MPP Insights Blog Archive https://mpp-insights.com en Fri, 03 Apr 2026 16:45:34 +0300 The 2016 Social Media Throwback Trend Is Helping Train AI Models https://mpp-insights.com/blog/throwback-photos-teach-ai Wed, 25 Feb 2026 14:55:00 +0300 Sahar Fallah AI & Machine Learning If you have joined the 2016 throwback trend, beware. Before-and-after pictures can teach AI models how faces change over time. The photos are clean data and add structured information that helps machines recognize patterns.

The 2016 Social Media Throwback Trend Is Helping Train AI Models

old images that people upload on social media, enables AI models learn how faces change over time.
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Sahar Fallah AI & Machine Learning
02.03.2026
There’s a trend among Instagram users where they share a photo of themselves from 2016 next to a photo from 2026. Sharing memories with a 10-year gap sounds fun and interactive for online users, but you might be surprised if I told you that you’re feeding an AI algorithm your face to train it. Your before and after photos are being harvested into datasets, and by posting them side-by-side, it’s less work to correlate them.
One of the challenges AI faces is understanding how faces change over time, including wrinkles, weight changes, hairlines, and skin texture. Companies would normally have to pay a lot of money to collect long-term data like this, but people are handing it over for free.
As a data engineering and AI/ML consulting firm in Richmond Virginia, MPP Insights sees this from a unique perspective. This topic is multi-layered - from data privacy to the ‘how’ the data is actually processed.
We asked a couple of questions to our U.S. Managing Director, Peter Bilzerian, to understand what’s actually happening behind the curtain and if this trend is actually as scary as it sounds.

How Exactly Can Old Photos be Used to Train AI Models?

Peter:
"AI models learn by correlating patterns across massive datasets.
When people upload 2016 photos alongside their current photos, they're creating what we call 'temporal paired data' - the same person, location, or object but at different points in time. This can train a model to understand the concept of time and change better.

Some examples of this are:
  • Aging and facial recognition across time - how does the phenotype change? Could this predict health issues?
  • Fashion and style evolution - how do trends change over 10 years
  • Environmental changes - have the cities or buildings changed?

From a data engineering perspective, these photos become labeled training examples. The metadata alone like the dates, locations and tagged people provides context to the model. It’s scary to think of the multitude of ways the models are trained on your simple transformation post.
In particular to this trend, I’m not concerned about privacy invasion since users aren’t directly putting them into ChatGPT or an LLM.

It’s just another trend to show your glow-up compared to other recent trends like putting actual images into ChatGPT or an LLM to generate an image of yourself looking like Studio Ghibli."

Why May Old Photos Be Particularly Useful for Training AI?

Peter:
“When ChatGPT came out in 2022 - it was trained from that point on using inputs from users (mostly recent photos) - so nearly 3.5 years of data is very recent, but older photos can help make the models more robust.
Historical photos teach models to recognize and adapt to different visual contexts across time periods.
My favorite optimal outcome is medical diagnostics and health applications of this.
Medical diagnostics can detect early disease progression from facial and body changes. We’re working on a project right now for a start-up where they’re implementing this for conditions such as Thyroid disorders (facial puffiness, eye changes), heart disease (high cholesterol causes eye changes), early-stage dementia (facial symmetry shifts).
As a two-time cancer survivor myself and Thyroid cancer survivor in particular, I’ve seen firsthand how early detection saves lives.
There is a silver lining to this trend, but only if there’s data privacy policies that are followed such as HIPPA. ”

Risk or Opportunity?

Sharing old photos from 2016 gives AI the ability to spot patterns and learn changes over time. It doesn’t stop there. The hidden metadata, such as location, also adds value.
Here comes an important question; should we get worried and stop posting old photos? As Peter explained, we can say no, because your picture is not being uploaded into a model interface. However, many vision models are trained to use public data, so it’s now your choice to upload your old photos publicly or not.
But let’s look at the bright side, these long-term facial changes can help models understand patterns that may be useful in medical applications and support early disease diagnosis. Considering all this, we can say technology itself is neutral, what matters is who controls the data and what they do with it.
]]> From the People Who Actually Build AI: Please Stop Letting it Write your Emails https://mpp-insights.com/blog/ai-generated-content-problems Mon, 16 Mar 2026 16:00:00 +0300 Sahar Fallah AI & Machine Learning AI is a powerful tool, but people sometimes use it to avoid thinking. AI-generated content has no personality, so never use it to create materials that go directly to other humans. Read this blog to learn how to use AI effectively.

