EAII Study Guide for the CEO

The 5-minute read

This is the whole story. If you only have time for one read of this guide, this is it. Every other tab is layered detail under what's here.

1.1 What we are building

We are building EAII, which stands for Emotional Artificial Intelligence Infrastructure. Here is the simplest way to think about it:

Today's AI chatbots can hold conversations, but they can't actually understand how the person they're talking to is feeling. They might sound empathetic in any one response, but they have no way of knowing whether the user is calm or frustrated, engaged or checking out, or building toward a crisis. We solve that.

Our product is a kind of "second pair of eyes" we install inside other companies' AI systems. We tell their AI: "this user is frustrated, their intensity is high, they're about to disengage, they need a supportive response not an informational one." The AI can then adjust its reply based on what we tell it.

We are not building a chatbot. We are building the layer underneath every AI that makes those AIs emotionally intelligent.

1.2 Why it matters

Companies using AI to talk to humans (call centers, healthcare, education, customer support, mental health apps, dating apps) lose customers and create bad experiences when their AI gets the emotional read wrong. Right now, every conversational AI in the world has this problem. Nobody has solved it as infrastructure.

We are positioning to be the standard layer that every conversational AI eventually plugs into, the same way every website needs payments processing (Stripe) or every business needs cloud computing (AWS). As AI gets deployed everywhere, we ride that wave.

1.3 The simplest one-sentence pitch

Modern AI is emotionally illiterate. We are the layer underneath that fixes it.

1.4 How we are building it (two phases)

Phase 1 (right now, 5 months)

The platform around the brain. Pivot-al AI is building this for us. The website, the system other companies plug into, the back-office tools, the safety net, and a temporary placeholder brain so we have something to demo while we build the real one.

Phase 2 (next, 12+ months)

The actual brain. Matt (our CTO) builds this. The proprietary technology that makes our product valuable. Replaces the placeholder brain.

1.5 Why split it that way?

Two reasons:

Speed. We can put a working investor demo in front of people in 5 months instead of 18.
Risk reduction. We lock the platform first, then plug the real brain into it later. Nothing has to be redesigned when the real brain comes online.

The placeholder brain in Phase 1 is intentionally simple and disposable. We replace it with the real one in Phase 2.

1.6 The current state, in one paragraph

We are about 2 months into Phase 1. Pivot-al has the contract, has the team, has set up the cloud infrastructure on Amazon Web Services, has the connection to the temporary brain (Microsoft's Azure OpenAI), and is currently building the foundation systems plus our investor demo. The investor demo is targeted to be ready by the end of June. After Phase 1 finishes, Matt builds the real emotional intelligence engine over Phase 2.

1.7 The team you talk about

Name	Role	Background
Patrick Brunet	Founder, co-founder, strategy role	The original idea is his.
Michelle Yates (you)	CEO	Insurance background. Lead growth and go-to-market.
Matt Elenniss	CTO	Came from running an AI hedge fund. Leads all technical work.
Dustin Borklund	COO	Operations, compliance, key relationships.
Pivot-al AI	Phase 1 development partner	Israeli software development firm. Their CEO is David Finkelshteyn.

That's the whole picture in 5 minutes

When you're ready to go deeper, Tab 2 has more detail on what's currently being built. Tab 3 explains each of the 12 pieces in plain language. Tab 4 has the investor talking points. Tab 5 has the vocabulary glossary.

2.1 The "platform around the brain" idea

If our product were a car, here's what we're doing right now:

Phase 1 = building the chassis, body, dashboard, steering wheel, seats, doors, electrical wiring. Everything needed for a working car except the actual engine. We put in a temporary engine (a small electric motor) that lets the car drive slowly so we can demo it. Pivot-al is doing this.
Phase 2 = building the actual high-performance engine. We swap it in once Phase 1 is done. Matt does this.

Phase 1 is about 80% of the system by piece count, but the value is in Phase 2. Phase 1 is intentionally generic infrastructure. Phase 2 is the proprietary technology that makes our product worth what it's worth.

2.2 What Pivot-al is building (the high-level list)

Pivot is building 12 specific things. Here's the full list with a one-sentence description for each. The detailed plain-English descriptions are in Tab 3.

