u/MuhammadMujtaba21

​

One of the first ideas I had while planning our AI verification prototype was to add a confidence score to every result.

The more I thought about it, the less I liked the idea.

Imagine an AI says:

"EBITDA = $12.3M (96% confidence)"

What does 96% actually tell the person reviewing a borrower package?

It doesn't explain:

where the number came from,

whether another document reports a different value,

whether the calculation follows the covenant definition,

or whether the evidence is complete.

A high confidence score can easily become another thing people trust without understanding.

So we removed it.

Instead, we're experimenting with something much simpler:

Every important financial claim should answer four questions:

Where did this value come from?

Can I open the source immediately?

Does another document disagree?

If it's calculated, can I reproduce the math?

Maybe confidence scores are useful in some applications.

For the kind of workflows we're exploring, I'd rather help someone verify an answer than persuade them to trust one.

I'm curious how others think about this.

If you're building AI products, do you expose confidence scores to users, or have you found better ways to communicate reliability?

​

One of the first ideas I had while planning our AI verification prototype was to add a confidence score to every result.

The more I thought about it, the less I liked the idea.

Imagine an AI says:

"EBITDA = $12.3M (96% confidence)"

What does 96% actually tell the person reviewing a borrower package?

It doesn't explain:

where the number came from,

whether another document reports a different value,

whether the calculation follows the covenant definition,

or whether the evidence is complete.

A high confidence score can easily become another thing people trust without understanding.

So we removed it.

Instead, we're experimenting with something much simpler:

Every important financial claim should answer four questions:

Where did this value come from?

Can I open the source immediately?

Does another document disagree?

If it's calculated, can I reproduce the math?

Maybe confidence scores are useful in some applications.

For the kind of workflows we're exploring, I'd rather help someone verify an answer than persuade them to trust one.

I'm curious how others think about this.

If you're building AI products, do you expose confidence scores to users, or have you found better ways to communicate reliability?

​

One of the first ideas I had while planning our AI verification prototype was to add a confidence score to every result.

The more I thought about it, the less I liked the idea.

Imagine an AI says:

"EBITDA = $12.3M (96% confidence)"

What does 96% actually tell the person reviewing a borrower package?

It doesn't explain:

where the number came from,

whether another document reports a different value,

whether the calculation follows the covenant definition,

or whether the evidence is complete.

A high confidence score can easily become another thing people trust without understanding.

So we removed it.

Instead, we're experimenting with something much simpler:

Every important financial claim should answer four questions:

Where did this value come from?

Can I open the source immediately?

Does another document disagree?

If it's calculated, can I reproduce the math?

Maybe confidence scores are useful in some applications.

For the kind of workflows we're exploring, I'd rather help someone verify an answer than persuade them to trust one.

I'm curious how others think about this.

If you're building AI products, do you expose confidence scores to users, or have you found better ways to communicate reliability?

One of the first ideas I had while planning our AI verification prototype was to add a confidence score to every result.

The more I thought about it, the less I liked the idea.

Imagine an AI says:

"EBITDA = $12.3M (96% confidence)"

What does 96% actually tell the person reviewing a borrower package?

It doesn't explain:

where the number came from,

whether another document reports a different value,

whether the calculation follows the covenant definition,

or whether the evidence is complete.

A high confidence score can easily become another thing people trust without understanding.

So we removed it.

Instead, we're experimenting with something much simpler:

Every important financial claim should answer four questions:

Where did this value come from?

Can I open the source immediately?

Does another document disagree?

If it's calculated, can I reproduce the math?

Maybe confidence scores are useful in some applications.

For the kind of workflows we're exploring, I'd rather help someone verify an answer than persuade them to trust one.

I'm curious how others think about this.

If you're building AI products, do you expose confidence scores to users, or have you found better ways to communicate reliability?

Over the past few weeks, I've been building a prototype that checks AI-generated financial claims against source documents.

I expected the hardest part to be the language model.

It wasn't.

The hardest part has been defining what "correct" actually means.

For example, imagine two documents in the same credit package:

A covenant certificate reports EBITDA as $12.4M

The management accounts report $11.9M

Neither document is necessarily "wrong."

One might exclude restructuring costs. The other might use the covenant definition from the credit agreement.