From the People Who Actually Build AI: Please Stop Letting it Write your Emails

02.03.2026
AI tools are a part of our lives, many people open a chatbot before they open a blank document. It’s more convenient for them to write their emails, proposals, LinkedIn messages, and even quick replies with a prompt.
These tools save time and help when someone doesn’t know how to start. But something else is happening too. More messages now sound the same, with a similar structure, tone, and even wording! It’s cheapening communication.
Many people warn about writing with AI, but it is more interesting when the warning comes from someone who actually works on AI.
Peter Bilzerian, U.S. Managing Director at MPP Insights, builds AI systems and works with these tools every day. He sees this change from the inside. For him, there is a simple rule when it comes to AI-written messages.
“This is the Wild West of AI - and the etiquette hasn’t caught up when it comes to new communication styles. My golden rule is - if it’s to another person directly (text, email), it should be your own voice.”

People Notice AI-Generated Content

AI-generated content stands out, and it’s easy for people to tell when a proposal or email was mostly written by an AI tool.
“I use this insult a ton - mostly calling out lazy AI ‘slop’ that was never proofread and not thorough. If you send me a proposal that sounds like a generic template with your company name on the banner, damn right I'm asking 'Did ChatGPT write that?' Because you just wasted my time with something you didn't even care enough to prepare yourself. Those vendors become blacklisted from me.”
The message is simple, we need to put real effort into our work.

While we're on the topic of AI, there's a trend on social media where people post “2016 vs. now” photos of themselves. What many don’t realize is that by doing this, you're actually giving AI models free data on how human faces age over time.

Want to learn more? Take a look at the interview we had with Peter Bilzerian, where we talk about it in more detail and explain how these photos are used to train AI models .

Using AI vs Letting AI Think For You

We’re not anti-AI, we also use AI tools because these tools are useful, and they are here for us to use. Peter even admits he uses them himself, but there is a line.
“Using AI as a tool versus outsourcing your thinking are completely different things. I use AI tools every single day in my work - for data analysis, digital archives, brainstorming solutions to engineering problems. AI is a productivity machine”

Why AI Writing Often Sounds The Same

AI-generated content often follows the same patterns. It is safe, generic, and it sounds corporate.
Peter explains why:
“For years before ChatGPT was even a thought, the same soulless corporate jargon and buzzwords lived in business communications books - but unfortunately it’s now virtually everywhere.
2026 is going to be the year of ‘anti-AI’ marketing. Whenever I consult an LLM about a genius creative marketing idea, it applauds me as if I’m a god. Why? Because they have no creativity. Everything produced is designed to be average and not be provocative.”

What Actually Makes Writing Sound Human?

“How I avoid the AI accusations? I only lead with stories that I can tell from lived experience. My experience as a two-time cancer survivor and the lessons I’ve learned cannot be replicated by an AI model. When I’m writing, I’ve always put soul into my communications with distinct optimistic energy. The key is consistently writing in your own style - and if you really want to be sneaky, train the models on your own writing, and then proofread any outcomes.”

Put More You Into It

Peter gives a clear rule:
“If you’re worried your writing sounds like ChatGPT wrote it, you probably need to put more YOU into it, not less AI-assistance.”
AI can help, but keep in mind that your voice should always be there. At the end of the day, people want to hear from you, not a tool.
]]> Modern Reporting and Analytics with MPP BI: A Cognos Alternative https://mpp-insights.com/blog/mpp-bi-cognos-alternative Mon, 02 Mar 2026 14:09:00 +0300 Sahar Fallah Business Intelligence Cognos analytics is a legacy reporting platform, but many companies prefer a modern solution. MPP BI is a more affordable alternative to Cognos that delivers customizable reports and interactive dashboards.

Modern Reporting and Analytics with MPP BI: A Cognos Alternative

02.03.2026
Companies deal with lots of data, and this data can be used to create reports. Reports can have a big impact, but collecting the data and generating reports doesn’t happen instantly. Big companies have multiple sources, and gathering all this information is a big challenge. That’s where Cognos Reporting comes in to help teams turn their data into reports and dashboards.
Cognos has been in the market for many years, and many businesses see it as an older system that can be complex and expensive.
MPP BI is also a Business Intelligence platform that creates analytical reports for enterprise-level companies. This platform can be a Cognos alternative for companies that want a more modern solution and lower costs without sacrificing value.
This article provides an overview of the MPP analytical platform and explains how it can serve as a replacement for IBM Cognos Analytics.