Investor Demo App. A website where investors can type a message and watch our product analyze their emotional state in real time.
Internal Tools. A separate, private website only our team uses. Five sub-tools for managing and improving the product.
API Service. The technical "phone line" other companies' software uses to talk to ours.
Data Pipeline. A standardized way to record everything that happens, like an audit log.
Placeholder Brain (Engine v0). The temporary version of our AI that we replace in Phase 2.
Safety Pipeline. An automatic safety net for crisis-related inputs.
Infrastructure. The actual computer servers and operational tools running our product.
Developer Interface. Documentation and example code that teaches other companies how to plug into our API.
Documentation and Handover. All the operational documents our team needs once Pivot finishes.
UI Design. The visual design of the product (colors, fonts, layouts).
Evaluation Framework. A test that proves our product works better than simpler alternatives.
External AI Scoring. An additional capability that lets us evaluate the quality of OTHER AIs' responses, not just user emotional state.

2.3 The 14-week timeline

The contract is 5 months total, with about 14 weeks of intensive build:

Phase	Weeks	What's happening
Architecture lock	Week 1	All major design decisions get nailed down
Foundation	Weeks 1 to 6	The basic system, temporary brain, safety net, demo
Internal tools	Weeks 4 to 9	The admin tools our team will use
Hardening	Weeks 10 to 12	Test everything, fix bugs
Handover	Weeks 9 to 14	Documentation, training, transition

We are about 2 months in right now. Roughly between "foundation" and "internal tools."

2.4 What the Pivot contract covers (and doesn't)

Phase 1 is intentionally limited

The contract covers Phase 1 only. It explicitly does NOT cover:

Phase 2 (the real emotional intelligence brain)
Cross-session memory (remembering users across separate conversations)
Personalization (adapting to individual users)
Adaptive learning (the system getting smarter over time)

All of those are Phase 2 features. Phase 1 is a focused, single-conversation, prototype-grade system intended for investor demos and early customer pilots. It is not a production system yet.

2.5 What success looks like at the end of Phase 1

Five things:

A working investor demo on a public website. Patrick can take an investor through a live conversation and they can see the emotional analysis update in real time.
A working API that other companies could (in theory) start integrating with.
Internal tools our team uses to debug, measure, label, and experiment.
An evaluation summary showing our structured approach beats simple sentiment analysis. This becomes part of investor materials and customer pitches.
A complete handover package so we can run the system without Pivot.

Once we have those, Phase 2 starts. Matt takes over the technical leadership of the engine work, and we start raising more capital against a working product.

2.6 What this all costs

The Phase 1 contract with Pivot is around $100K total. Patrick has been paying it monthly out of his own funding. The pre-seed round we're raising will fund Phase 2 (Matt's engine work) and ongoing operations.

How to read this tab

Each piece has three parts: what it is, why it matters for the business, and a real-world parallel from a familiar industry. You don't have to memorize all 12, but you should be able to recognize each one when it comes up.

Investor Demo App

What it is: A public website with a chat box. You type a message, click submit, and watch the product analyze the emotional state of what you typed. Shows things like "emotion: frustrated, intensity: high, the user is about to disengage."

Why it matters: This is the primary investor showcase. Patrick uses it to demonstrate the product live in pitch meetings. "Try typing something and watch what happens" is the live demo.

Real-world parallel: Like the demo center at a Tesla showroom. You don't sell the car by describing it; you let people sit in it and feel how it drives.

Internal Tools (the admin website)

What it is: A separate, private website only our team can access. It has 5 different tools inside.

Why it matters: Once the product is in the world, our team needs to debug problems, see how it's performing, label data to improve it, run tests, and experiment. Without these tools, we're flying blind.

Real-world parallel: The back office of an insurance company. Customers see the policy website. Staff use a separate admin system to actually do the work.

The 5 sub-tools inside:

Debugger. Look at any conversation and see exactly what the system did, step by step. Like reading a flight recorder after a plane lands.
Dashboard. Charts and statistics: how many messages, how often the system is failing, average response time, distribution of emotions detected.
Labeling tool. Staff correct the system's analysis when it's wrong. Those corrections become training data that improves the system over time.
Simulator. Generates fake conversations automatically. Used to stress-test the system at scale.
Research playground. Lets the team experiment with different settings and see how the system behaves before changing the live product.

API Service

What it is: The technical "phone line" other companies' software uses to talk to ours. When someone's chatbot wants to know the emotional state of a user message, it sends a request through this phone line and gets an answer back.