An AI can extract both numbers perfectly and still leave you with the real question:

Which definition should be used for this specific decision?

That made me realize something:

In many business workflows, the challenge isn't generating answers.

It's defining the rules that determine which answer is acceptable.

The AI isn't always the weakest link.

Sometimes our own business processes are.

For those of you building AI products:

Have you found that defining business rules was harder than building the AI itself?

I'd be interested to hear examples from other industries.

Over the past few weeks, I've been building a prototype that checks AI-generated financial claims against source documents.

I expected the hardest part to be the language model.

It wasn't.

The hardest part has been defining what "correct" actually means.

For example, imagine two documents in the same credit package:

A covenant certificate reports EBITDA as $12.4M

The management accounts report $11.9M

Neither document is necessarily "wrong."

One might exclude restructuring costs. The other might use the covenant definition from the credit agreement.

An AI can extract both numbers perfectly and still leave you with the real question:

Which definition should be used for this specific decision?

That made me realize something:

In many business workflows, the challenge isn't generating answers.

It's defining the rules that determine which answer is acceptable.

The AI isn't always the weakest link.

Sometimes our own business processes are.

For those of you building AI products:

Have you found that defining business rules was harder than building the AI itself?

I'd be interested to hear examples from other industries.

Over the last few months, I've been thinking about one question:

Why do we trust AI answers simply because they sound confident?

In many domains, that confidence is harmless.

But in finance, a single incorrect number can influence lending decisions, covenant monitoring, portfolio reviews, or risk assessments.

The problem isn't that AI makes mistakes.

Humans do too.

The problem is that today's AI systems rarely show why a financial claim should be trusted.

That realization led me to start building AutoFlow.

We're not building another chatbot or AI wrapper.

We're building a Credit Evidence Engine that verifies eligible financial claims against source evidence, calculation rules, and document consistency.

Our first prototype is intentionally narrow.

It focuses on credit packages, borrower financial statements, covenant calculations, and exception detection.

If two documents report different EBITDA values, the system shouldn't silently choose one.

It should expose the contradiction.

If a leverage ratio is calculated, it should be traceable back to the covenant definition and supporting evidence.

I'm sharing this journey in public because I believe trust is earned through transparent decisions, honest limitations, and continuous learning—not confident marketing.

I'm still in the prototype stage, and I expect many assumptions to be challenged.

That's exactly why I'm building in public.

Question for other founders:

When you're building trust before you have customers or production case studies, what has mattered most in your experience—clear scope, technical proof, transparent progress, or something else?

I'd genuinely like to learn from your experience.

Over the last few months, I've been thinking about one question:

Why do we trust AI answers simply because they sound confident?

In many domains, that confidence is harmless.

But in finance, a single incorrect number can influence lending decisions, covenant monitoring, portfolio reviews, or risk assessments.

The problem isn't that AI makes mistakes.

Humans do too.

The problem is that today's AI systems rarely show why a financial claim should be trusted.

That realization led me to start building AutoFlow.

We're not building another chatbot or AI wrapper.

We're building a Credit Evidence Engine that verifies eligible financial claims against source evidence, calculation rules, and document consistency.

Our first prototype is intentionally narrow.

It focuses on credit packages, borrower financial statements, covenant calculations, and exception detection.

If two documents report different EBITDA values, the system shouldn't silently choose one.

It should expose the contradiction.

If a leverage ratio is calculated, it should be traceable back to the covenant definition and supporting evidence.

I'm sharing this journey in public because I believe trust is earned through transparent decisions, honest limitations, and continuous learning—not confident marketing.

I'm still in the prototype stage, and I expect many assumptions to be challenged.

That's exactly why I'm building in public.

Question for other founders:

When you're building trust before you have customers or production case studies, what has mattered most in your experience—clear scope, technical proof, transparent progress, or something else?

I'd genuinely like to learn from your experience.

Over the last few months, I've been thinking about one question:

Why do we trust AI answers simply because they sound confident?

In many domains, that confidence is harmless.

But in finance, a single incorrect number can influence lending decisions, covenant monitoring, portfolio reviews, or risk assessments.

The problem isn't that AI makes mistakes.

Humans do too.

The problem is that today's AI systems rarely show why a financial claim should be trusted.

