaningless signals.

The ZCG Team approaches AI adoption with the same discipline it applies to investment underwriting. Every tool requires a defined use case and a measurable success metric. A review process keeps experienced judgment in the decision chain. That framework prevents the common failure mode where AI adoption generates activity without improving outcomes.

Firms that treat AI as a capability layer on top of sound investment processes generate sustainable advantages. Those that treat AI as a replacement for process discipline find the technology amplifies existing weaknesses. It rarely corrects them.