Why it matters: This is how we make money. Other companies pay us based on how many calls they make through this phone line.

Real-world parallel: Like Twilio, which lets any company add SMS texting to their app by paying per message. Or Stripe, which lets any company accept credit cards by paying per transaction. We're "infrastructure as a service" for emotional intelligence.

This is the most important commercial piece. When investors ask "how do you make money?" the answer is: companies pay us per API call.

Data Pipeline

What it is: A standardized way to record everything that happens. Every message, every analysis, every system action gets recorded in a consistent format with timestamps and unique IDs.

Why it matters: Two reasons. First, when something goes wrong, we need to trace exactly what happened. Second, the data we collect is the most valuable asset of the company. We use it to make the product smarter over time. That's our competitive moat.

Real-world parallel: An insurance company's claim audit log. Every claim, every approval, every adjustment is recorded in a consistent format with timestamps. You can pull up any claim and see its full history.

Placeholder Brain (Engine v0)

What it is: A temporary, simple version of our AI that produces the emotional analysis. Inside, it's actually just calling Microsoft's AI service (Azure OpenAI, the technology behind ChatGPT) and asking it to do the analysis. Then it formats the response into our standard structure.

Why it matters: We need SOMETHING that produces emotional analyses while we build the real product. This is the placeholder. It works, it produces real outputs, but it's not the proprietary technology we'll eventually charge for.

Real-world parallel: Like leasing a generator while you build your own power plant. It works and keeps the lights on, but it's not the long-term solution.

Important to understand: Phase 1 contains a placeholder. When investors ask "what's actually doing the analysis?" the answer is "we're using a placeholder for the demo, and our CTO Matt is building the real engine in Phase 2." That's not a weakness; it's a deliberate strategy.

Safety Pipeline

What it is: An automatic safety net. If a user types something concerning (like content related to self-harm or crisis), the system automatically detects it and responds with a pre-written safe message instead of letting the AI try to handle it.

Why it matters: Liability and ethics. AI is unpredictable in crisis situations. We don't want our product giving the wrong response to someone in distress. So we route those interactions to a pre-approved safe response automatically. Protects users AND protects us legally.

Real-world parallel: A 911 redirect at a help line. If someone calls about a non-urgent issue but mentions a crisis, the operator routes them to emergency services automatically. Same idea.

Infrastructure

What it is: The actual computer servers, networking, and operational tools that run our product. We rent these from Amazon (Amazon Web Services, called AWS). Microsoft's Azure is where the placeholder brain lives.

Why it matters: Without this, none of our software runs. It's the building, electricity, and plumbing of our digital business.

Real-world parallel: Renting office space and utilities for a business. We don't own the servers; we rent them from Amazon, and they handle the building maintenance.

Developer Interface

What it is: Documentation and example code that teaches other companies how to plug into our API. Includes step-by-step instructions, sample code, and a one-page integration guide.

Why it matters: When we sign up our first customers, they need clear instructions on how to integrate. Bad documentation kills sales.

Real-world parallel: An installation manual for a contractor. Without it, even a great product is hard to use.

Documentation and Handover Package

What it is: All the operational documents our team needs once Pivot finishes their contract: how to deploy updates, how to fix problems, what every part of the system does, how Phase 2 will plug in.

Why it matters: When Pivot's contract ends, our team has to be able to run the product without them. This is the manual.

Real-world parallel: When a contractor finishes building a house, they hand over the homeowner's manual: where every shut-off valve is, how to maintain the systems, who to call when something breaks.

UI Design

What it is: The visual design of the product (colors, fonts, button styles, page layouts). Pivot creates 3 design options. We pick one within 5 business days.

Why it matters: Investor demos need to look professional. A great product with bad design loses more deals than people think.

Real-world parallel: Choosing the interior design of a new office before opening day. Doesn't change what your business does, but absolutely changes the impression visitors have.

Evaluation Framework

What it is: A formal test that proves our product works better than simpler alternatives. We run our system AND a basic competitor (basic sentiment analysis) on the same fake conversations, and compare results.

Why it matters: Investors and customers will ask: "does this actually work better than just doing X?" We need a credible answer with data behind it. The evaluation framework produces that data and a written summary we can include in pitch materials.