That realization led me to start building AutoFlow.

We're not building another chatbot or AI wrapper.

We're building a Credit Evidence Engine that verifies eligible financial claims against source evidence, calculation rules, and document consistency.

Our first prototype is intentionally narrow.

It focuses on credit packages, borrower financial statements, covenant calculations, and exception detection.

If two documents report different EBITDA values, the system shouldn't silently choose one.

It should expose the contradiction.

If a leverage ratio is calculated, it should be traceable back to the covenant definition and supporting evidence.

I'm sharing this journey in public because I believe trust is earned through transparent decisions, honest limitations, and continuous learning—not confident marketing.

I'm still in the prototype stage, and I expect many assumptions to be challenged.

That's exactly why I'm building in public.

Question for other founders:

When you're building trust before you have customers or production case studies, what has mattered most in your experience—clear scope, technical proof, transparent progress, or something else?

I'd genuinely like to learn from your experience.

AutoFlow Research Initiative — Looking for Deep Technical Thinkers

Over the last several months, I've been exploring a question that sits at the intersection of AI, verification, trust, and decision systems:

Can we build systems that independently verify claims produced by AI rather than simply generating answers?

The original idea began with financial analysis.

Consider a statement such as:

"Company revenue grew 25% year-over-year."

Today, most AI systems generate this claim, but they do not formally verify it.

Our approach is different:

1. Extract claims from documents, reports, or AI outputs.
2. Gather supporting evidence.
3. Apply mathematical and logical verification where possible.
4. Identify inconsistencies and contradictions.
5. Produce transparent reasoning rather than black-box conclusions.

The first prototype is focused on finance because financial claims are structured, measurable, and often objectively verifiable.

Examples include:

• Revenue growth calculations
• Financial ratio validation
• Cross-document consistency checks
• Balance sheet reconciliation
• Earnings statement verification

As research progressed, we encountered deeper questions involving computability, trust, governance, formal verification, and adjudication.

One realization is that not every claim can be mathematically proven.

This raises a larger challenge:

Where is the boundary between:

• Proven facts
• Verifiable claims
• Evidence-supported conclusions
• Human-style adjudication

That question is becoming the foundation of our long-term research vision.

Recent Milestones

• Accepted into NVIDIA Inception
• Access to NVIDIA startup resources and technical programs
• Building the architecture for our first verification-focused prototype
• Engaging with researchers and experienced engineers on verification and governance concepts
• Initial outreach to pre-seed investors and startup ecosystems

Who I'm Looking For

I'm interested in meeting people who enjoy difficult problems and are willing to challenge assumptions.

Particularly:

• AI/ML researchers and engineers
• Formal verification and theorem-proving enthusiasts
• Distributed systems and orchestration experts
• C++ systems engineers
• Applied mathematicians
• Trust, governance, and decision-system researchers

What You'll Receive

For the right long-term collaborators:

• Significant technical ownership
• Direct influence on architecture and research direction
• Equity participation based on contribution and commitment
• Access to NVIDIA Inception resources available to the team
• Opportunity to help define a new category around AI trust and verification

I'm not looking for people who simply agree with the vision.

I'm looking for people who can find the flaws in it.

If concepts such as verification, computability, trust, formal reasoning, governance, theorem proving, symbolic systems, or AI reliability interest you, I'd love to connect and exchange ideas.

Feel free to comment or send a message.

AutoFlow Research Initiative — Looking for Deep Technical Thinkers

Over the last several months, I've been exploring a question that sits at the intersection of AI, verification, trust, and decision systems:

Can we build systems that independently verify claims produced by AI rather than simply generating answers?

The original idea began with financial analysis.

Consider a statement such as:

"Company revenue grew 25% year-over-year."

Today, most AI systems generate this claim, but they do not formally verify it.

Our approach is different:

1. Extract claims from documents, reports, or AI outputs.
2. Gather supporting evidence.
3. Apply mathematical and logical verification where possible.
4. Identify inconsistencies and contradictions.
5. Produce transparent reasoning rather than black-box conclusions.

The first prototype is focused on finance because financial claims are structured, measurable, and often objectively verifiable.