What is Cognos Reporting and How It Works?

Cognos Reporting is a software tool made by IBM. It pulls data from different sources, organizes it into tables, charts, and dashboards, and lets companies analyze this information to make better decisions. It’s part of a larger package called IBM Cognos Analytics, which is designed for analyzing business data.

An Introduction to MPP BI as a Cognos Alternative

MPP BI is a platform that visualizes data and creates analytical reports. It lets you see your business in ways that help you understand what’s happening.
But where does this data come from, and how does it get prepared for reporting? This is where MPP ETL comes in as part of the platform. As the name suggests, it Extracts, Transforms, and Loads the data so MPP BI can use it for analysis and dashboards.

How Does MPP BI Reporting Work?

1. Connect to Data

MPP ETL works with different kinds of data such as sales numbers, inventory and records. The data is usually stored in systems or databases, so MPP ETL reads it from where it lives.
One feature that makes MPP BI stand out is that it can work with many different data sources. It is flexible and can connect directly to databases using JDBC (Java Database Connectivity), or connect to other systems through APIs.

2. Model the Data

Before reporting starts, someone needs to set up a data model. We make this easy by letting users drag and drop files in a user-friendly interface. Basically, this is a way to tell the system what data exists and how different parts relate to each other.

3. Create Customizable Reports

Users can generate instant reports in different formats that shows:
  • Lists of numbers (like Excel tables)
  • Summaries (like total sales per month)
  • Graphs and charts (like bar charts or pie charts)
The format of the generated reports is flexible based on your company’s needs. You can export them as Excel, PDF, Word documents, or even presentations in a matter of seconds.

4. Data Visualization

Now you have clean data from different sources, ready to analyze. MPP BI helps you make this analysis faster and more efficient. The data can be displayed in different formats, such as charts, maps, tables, and text-based analytics. This allows users to view insights at a glance and create analytical reports easily.
Why does this matter? As a decision-maker, you need insights right in front of you, so you can quickly review the information and make informed decisions.

Benefits and Features of MPP BI Platform

More Affordable Alternative to Cognos

MPP BI, with integrated MPP ETL, offers two types of subscriptions: read-only users and admin users. The pricing differs for each type, which helps make the platform more affordable for customers.
In general, the monthly subscription rate can be up to 60% more affordable than IBM Cognos reporting, depending on the type of user.

Perpetual License

MPP BI also offers a perpetual license for both read-only and admin users. This option is designed for customers who want to avoid annual subscription costs, and it can be more cost-effective in the long term.

Modern and Flexible

MPP BI has a modern and easy-to-use interface that can be customized to fit your company’s needs. It works well with other tools and systems that your company uses. This is also an ideal solution for companies that have growing amounts of data, because it is fast and scalable.

Cloud and On-Premise Options

MPP BI is available both as a cloud solution and as an on-premise deployment. Companies can choose the option that fits their infrastructure and security needs.
With the on-premise version, you fully own and control your data on your own servers. With the cloud version, you can access your data easily without managing servers yourself.

Customizable Visuals

The BI platform for reporting and analytics is interactive and customizable. Even non-technical users can explore it and quickly understand how the interface works.
We also offer the option to use your company’s brand colors and logo in the interface, because we believe every company should have a system that reflects its own identity and feels familiar to its team.

Retail Case Study Example

A retail company had data coming from three sources:
  • Store sales
  • Online orders
  • Warehouse inventory
We used MPP ETL to collect the data, clean it, and prepare it for reporting. With MPP BI, the sales manager opens the dashboard, and on the screen sees:
  • A chart showing total sales this month
  • A table listing the top-selling products
  • A map showing which cities have the highest sales
  • A simple number showing how much inventory is left
Instead of opening different systems or asking IT for reports, the manager can see everything in one place and make decisions quickly. For example, if one product is selling fast but inventory is low, they can reorder it immediately.
This is the same level of reporting many companies expect from Cognos, but at a lower cost and with more flexibility.