Real-world parallel: A comparative effectiveness study. Hospitals run them to prove a new treatment works better than the old standard of care.

External AI Scoring

What it is: An additional capability we asked Pivot to add (in late April). Our system can evaluate not just the human user's emotional state, but ALSO the QUALITY of an AI's response. Did the AI sound defensive? Did it hedge appropriately? Did it match the user's emotional needs?

Why it matters: Two markets, not one. We can pitch to companies who want to "understand their users" AND companies who want to "evaluate the quality of their AI." Same underlying technology, two different sales angles.

Real-world parallel: An insurance company that ALSO offers a service to evaluate the quality of dental clinics. Different product, same data, twice the addressable market.

The most practical tab

Memorize the elevator pitch, internalize the FAQ answers, and you'll be confident in any investor room. Drill these until they're automatic.

4.1 The 60-second elevator pitch (memorize this)

Modern AI can hold conversations, but it can't actually understand how the person it's talking to is feeling. We build the layer underneath every AI that gives it emotional intelligence. We tell other companies' AI systems whether their user is frustrated, engaged, ready to escalate, ready to disengage. We're positioning to be the standard infrastructure that every conversational AI eventually plugs into.

That's 60 seconds. Drill it until it's automatic. You should be able to deliver it confidently in any setting.

4.2 The shorter version (15 seconds)

AI is becoming infrastructure for human interaction. But it has no emotional understanding. We're the missing layer.

4.3 The comparison version (when investors want a model to anchor on)

Just like every website needs a payments processor (Stripe) or every business needs cloud computing (AWS), every AI deployed to talk with humans will need an emotional intelligence layer. That's us.

4.4 The questions investors WILL ask, with answers ready

"How does it actually work technically?"

"Matt is our CTO and the right person for the deep technical detail. At a high level, we use a structured analysis layer that produces a standardized emotional state representation, and any AI system can request that analysis through our API. Want me to set up time with Matt for the technical deep-dive?"

This is a totally legitimate answer. CEOs are not expected to know every implementation detail. Sharp investors RESPECT a CEO who says "let me bring in the technical co-founder."

"Why won't a big tech company like Microsoft or Google just build this themselves?"

"Three reasons. First, we'll have a multi-year head start on the data we collect, and data accumulation is our moat. Second, big tech companies are focused on building their own AI products; they don't actually want to be the infrastructure layer underneath everyone else's. Third, the ones who do try face a Switzerland problem: their customers don't want to depend on their direct competitors. We're neutral infrastructure."

"What's the size of the market?"

"Patrick built our market sizing analysis. The short version is that every conversational AI deployed to talk with humans is a potential customer. As that AI market grows, ours grows with it. I'll have Patrick walk you through the full TAM analysis."

TAM means "Total Addressable Market." The size of the opportunity. Patrick handles this in detail.

"When can we see this working?"

"We're in Phase 1 right now, building the platform with our development partner Pivot-al AI. Investor demo is targeted for around end of June. After that, our CTO Matt builds the actual emotional intelligence engine in Phase 2."

"Are you the founder, or did Patrick hand you the role?"

"Patrick is the founder; the original idea is his. He brought me on as CEO because he wanted someone to lead growth, sales, and fundraising. We have a strong technical team with Matt as CTO, and Dustin running operations as COO. Patrick is now in a co-founder and strategy role."

"What's your AI background?"

"I came from insurance. I'm running this company because I'm strong at sales, fundraising, and building teams. Our AI technical leadership is Matt, who has deep AI experience including running a predictive AI hedge fund. I'm not the AI expert in the room; I'm the person who turns the AI experts' work into a business."

This is a GREAT answer. Investors love a non-technical CEO with deep operational and sales experience paired with a strong technical co-founder. Don't apologize for not being technical. Lean into it. The combination is what makes a startup work.

"Who is Pivot-al AI? Why are you using a contractor for the build?"

"Pivot-al is an Israeli software development firm. Patrick brought them on to build the platform layer in Phase 1, which lets us move fast and put a working investor demo in front of people in 5 months instead of 18. The actual proprietary engine, the Phase 2 work, our CTO Matt builds in-house."

"What's the long-term competitive moat?"

"Three things. One: data accumulation. Every emotional interaction our customers run through our system makes our product smarter. Two: standardized format. We're defining the schema everyone else will adopt. Three: switching cost. Once a company integrates our API into their AI product, swapping us out is expensive and risky."