Examples include:

• Revenue growth calculations
• Financial ratio validation
• Cross-document consistency checks
• Balance sheet reconciliation
• Earnings statement verification

As research progressed, we encountered deeper questions involving computability, trust, governance, formal verification, and adjudication.

One realization is that not every claim can be mathematically proven.

This raises a larger challenge:

Where is the boundary between:

• Proven facts
• Verifiable claims
• Evidence-supported conclusions
• Human-style adjudication

That question is becoming the foundation of our long-term research vision.

Recent Milestones

• Accepted into NVIDIA Inception
• Access to NVIDIA startup resources and technical programs
• Building the architecture for our first verification-focused prototype
• Engaging with researchers and experienced engineers on verification and governance concepts
• Initial outreach to pre-seed investors and startup ecosystems

Who I'm Looking For

I'm interested in meeting people who enjoy difficult problems and are willing to challenge assumptions.

Particularly:

• AI/ML researchers and engineers
• Formal verification and theorem-proving enthusiasts
• Distributed systems and orchestration experts
• C++ systems engineers
• Applied mathematicians
• Trust, governance, and decision-system researchers

What You'll Receive

For the right long-term collaborators:

• Significant technical ownership
• Direct influence on architecture and research direction
• Equity participation based on contribution and commitment
• Access to NVIDIA Inception resources available to the team
• Opportunity to help define a new category around AI trust and verification

I'm not looking for people who simply agree with the vision.

I'm looking for people who can find the flaws in it.

If concepts such as verification, computability, trust, formal reasoning, governance, theorem proving, symbolic systems, or AI reliability interest you, I'd love to connect and exchange ideas.

Feel free to comment or send a message.

AutoFlow Research Initiative — Looking for Deep Technical Thinkers

Over the last several months, I've been exploring a question that sits at the intersection of AI, verification, trust, and decision systems:

Can we build systems that independently verify claims produced by AI rather than simply generating answers?

The original idea began with financial analysis.

Consider a statement such as:

"Company revenue grew 25% year-over-year."

Today, most AI systems generate this claim, but they do not formally verify it.

Our approach is different:

1. Extract claims from documents, reports, or AI outputs.
2. Gather supporting evidence.
3. Apply mathematical and logical verification where possible.
4. Identify inconsistencies and contradictions.
5. Produce transparent reasoning rather than black-box conclusions.

The first prototype is focused on finance because financial claims are structured, measurable, and often objectively verifiable.

Examples include:

• Revenue growth calculations
• Financial ratio validation
• Cross-document consistency checks
• Balance sheet reconciliation
• Earnings statement verification

As research progressed, we encountered deeper questions involving computability, trust, governance, formal verification, and adjudication.

One realization is that not every claim can be mathematically proven.

This raises a larger challenge:

Where is the boundary between:

• Proven facts
• Verifiable claims
• Evidence-supported conclusions
• Human-style adjudication

That question is becoming the foundation of our long-term research vision.

Recent Milestones

• Accepted into NVIDIA Inception
• Access to NVIDIA startup resources and technical programs
• Building the architecture for our first verification-focused prototype
• Engaging with researchers and experienced engineers on verification and governance concepts
• Initial outreach to pre-seed investors and startup ecosystems

Who I'm Looking For

I'm interested in meeting people who enjoy difficult problems and are willing to challenge assumptions.

Particularly:

• AI/ML researchers and engineers
• Formal verification and theorem-proving enthusiasts
• Distributed systems and orchestration experts
• C++ systems engineers
• Applied mathematicians
• Trust, governance, and decision-system researchers

What You'll Receive

For the right long-term collaborators:

• Significant technical ownership
• Direct influence on architecture and research direction
• Equity participation based on contribution and commitment
• Access to NVIDIA Inception resources available to the team
• Opportunity to help define a new category around AI trust and verification

I'm not looking for people who simply agree with the vision.

I'm looking for people who can find the flaws in it.

If concepts such as verification, computability, trust, formal reasoning, governance, theorem proving, symbolic systems, or AI reliability interest you, I'd love to connect and exchange ideas.

Feel free to comment or send a message.

AutoFlow Research Initiative — Looking for Deep Technical Thinkers

Over the last several months, I've been exploring a question that sits at the intersection of AI, verification, trust, and decision systems:

Can we build systems that independently verify claims produced by AI rather than simply generating answers?