Why Choose Our Platform Over Cognos

For businesses looking for a modern and affordable Cognos alternative, MPP BI is worth considering. It gives you the same kind of reporting and dashboards, but faster, at lower cost, and with an easy-to-use interface.
We are a data engineering and AI/ML consulting firm based in Richmond, Virginia. We recommend a free consultation call to talk about your needs and show you sample reports so you can see how MPP BI works in practice.
]]> What Is Business Intelligence (BI)? A Simple and Complete Guide https://mpp-insights.com/blog/what-is-business-intelligence Fri, 13 Mar 2026 12:25:00 +0300 Sahar Fallah Business Intelligence Learn what Business Intelligence is and why it matters, with a healthcare case study showing how our BI dashboards help businesses make better decisions.

What Is Business Intelligence (BI)? A Simple and Complete Guide

02.03.2026
Numbers do magic in a business, only if you’re able to understand them. Companies have tons of numbers from sales, customer info, website visits, inventory, and much more. This raw data is meaningless unless it’s cleaned, organized, and finally, visualized!
Business Intelligence (BI) at its core is about visualizing data in a way that humans can understand the impact and make informed decisions. Its name is simply - business intelligence, for a reason.
This article is an introduction to Business Intelligence and why it matters for your business.
Our team believes that data can either save you money or make more money - or in more business jargon, creating shareholder value is done through optimizing businesses or adding revenue streams. The benefits of having real business intelligence are game-changing. Unfortunately most businesses struggle even though they have access to the best tools for business intelligence because they either don’t have time, they aren’t technical, or they don’t believe there is an ROI in BI.
Because we love coffee, and I’m sure you do too, we’ll use a vivid example of implementing BI in a coffee shop so you can understand how it works step by step. We’ll also share one of our healthcare cases in more detail. There are many more cases, and you can explore them as well.

What Is Business Intelligence (BI) In Simple Terms

BI is basically the process of taking all the data a company has and turning it into information that helps people make better decisions. Every day, you create millions of data points by simply living and going about your day, but turning that data into information is the journey of data engineering.
In a coffee shop, they record every sale, what drink was bought, at what time, and by whom. BI can take all this info and show:
  • Which drinks sell the most
  • What time of day is busiest
With this information, the owner can decide to make more of the popular drinks during peak hours.
In simple terms, we answer the question 'What is Business Intelligence?' briefly: it's all about turning confusing numbers into useful insights.

How Business Intelligence Works

BI works in a few simple steps, even though behind the scenes it can get technical. We’ll keep it very simple:

Collecting Data

First, a company gathers data from all sorts of places. At this stage the data is scattered in multiple places.
The coffee shop collects sales from the cash register, website orders, customer feedback forms, and inventory records. If the coffee shop has several branches, each branch also produces its own data every day.

Moving and Preparing Data (ETL)

Before the data can be analyzed, it usually needs to be organized. This process is called ETL, which stands for Extract, Transform, and Load.
In simple terms, it means taking data from different systems, cleaning it, and putting it in one place so it can be used.
The coffee shop has sales data in the cash register and on the website. ETL collects this data, organizes it, and prepares it for analysis.

Storing Data

After the data is prepared, it is stored in a database or a data warehouse. All this data needs to be in one place so it can be used and analyzed. This system can be on a company server, which is called on-premise, or in the cloud.

Processing and Analyzing Data

Now the clean and organized data is ready to be processed and analyzed. At this stage, the user chooses what they want to explore by combining or comparing data, such as filters, time ranges, products, locations, and more. So the BI system doesn’t give fixed answers. The owner asks questions to the data.
By combining different pieces of data, the user can start to see patterns and make better decisions.
The coffee shop owner might look at sales by drink, by time of day, or by season. The system might show that iced coffee sells more in summer, or that muffins sell more on weekends.

Presenting Insights

The results are shown in a simple way using dashboards, graphs, or reports. These visuals help people understand the data quickly.
The coffee shop can see on the dashboard which drink sells best each day, without having to count everything manually. If they want more details, they can filter the dashboard by week, month, or by a specific branch.

Making Decisions

Now that every insight is in front of you, it’s time to take action. The patterns and trends help users make better and faster decisions. This can improve sales and customer satisfaction. Sometimes, putting time and money into the wrong product or approach can cost a lot with little return.
The coffee shop might start a weekend muffin special because the dashboard shows muffins sell more on weekends. They might also stock extra iced coffee in summer when sales are highest.