"What's the revenue model?"

"Companies pay us per API call. Like Twilio for SMS, or Stripe for payments. Volume scales with our customers' usage. Pricing is still being finalized as we move into pilot conversations."

"Why now? Why hasn't someone built this already?"

"Two things. AI's move from research to infrastructure happened in the last 18 months; it wasn't a real market before. And recent interpretability research, including from Anthropic, has shown emotional concepts are actually present inside modern AI models in ways nobody knew before. We're the team turning that into a product."

"What's the risk that doesn't get solved?"

"The biggest technical risk is in Phase 2, the actual emotional intelligence engine. Matt's our CTO and he's planning that build. There's also a market risk that conversational AI takes longer than expected to scale. We're optimistic on both, but those are the honest answers."

Honest "what could go wrong" answers actually impress investors. Don't pretend everything is fine.

4.5 When to defer to Matt (this is a strength, not a weakness)

Always defer to Matt when:

An investor asks "how does the technology actually work?"
An investor asks about specific algorithms, models, or technical claims
An investor asks about timelines for technical milestones
An investor asks about technical competition
Anything where you don't actually know the answer

How to defer cleanly:

"That's a great question for Matt. He's our CTO and built the technical roadmap. Want me to bring him in for a follow-up? He can walk you through it in detail."

Don't pretend to know technical things you don't. It's much worse to give a confidently wrong answer than to defer. Investors LOVE seeing a CEO who clearly understands the role split between business leadership and technical leadership.

4.6 Things to NEVER say

"AI is dangerous and we make it safe." Too vague. Sounds like marketing.
"We're like ChatGPT but for emotion." Wrong. We're not a chatbot.
"We're the future of AI." Too vague. Sounds like hype.
"I don't really understand the tech, but my CTO does." Too apologetic. Reframe as: "Matt handles deep technical detail; I focus on growth and go-to-market."
Don't promise specific technical capabilities you're not sure about.
Don't use technical words you don't fully understand. If you don't know what something means, don't say it.

4.7 Things you CAN say with full confidence

"We're building the emotional intelligence layer for AI."
"Modern AI is emotionally illiterate. We fix that."
"We're infrastructure. Other companies' AI systems use us."
"Our partner Pivot-al is currently building the platform; our CTO Matt is building the actual intelligence engine."
"Our product gets installed inside our customer's systems and gives their AI a 'second pair of eyes.'"
"I came from insurance. Matt came from running an AI hedge fund. We complement each other."

4.8 The two-sentence pitch for someone with no time

Sometimes you'll have 10 seconds in a hallway:

Every AI assistant deployed to talk with humans is missing emotional understanding. We're the layer that fixes that. Everyone eventually plugs into us.

That's it. Move on. They'll ask for more if they're interested.

Final Confidence Builder

Before any investor meeting, re-read these:

The elevator pitch (Section 4.1). Be able to deliver it cold.
The team description (Section 1.7). Know who does what.
The 12 deliverables (Section 2.2). Know the list, even if you don't memorize all the descriptions.
The "when to defer to Matt" framing (Section 4.5). Be ready to defer cleanly and confidently.

You don't have to be an AI expert. You have to be a confident CEO who knows the product, the team, and the strategy. That's what this guide gives you. The technical experts are Matt and (in Phase 1) Pivot. Your job is to lead the company.

How to use this

Skim through once to get familiar. Then come back when something puzzles you. No need to memorize all of it.

A

AI / Artificial IntelligenceSoftware that can do things that traditionally required human intelligence: understanding language, recognizing images, making decisions. Modern AI is essentially "very advanced statistics."

APIA standardized way for two different software systems to talk to each other. Like a waiter at a restaurant: you tell the waiter (the API) what you want, the waiter takes it to the kitchen (the system), and brings back what you ordered. Other companies' software talks to ours through our API.

ADR (Architectural Decision Record)A short document explaining one technical decision: what was decided, why, what alternatives were considered. Matt's team produces these so we have a record of "why we built it this way."

AWS (Amazon Web Services)The cloud computing service from Amazon where our system runs. Think of it as renting computer servers from Amazon instead of buying them ourselves.

AzureMicrosoft's version of AWS. We use Microsoft's Azure for the placeholder AI brain (their AI service is called Azure OpenAI).