The original idea began with financial analysis.

Consider a statement such as:

"Company revenue grew 25% year-over-year."

Today, most AI systems generate this claim, but they do not formally verify it.

Our approach is different:

1. Extract claims from documents, reports, or AI outputs.
2. Gather supporting evidence.
3. Apply mathematical and logical verification where possible.
4. Identify inconsistencies and contradictions.
5. Produce transparent reasoning rather than black-box conclusions.

The first prototype is focused on finance because financial claims are structured, measurable, and often objectively verifiable.

Examples include:

• Revenue growth calculations
• Financial ratio validation
• Cross-document consistency checks
• Balance sheet reconciliation
• Earnings statement verification

As research progressed, we encountered deeper questions involving computability, trust, governance, formal verification, and adjudication.

One realization is that not every claim can be mathematically proven.

This raises a larger challenge:

Where is the boundary between:

• Proven facts
• Verifiable claims
• Evidence-supported conclusions
• Human-style adjudication

That question is becoming the foundation of our long-term research vision.

Recent Milestones

• Accepted into NVIDIA Inception
• Access to NVIDIA startup resources and technical programs
• Building the architecture for our first verification-focused prototype
• Engaging with researchers and experienced engineers on verification and governance concepts
• Initial outreach to pre-seed investors and startup ecosystems

Who I'm Looking For

I'm interested in meeting people who enjoy difficult problems and are willing to challenge assumptions.

Particularly:

• AI/ML researchers and engineers
• Formal verification and theorem-proving enthusiasts
• Distributed systems and orchestration experts
• C++ systems engineers
• Applied mathematicians
• Trust, governance, and decision-system researchers

What You'll Receive

For the right long-term collaborators:

• Significant technical ownership
• Direct influence on architecture and research direction
• Equity participation based on contribution and commitment
• Access to NVIDIA Inception resources available to the team
• Opportunity to help define a new category around AI trust and verification

I'm not looking for people who simply agree with the vision.

I'm looking for people who can find the flaws in it.

If concepts such as verification, computability, trust, formal reasoning, governance, theorem proving, symbolic systems, or AI reliability interest you, I'd love to connect and exchange ideas.

Feel free to comment or send a message.

AutoFlow Research Initiative — Looking for Deep Technical Thinkers

Over the last several months, I've been exploring a question that sits at the intersection of AI, verification, trust, and decision systems:

Can we build systems that independently verify claims produced by AI rather than simply generating answers?

The original idea began with financial analysis.

Consider a statement such as:

"Company revenue grew 25% year-over-year."

Today, most AI systems generate this claim, but they do not formally verify it.

Our approach is different:

1. Extract claims from documents, reports, or AI outputs.
2. Gather supporting evidence.
3. Apply mathematical and logical verification where possible.
4. Identify inconsistencies and contradictions.
5. Produce transparent reasoning rather than black-box conclusions.

The first prototype is focused on finance because financial claims are structured, measurable, and often objectively verifiable.

Examples include:

• Revenue growth calculations
• Financial ratio validation
• Cross-document consistency checks
• Balance sheet reconciliation
• Earnings statement verification

As research progressed, we encountered deeper questions involving computability, trust, governance, formal verification, and adjudication.

One realization is that not every claim can be mathematically proven.

This raises a larger challenge:

Where is the boundary between:

• Proven facts
• Verifiable claims
• Evidence-supported conclusions
• Human-style adjudication

That question is becoming the foundation of our long-term research vision.

Recent Milestones

• Accepted into NVIDIA Inception
• Access to NVIDIA startup resources and technical programs
• Building the architecture for our first verification-focused prototype
• Engaging with researchers and experienced engineers on verification and governance concepts
• Initial outreach to pre-seed investors and startup ecosystems

Who I'm Looking For

I'm interested in meeting people who enjoy difficult problems and are willing to challenge assumptions.

Particularly:

• AI/ML researchers and engineers
• Formal verification and theorem-proving enthusiasts
• Distributed systems and orchestration experts
• C++ systems engineers
• Applied mathematicians
• Trust, governance, and decision-system researchers

What You'll Receive

For the right long-term collaborators:

• Significant technical ownership
• Direct influence on architecture and research direction
• Equity participation based on contribution and commitment
• Access to NVIDIA Inception resources available to the team
• Opportunity to help define a new category around AI trust and verification

I'm not looking for people who simply agree with the vision.