How BI analyzes data to find patterns

What you see behind those colorful dashboards is thousands of numbers that are turned into clear answers. To do this, BI performs a few actions on the data. When the data is combined, patterns start to appear.
Counting: The system counts how many times something happened. (Example. How many lattes were sold today)
Adding and totaling: The system adds numbers together to show totals. (Example. Total revenue from iced coffee)
Grouping data: The system groups data into categories. (Example. Sales by drink type)
Comparing data: The system compares numbers to see differences. (Example. Morning sales vs afternoon sales)
Finding trends and forecasting: The system looks at how numbers change over days, months, or seasons to identify patterns. It can also predict what might happen next using trend analysis and forecasting. (Example: Iced coffee sales increase in summer, so the system can help anticipate future demand.)

How BI Presents Insights

After the data is analyzed, the results need to be easy to read. If people cannot understand the results quickly, the analysis isn’t very useful.
At this stage, the BI process is complete. Data is collected, stored, analyzed, shown in dashboards or reports, and then used to make decisions.

Dashboards in BI

A dashboard is a visual screen that shows the most important information in one place. It usually includes charts, graphs, and simple numbers.
BI dashboards are interactive. Users can filter data, change time ranges, or focus on specific products or locations. Modern BI tools like MPP BI are easy to use, so even non-technical users can work with them.

Reports in BI

A report gives more detailed information. It helps users explore the numbers more closely. The coffee shop report might show sales by day and by drink type.
One BI tool people often use is IBM Cognos. We’ve compared it with MPP BI, a modern and more affordable Cognos alternative. Check out our full comparison to see how MPP BI dashboards and reporting can be customized to fit your business needs.

Benefits of Business Intelligence

BI helps businesses understand what is working, what is not working, and what they should do next. Here are some simple benefits to find out the importance of business intelligence:

Better decisions

BI turns numbers into clear information. This helps people make decisions based on facts, and not guesses.

Saves time

Without BI, people often search through spreadsheets or count numbers manually. BI shows the information quickly in one place.
Instead of checking hundreds of receipts, the owner opens the dashboard and immediately sees total sales for the day.

Spot trends and opportunities

BI helps businesses notice patterns over time. The dashboard shows that iced coffee sales increase every summer. The shop can prepare promotions and stock more ingredients before the season starts.

Identify problems early

BI can also show when something is going wrong. When customer feedback shows slower service on weekends, the owner can schedule more staff during busy hours.

Improve profit

When a business clearly sees what sells well and what doesn’t, it can focus on the right products.
Data engineering companies like us can use BI to build solutions for different industries. For example, we have call analytics for call centers. This solution analyzes customer conversations and presents the data in dashboards to help teams understand customer needs and common problems. Every business that has calls from customers needs BI solutions to understand its customers. Happy customers make a business more successful, and that's the secret to growth.

Impressive BI Numbers for 2025–2026

Now that we understand the benefits of BI, let's look at some numbers from sources like Hydrogen BI for 2025–2026. These stats show how BI can really change a business.

Types of Business Intelligence

Descriptive BI – what happened

This type tells you what has already happened using historical data, it shows you the past. It answers questions like which products sold most or which periods were busiest.

Diagnostic BI – why it happened

This type looks at data to understand the reasons behind past results. It helps find patterns, trends, and causes so you know why something happened.

Predictive BI – what might happen

This type uses past data and trends to forecast future outcomes. It helps you see what’s likely to happen next so you can plan ahead.

Prescriptive BI – what to do next

Prescriptive BI recommends actions based on data and predictions. It shows the best steps to take to achieve your goals or avoid problems.
BI might suggest adding a weekend muffin promotion because historical data shows that promotions increase sales by 20%.

We’d love to hear about your business and what matters most to you. We can walk you through dashboards and reports made for your needs and show how data can make decisions easier and faster.

Let’s Connect

Healthcare BI Case Study

To make BI easier to understand, we used the coffee shop example throughout the article. Now we’ll share a real case from the healthcare industry. We have many more BI case studies in different industries, and you can explore them as well.

Challenge

A healthcare provider had large amounts of data from different systems. Patient records, insurance claims, and hospital departments all produced data every day. But this data was scattered and hard to analyze.
The team could not easily see how many patients they treated, which departments were busiest, or which treatments created the highest costs. It was also difficult to understand patient trends over time or compare inpatient and outpatient activity. As a result, many decisions were based on partial information.