B

BackendThe behind-the-scenes part of a software product that the user doesn't see directly. The actual logic and data live there.

C

CloudComputer servers we rent from a big provider (Amazon, Microsoft) instead of owning ourselves. Like leasing office space rather than buying a building.

Containers (Docker)A way of packaging software so it runs the same way on any computer. Like shipping containers in physical logistics: standardized box, easy to move around, contents work the same wherever they end up.

D

DatabaseA digital filing cabinet that stores data in an organized, searchable way. Our data pipeline writes to a database.

DeliverableSomething specific that Pivot owes us at the end of the contract. Pivot has 12 deliverables.

Demo AppThe website where investors and prospects can try our product and see it work.

E

EngineThe "brain" of our product. The part that actually does the emotional analysis.

Engine v0The temporary, simpler "brain" being built right now in Phase 1. It works, but it's a placeholder.

Engine v2.0The real "brain" Matt will build in Phase 2. This is our actual proprietary technology.

Event storeThe database where every event in our system gets logged. Like an audit trail.

F

FallbackWhat the system does when something fails. If our AI can't produce a clean answer, the system "falls back" to a safe default response and logs the failure.

FrontendThe part of a software product that the user actually sees and clicks on. The website, the buttons, the screens.

G

Gateway (API gateway)A piece of software that sits in front of our system and checks every incoming request: is it from an authorized customer? Are they making too many requests? Then it routes valid requests to our backend.

I

InfrastructureThe behind-the-scenes computer systems, networking, and tools that run a software product.

IntegrationWhen another company plugs their software into ours. The technical work of "making our two systems talk to each other."

L

LLM (Large Language Model)A type of AI trained on huge amounts of text. ChatGPT is an LLM. Claude (made by Anthropic) is an LLM. Our temporary placeholder brain (Engine v0) uses Microsoft's LLM (Azure OpenAI, which is GPT-4) to do its work. The real engine in Phase 2 will use a custom approach that Matt builds.

M

MongoDBA type of database that stores data flexibly. We're moving away from it (to PostgreSQL) for technical reasons that don't matter for the pitch.

O

OpenAPI specA standardized document describing how to connect to an API. Other companies' developers read this to plug into our system.

P

Phase 1The first phase of our build, currently in progress with Pivot-al AI. The platform around the brain.

Phase 2The next phase, after Phase 1 finishes. Matt will build the actual emotional intelligence brain. This is the proprietary technology that makes our product valuable.

Pivot-al AIThe Israeli software development company we hired to build Phase 1 for us. Their CEO is David Finkelshteyn.

PostgreSQLA type of database. We're using it as our event store. (You don't need to know more than that.)

R

Rate limitingA limit on how often any one customer can use our system, so a single customer can't overload it. Like a "fair use" cap.

S

Safety PipelineAn automatic safety check. Catches risky inputs (like crisis-related content) and responds with a pre-written safe message instead of letting the AI try to handle it.

SchemaA standardized format for data. Like how an insurance form always has fields for "Name," "Date of Birth," "Policy Number." Everyone fills it in the same way. Our system has a schema for emotional state data.

Shadow modeA way for a customer to deploy our product alongside their existing AI without changing what their users see. Their AI keeps responding as before. Meanwhile, we analyze the conversations in the background. Lets the customer evaluate our product without any production risk.

SOW (Statement of Work)The formal contract document that lists exactly what Pivot is building for us, on what timeline, for how much money.

Structured OutputA consistent way to format data. Computers can process structured data reliably; they struggle with free-form text. Our product outputs structured emotional state data.

T

TAM (Total Addressable Market)The size of the market opportunity. Patrick handles this in investor pitches.

TaxonomyA standardized list of categories. Like how an insurance company has standard codes for medical procedures, we have standard categories for emotional states.

V

ValenceA psychology word for "positive vs. negative vs. neutral." When our product analyzes a message, valence is one of the things it tells you.

Other terms you might hear

StripeA famous company that makes payment processing infrastructure. Used as an analogy for what we're building.

TwilioA famous company that makes SMS infrastructure. Used as an analogy for what we're building.

AnthropicAn AI research company. Their April 2026 research showed emotional concepts are present in AI models in ways nobody knew before. Supporting evidence for our thesis.

End of guide. Good luck out there.