I'm looking for people who can find the flaws in it.

If concepts such as verification, computability, trust, formal reasoning, governance, theorem proving, symbolic systems, or AI reliability interest you, I'd love to connect and exchange ideas.

Feel free to comment or send a message.

AutoFlow Research Initiative — Looking for Deep Technical Thinkers

Over the last several months, I've been exploring a question that sits at the intersection of AI, verification, trust, and decision systems:

Can we build systems that independently verify claims produced by AI rather than simply generating answers?

The original idea began with financial analysis.

Consider a statement such as:

"Company revenue grew 25% year-over-year."

Today, most AI systems generate this claim, but they do not formally verify it.

Our approach is different:

1. Extract claims from documents, reports, or AI outputs.
2. Gather supporting evidence.
3. Apply mathematical and logical verification where possible.
4. Identify inconsistencies and contradictions.
5. Produce transparent reasoning rather than black-box conclusions.

The first prototype is focused on finance because financial claims are structured, measurable, and often objectively verifiable.

Examples include:

• Revenue growth calculations
• Financial ratio validation
• Cross-document consistency checks
• Balance sheet reconciliation
• Earnings statement verification

As research progressed, we encountered deeper questions involving computability, trust, governance, formal verification, and adjudication.

One realization is that not every claim can be mathematically proven.

This raises a larger challenge:

Where is the boundary between:

• Proven facts
• Verifiable claims
• Evidence-supported conclusions
• Human-style adjudication

That question is becoming the foundation of our long-term research vision.

Recent Milestones

• Accepted into NVIDIA Inception
• Access to NVIDIA startup resources and technical programs
• Building the architecture for our first verification-focused prototype
• Engaging with researchers and experienced engineers on verification and governance concepts
• Initial outreach to pre-seed investors and startup ecosystems

Who I'm Looking For

I'm interested in meeting people who enjoy difficult problems and are willing to challenge assumptions.

Particularly:

• AI/ML researchers and engineers
• Formal verification and theorem-proving enthusiasts
• Distributed systems and orchestration experts
• C++ systems engineers
• Applied mathematicians
• Trust, governance, and decision-system researchers

What You'll Receive

For the right long-term collaborators:

• Significant technical ownership
• Direct influence on architecture and research direction
• Equity participation based on contribution and commitment
• Access to NVIDIA Inception resources available to the team
• Opportunity to help define a new category around AI trust and verification

I'm not looking for people who simply agree with the vision.

I'm looking for people who can find the flaws in it.

If concepts such as verification, computability, trust, formal reasoning, governance, theorem proving, symbolic systems, or AI reliability interest you, I'd love to connect and exchange ideas.

Feel free to comment or send a message.

🚀 Join AutoFlow: Shape the Future of AI Trust 🚀

​

I’m a 17-year-old founder, and AutoFlow—a startup in the NVIDIA Inception Program—is on a mission to redefine AI trustworthiness.

​

We’re building a mathematical verification engine that ensures AI claims are transparent, structured, and provable. Thanks to NVIDIA Inception, we now have access to GPUs, cloud credits, and technical mentorship to accelerate us.

​

Right now, we need a small but passionate technical team: students, recent graduates, or open-source developers who want to dive into C++, knowledge graphs, and formal verification. In return, you’ll get hands-on experience with industry-level AI tools, cloud resources, and a chance to shape a real-world prototype for a future seed round.

​

If you’re passionate about AI safety, formal methods, or trust in enterprise AI, I’d love to connect.

​

Let’s build something groundbreaking together! 🚀

​

#AI #Startups #NVIDIAInception #TrustworthyAI #CPlusPlus #KnowledgeGraphs #OpenSource #AIResearch

​

​

🚀 Join AutoFlow: Shape the Future of AI Trust 🚀

​

I’m a 17-year-old founder, and AutoFlow—a startup in the NVIDIA Inception Program—is on a mission to redefine AI trustworthiness.

​

We’re building a mathematical verification engine that ensures AI claims are transparent, structured, and provable. Thanks to NVIDIA Inception, we now have access to GPUs, cloud credits, and technical mentorship to accelerate us.