Solution

A BI system was introduced to bring all healthcare data into one place. Data from patient records, insurance systems, and hospital departments was collected and organized.
Dashboards and reports were created so the healthcare team could easily explore the data. They could see how many patients were treated, compare inpatient and outpatient activity, and follow patient trends over time.
The system also helped the team analyze treatment costs and insurance coverage. Management could see how costs changed by insurance provider, treatment type, or disease.
The BI system also showed operational data, such as where patients were coming from, which medical divisions treated the most patients, and the average length of hospital stays.

Result

With all the data in one place, the healthcare provider could see patterns more clearly. They could track patient activity and understand treatment costs.
The dashboards helped the team see how many patients were treated, compare departments, and follow changes over time. They could also see which diseases created higher costs and how insurance coverage affected total expenses.
With this information, the hospital could make better decisions, adjust resources between departments, and plan their work more effectively. BI helped turn scattered healthcare data into clear and useful information.

How We Help Businesses with BI

Searching for business decision support is a tedious process and most companies don’t know where to start, which tools to try, or who to trust. We’ve met executives who just picked a big company and paid hundreds of thousands of dollars for minimal results.
Plenty of companies in the data consulting industry are only selling a single software solution for a singular problem - rather than assessing and improving the entire data pipeline and prescribing the correct solution. Business Intelligence is only the visualization layer, and if there’s garbage in, there’s garbage out too - despite how nicely designed the dashboards are.
MPP Insights is proud to be the holistic consulting firm that specializes in every aspect of data. Our teams will audit your infrastructure and identify weaknesses or single points of failure so we can confidently build solutions that will improve your business.
MPP Insights is a leading data engineering and AI/ML consulting firm based in Richmond, Virginia, with decades of experience helping firms across all industries. We help companies collect data from different places, organize it, and turn it into dashboards and reports that are easy to read. Each dashboard is customized to show exactly what you want to see. Our solution can run on-premise, integrate with your existing systems, and is more affordable than traditional options.
We start by understanding your goals and challenges, then help find the right solutions and make decisions based on the insights.
Want to see how it works? Schedule a call and we can explore our dashboards and reporting system. You’ll see how simple it is to turn data into decisions.
]]> How MPP Call Analytics Helps You Understand Customers and Improve Service https://mpp-insights.com/blog/call-analytics-for-call-centers Tue, 24 Mar 2026 16:00:00 +0300 Sahar Fallah AI & Machine Learning Call analytics helps understanding customer calls. They show how customers feel, why they call, and if their issues are solved. These insights help fix recurring problems, improve service, and drive business growth.

How MPP Call Analytics Helps You Understand Customers and Improve Service

02.03.2026
Most businesses, especially B2C ones, get calls from customers for different reasons. The call center answers the calls, and these calls are recorded for supervision. The conversations can give business owners insights that help improve the business. But who has the time to relisten to all those calls? Even if they do, how can they clean the unstructured data for analysis? That is a headache. But why do it manually when LLMs can make this much easier?
It all started with a Hackathon we held in Yerevan, where our R&D office is located. We got audio files from Globbing’s call center. We needed to turn the files into text so participants could work on the data and find customer feelings, problems, and ways to improve service. A data engineering company like ours hates manual work, so we thought, why not develop a tool that does it all from start to finish?
Now this idea has grown into MPP LLM Call Analytics, a tool for call centers. It converts speech to text, analyzes calls to track customer feelings and resolutions, and shows the insights in an easy-to-use dashboard.
Many layers work in the background to make this happen. Here, we’ll explain how MPP LLM Call Center Analytics works. If you have lots of customer complaints on the phone, you might need to understand them before they hang up!

What is Call Analytics for Call Centers?

Business growth comes from customer satisfaction. A lot of useful information is hidden in the calls customers make. Call center analytics is a way to understand these calls, interpret them, and use the insights to grow the business.
It helps businesses see patterns, find problems, and improve service without listening to every call.
  • How the customer feels: angry, neutral, or annoyed
  • Why the customer called: information, complaint, account check, pricing, or billing
  • If the problem was solved: resolved, partly solved, not solved, or escalated
  • How the call went: steady, got worse, or got better

How Technology Makes Call Center Analytics Work

ASR (Automatic Speech Recognition)

The system listens to the audio and breaks it into small pieces to understand the speech. With language models, it can guess the words in a sentence. For example, if someone says “I want to pay my bill,” it knows “bill” fits at the end.
ASR can handle different accents and work with background noise. Now the transcript of the call is ready for the next layer!