​

Right now, we need a small but passionate technical team: students, recent graduates, or open-source developers who want to dive into C++, knowledge graphs, and formal verification. In return, you’ll get hands-on experience with industry-level AI tools, cloud resources, and a chance to shape a real-world prototype for a future seed round.

​

If you’re passionate about AI safety, formal methods, or trust in enterprise AI, I’d love to connect.

​

Let’s build something groundbreaking together! 🚀

​

#AI #Startups #NVIDIAInception #TrustworthyAI #CPlusPlus #KnowledgeGraphs #OpenSource #AIResearch

​

​

🚀 Join AutoFlow: Shape the Future of AI Trust 🚀

​

I’m a 17-year-old founder, and AutoFlow—a startup in the NVIDIA Inception Program—is on a mission to redefine AI trustworthiness.

​

We’re building a mathematical verification engine that ensures AI claims are transparent, structured, and provable. Thanks to NVIDIA Inception, we now have access to GPUs, cloud credits, and technical mentorship to accelerate us.

​

Right now, we need a small but passionate technical team: students, recent graduates, or open-source developers who want to dive into C++, knowledge graphs, and formal verification. In return, you’ll get hands-on experience with industry-level AI tools, cloud resources, and a chance to shape a real-world prototype for a future seed round.

​

If you’re passionate about AI safety, formal methods, or trust in enterprise AI, I’d love to connect.

​

Let’s build something groundbreaking together! 🚀

​

#AI #Startups #NVIDIAInception #TrustworthyAI #CPlusPlus #KnowledgeGraphs #OpenSource #AIResearch

​

​

​

​

At 17, I started asking a simple question:

​

If AI is going to power the future, who will make AI trustworthy?

​

Today, most AI systems remain probabilistic. They hallucinate, produce unverifiable outputs, and struggle in high-stakes domains like finance, healthcare, and compliance.

​

At AutoFlow, we're researching a different direction:

​

Building an external Mathematical Verification Engine that sits around LLMs and verifies their outputs using knowledge graphs, symbolic reasoning, and deterministic consistency checking.

​

Our long-term vision is not to replace LLMs.

​

Our vision is to build the trust infrastructure that future AI systems depend on.

​

Current Research Areas

​

1. Structured fact graph construction from documents

​

2. Claim extraction from LLM outputs

​

3. Mathematical consistency verification

​

4. Symbolic reasoning using Z3/CVC5

​

5. High-performance C++ verification engine

6. Multi-agent orchestration and audit trails

​

7. Benchmarking against RAG, CoT etc.

​

We are starting with finance as the first proof-of-concept because financial data is highly structured and mathematically verifiable.

​

Our architecture currently explores:

​

Input → Fact Graph → LLM → Claim Extraction → Verification → Certificate

​

Milestone:

​

We're proud to share that AutoFlow has been accepted into the NVIDIA Inception Program, giving us access to startup resources, GPU infrastructure opportunities, cloud benefits, and technical ecosystem support.

​

We Are Looking For contributors for:

​

NLP & Information Extraction, Knowledge Graphs,Symbolic AI Formal Logic & Theorem Proving, C++ Systems Engineering, Distributed Systems AI Safety & Trustworthy AI

​

If you're excited by hard problems and want to work on the future of trustworthy AI, let's connect.

​

The goal isn't to build another AI wrapper.

​

The goal is to build infrastructure that AI systems can trust.

​

​

AI models have become incredibly capable.

​

But one problem remains:

​

Trust.

​

Even state-of-the-art models hallucinate, especially in high-stakes industries like finance and healthcare.

​

At AutoFlow, we're researching whether AI outputs can be externally verified through:

Knowledge graphs

​

Mathematical consistency checks

Symbolic reasoning

Verification certificates

​

Instead of asking:

"Is the model confident?"

​

We ask:

"Can the claim be proven?"

​

We're beginning with finance as a proof of concept before expanding to broader domains.

​

AutoFlow was recently accepted into the NVIDIA Inception Program, helping us accelerate research into trustworthy AI systems.

​

Question for the community:

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Do you think truly verifiable AI is possible, or will AI always remain probabilistic?