LLM / AI Models

LLM reads the transcript and understands what is going on in the call. It does this using models that are trained on a large amount of text and conversations. Because of this training, it understands how people speak, what words mean, and how sentences are used in real situations. So now it can read a conversation and understand it like a human, but much faster.
It can find out how customers feel, why they called, and what the call outcome was.

ETL (Extract, Transform, Load)

After the AI reads the call, the result is still messy text. ETL takes this messy text and turns it into clean, organized data.
  • Extract: It takes useful results from the AI, like “angry”, “billing issue”, or “not resolved”.
  • Transform: It cleans the data and puts similar sentences into one group. For example, “I am upset” and “this is frustrating” both become “angry”.
  • Load: It saves the clean data in a table. Each call is one row, with things like feeling, reason, and result.

BI Dashboard

This is the part where all the cleaned data is shown in charts, graphs, and tables. You can see exactly what is happening in your call center.
For example, you can see:
  • How many problems were solved, partly solved, or escalated
  • How long calls usually last
You can choose what to look at and focus on what matters for your business. You don’t need to read every transcript. The dashboard shows patterns, common problems, and agent performance at a glance.
If you want to learn more about BI, read our article “What is Business Intelligence” to gain more insights.

How Does MPP Call Analytics Stand Out from Other Tools?

Interactive Dashboards

MPP BI dashboards are modern and interactive. The dashboards show call sentiment analysis and call resolution tracking, so you can see how customers feel and if their issues are solved.
  • Filter what you want: Look at calls by agent, reason, customer feeling, or resolution.
  • See trends as they happen: Spot problems and patterns in real time.
  • AI helps you: The system highlights frequent complaints or unresolved calls.

Brand and UI Customization

The dashboards can match your company’s colors and branding. We can also make the layout and features fit your team’s needs.

Chatbot Integration

For those who need it, we can add a chatbot to the call center analytics system. You can ask questions directly, like “How many unresolved calls did we have last week?” The chatbot gives answers instantly in plain language.

Actionable Data

Decision-makers need insights to improve the process. Charts are not just there to show numbers from the calls. Without clear actions, the data can be useless in the long run. For this reason, we focus on providing actionable data.
For example, your call center gets many calls about delayed deliveries. Without insights, you might not notice the same issue keeps happening. With call data analysis, you can see the trend, find the root cause, and take action to fix it before more customers are affected.

Flexible Deployment

MPP Call Analytics can work on your own servers or in the cloud.
  • On your own servers (on-premise): The system runs on your company’s computers. This is good if you care about keeping your data private. It can be more secure, but it may cost more because you need your own hardware and IT support.
  • In the cloud: The system runs on the internet using remote servers. You can access it from anywhere and don’t need to manage computers. It can be faster to start and cheaper for small teams.

Extra MPP Advantages

MPP Call data analysis has extra features that make it easy to use:
  • It can track important KPIs in real time and generate automated reports.
  • It can also integrate easily into any environment.
  • The different services talk to each other through open APIs, and the source code is available per license, so connecting it with your own systems is simple.

Does Your Business Need Customer Call Insights?

If you have a call center and get many customer calls, the insights from those calls can help your business. Ask yourself these questions:
  • Do you want to know why customers call and how they feel?
  • Do you want to find recurring issues to see common problems customers have?
  • Do you want to spot patterns early and prevent complaints?
  • Do you want to check how well agents follow procedures and handle calls?
If your answer is “yes” to any of these, MPP Call Analytics can help.

We’d love to hear about your business and what matters most to you. We can walk you through dashboards and reports made for your needs and show how data can make decisions easier and faster.

Let’s Connect

Making the Most of Call Data

We have a keen eye for data and think about how to make the most of it. That’s why we even look at unstructured data, like voice conversations. That’s a lot of data, and a lot of opportunity for business growth. Why leave those calls without insights?
Turning speech into text helps us use technology and do amazing things with the data we get. The technology is out there, and businesses should find ways to use it for growth.
And because we love to think outside the box, we can add a chatbot for interactive querying of call data. What if someone wants to ask questions and get insights instantly? That’s exactly what MPP LLM Call Analytics makes possible.
We’re a data and AI company based in Richmond, Virginia. Many companies only sell one software for one problem, but we look at the whole data pipeline and make sure it works right.
Our team would be happy to have a call, show you how the dashboards work, and learn about your business goals.
]]> Throwback Thursday: First Data Engineering & LLM Hackathon in Armenia https://mpp-insights.com/blog/llm-hackathon-armenia-2025 Thu, 02 Apr 2026 10:27:00 +0300 Sahar Fallah Data Engineering MPP Insights hosted the first LLM and data engineering hackathon in Yerevan, Armenia. Read more to see what the hackathon was about.

Throwback Thursday: First Data Engineering & LLM Hackathon in Armenia

02.03.2026
On October 18, 2025, we hosted the LLM 4 ETL Hackathon at the American University of Armenia. Participants in the hackathon had the chance to work with real data from a local brand. This gave them hands-on experience with data analysis and using LLMs for ETL and ELT. They did all of this using our MPP ETL tool.
We are a data engineering and AI/ML consulting firm based in Richmond, and our R&D office is in Yerevan, so it was exciting to bring this kind of event to our local community. It was Armenia’s first hackathon focused on LLMs and data engineering, and we were happy to have eLogiFest and Globbing as our sponsors.
Here’s a throwback to that day, to remind us how inspiring it was to see so many people learning, experimenting, and having fun at the same time.

What The Hackathon Was About

Our team wanted this hackathon to be a different kind of event in Yerevan, so we started by bringing real data from a local company. We partnered with Globbing, a well-known e-commerce and logistics company in Armenia, and got anonymized data from their call center.
During the hackathon teams worked with these datasets using our MPP ETL platform with its LLM extension.
Our MPP ETL tool makes complex data pipelines simple and helps teams analyze data easily. It can collect data, clean it, and organize it so teams can use it for analysis. With the LLM plugin, participants could also use simple prompts to go through the data, ask questions, and find useful insights.

Managing Tokens and Choosing Models

To add more excitement and a sense of competition, we gave every team the same token budget to work with, and displayed current budgets spent/left for all teams on a wide screen. Teams had to think carefully about how they used their tokens while building their solutions.
Participants could use different Large Language Models, ranging from best-in-class OpenAI to less expensive DeepSeek - 30 in total. But they had to be careful. Each team had a limited number of tokens, and the choice of model also affected the cost. Expensive models could use the budget faster, so teams had to ask questions carefully and avoid wasting tokens.

How Teams Worked With the Data

  • Teams used LLMs to go through call center conversations to understand how customers felt, what they needed, and whether the customer issues were actually solved
  • They pulled out the key points, and suggested ways to improve call-center operations
  • They shared their thoughts about the tool and how it could be improved
This idea is now part of our MPP LLM Call Analytics, which helps call centers understand customer feelings and problems. If you want to see how it works behind the scenes, check out our blog.

How We Handled Judging

The hackathon had a jury of IT experts to make sure judging was fair. Each team was scored based on clear criteria, and there was a scoreboard that helped keep everything organized. Judging happened in two steps of automated check and manual check.
The automated check was for giving quick feedback, but the jury’s review was the main scoring. Teams’ pipelines were tested with new data. This showed:
  • How many tokens they used;
  • Which models they picked;
  • How many labels were in their ETL pipelines.
The manual review was the jury’s judgment based on five main things:
  • Token use: how much each team spent and the models they picked;
  • Implementation: how complete and well-structured the ETL pipelines were;
  • Business value: how useful the solution was for solving data problems;
  • Culture of work: Clarity and neatness of the work and whether it was completed on time;
  • Overall approach: a mix of creativity and efficiency.
Teams could see their progress on the scoreboard. This made the event more fun and helped the jury focus on careful scoring.

Hackathon Takeaways

Teams came up with lots of creative ideas and solutions during the hackathon. It was amazing to see participants learning new skills, trying new ideas, and working together in new ways.
Everyone left with something valuable from the day. Sponsors and supporters got a first look at future data analytics talent, and students gained practical experience, networking opportunities, and even chances for internships.
Participants also received a certificate of participation in the hackathon “LLM 4 ETL”.

Meet our Winners

Meet Our Sponsors and Jury

We couldn’t have done this without our sponsors and expert jury. A big thanks to Globbing and eLogiFest for supporting the event.
Our jury helped guide the teams and made sure everything was fair:

Why the LLM 4 ETL Hackathon Mattered

The Hackathon was a big step for showing Armenia’s growing strength in data engineering. We were the first company to host this kind of hackathon. We made it modern with our ETL tool, LLM extensions, and a smart judging system. Working with well-known brands like Globbing and eLogiFest made it even more special.
Who knows what other events like this we’ll have next? We’re always ready to be the bridge between technology and local talent.
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