The first stock-research vertical agent you actually own. Tesseract GPT 2.0
Fully owned. Customizable. Built to last for years.
10 sequential and cumulative prompts
20 skills containing more than 400 pages that guide the research
Inspired by the latest literature by Stanford and Columbia studies
Usable in ChatGPT, Claude, Gemini, etc
In 2026, most investors do not lack information. They have too much of it.
Everything looks relevant.
Everything looks urgent.
Everything looks worth checking.
- One source says buy.
- Another says wait.
- Another says the story is somewhere else.
- After enough reading, your picture often gets worse.
Too much input does not create conviction. Most of the time, it destroys it.
Most investing tools give you everything except the most important thing: interpretation.
As a modern investor, you need a tool that helps you to interpret situations and make better decisions.
Instead, most tools just do this:
- They blast you with 50 recommendations at once.
- For each stock, they give tens of charts that serve no point.
- They overload you with words and numbers.
- In the end, they leave you with zero interpretation.
That is exactly where investors still lose time, clarity, and conviction.
2 ways of AI assistance in stock analysis
While the outlook appears cautiously optimistic, the stock’s price over the next 12 months will depend on broader market conditions, macroeconomic trends, and company-specific execution; investors should monitor key catalysts such as earnings results, guidance updates, and changes in industry demand, as these factors can meaningfully impact valuation; given the inherent uncertainty in forecasting, a range of outcomes is possible, and price action may remain volatile in response to news flow and sentiment shifts; overall, maintaining a diversified approach and aligning position sizing with your risk tolerance can help manage potential downside while participating in upside potential.
Jonathan, do not buy this stock right now, you will get burned. Its margins are shrinking, insiders are selling, and if this was not enough, it is overvalued as hell.
Regards,
Tesseract GPT
One page is all that’s needed for an investment thesis.
Warren Buffett, Sequoia Capital, or Peter Thiel could always compress the heart of an investment thesis into one page.
Good analysis should not be long. It should interpret the actual facts. Length often hides weak judgment and weak interpretation.
Tesseract GPT 2.0 is built on the opposite idea: investors need synthesized arguments, not a 20-page slop that says nothing and ends up inconclusive.
That’s exactly what Tesseract GPT 2.0 does.
It takes scattered inputs and turns them into a simpler, cleaner, more usable investment view.
Point A
Noise, friction, and scattered judgment.
Point B
Clarity, compression, and decision support.
Three output modes, if 1 page is not enough...
You only need to run the chain once to get all the three outputs in the end. If one page is not enough, you can simply say, give me the advanced/max version.
Standard
1 page
- Fastest way to judge an idea
- Clear thesis, risks, catalysts, target
- Best for most situations
Advanced
3 pages
- More context, numbers, and nuance
- Deeper view of thesis strength
- Best for heavier due diligence
Maximum
10 pages
- Maximum depth for complex cases
- Full breakdown of thesis mechanics
- Best after shorter modes
Statistics
90%
maximum time reduction for the analysis of a stock investment thesis
<2 min
the average time that the user will have to spend on his own for 1 final output
400+
pages of instructions, guidelines and best practices on the backend
100x
more information considered, compared with single prompts
What you get with Tesseract GPT 2.0
A complete research workflow built to save time, improve clarity, and make your output more decision-ready.
Pro-Chain workflow
A structured prompt-chain system designed to move from raw input toward cleaner analytical output.
Support library
Internal documents and research materials that strengthen the reasoning behind the generated work.
Tutorials included
Written and video guidance to help you understand the system and apply it without friction.
Maintenance protocol
A system for updating prompts and resources so the workflow can keep evolving over time.
Resource expansion guide
Extra guidance on how to make the framework stronger with added inputs, context, and source material.
Build your own workflow
Import the framework into your own process and adapt it into a more customized research engine.
The core premise
(prompt chains win)
Pro-Chain beats single prompts, Agent mode and Deep Research for deeply analytical tasks
Chains force structure (no wandering)
Chains stack context (each step improves the next)
Chains stress-test before committing (less confident nonsense)
The proof from Stanford & Columbia studies
Sharper decisions: multi-step workflows produced up to 5x stronger performance on difficult reasoning tasks.
Better reasoning: Stanford showed that structured search pushed success from 4% to 74% on a hard planning task.
Accuracy: a 2025 study showed 70.7 vs 42.5 for a structured action-chain workflow over chain-of-thought.
A commentary on the result of the studies can be found in this article from our blog.
How do you use it?
You write the name of the stock in the first prompt.
You copy and paste the sequence of given prompts.
You read the final analysis.
And that’s it. The 400+ page logic runs in the backend.
The system is built to save you time and mental power so you can be more confident before acting in the markets.
Want to see a video tutorial? Check this YouTube video.
Can it be used for other purposes?
Comparing stocks
Analyzing ETFs
Assisting in equity reports
Monitoring specific companies
Yes, it can do all of that and much more, as long as the underlying object of the analysis is equities.
Guided by proprietary resources...
The engine doesn’t rely on random web browsing. It draws from private, curated material that guides each step.
Read more about the logical ratio of choosing proprietary information as the best choice for highly complex tasks in this article of the blog:
Article title
Note: The customization of the system is not advised to people that don’t know really well what they’re doing. These people usually fall under 2 categories: experienced investors or highly competent people working in finance.
...while being fully customizable
Do you want to turn the entire workflow into a system focused purely on value investing?
Do you want to tailor it to equities listed in a specific country?
Do you want to convert the files into a Claude Skills .md resource?
Do you want to build an agentic AI infrastructure around this product?
You can do all of that. It is all up to you.
Read more about why customization freedom gives the user so many opportunities to shape the workflow based on his specific needs in this article of the blog:
Article titleTechniques and concepts used for the system logic
Mathematical concepts
Deep analytical logical concepts
Historical data
As a fan of computational statistics I have included the thoughts behind the mathematical logic of the system in this blog article:
Article titleThe intention of the agent is one and only
“The end result will be one of the best pieces of information that an equity investor can use to decide about a specific investment decision.”
Why a public LLM can’t do this
Built to handle many different tasks.
Built for one specific equity-analysis workflow.
Starts from a blank conversational state.
Runs through a predefined structured chain.
Can wander depending on prompt quality.
Forces ordered steps before reaching conclusions.
Gets guidelines on the go for each new question.
Includes best practices inside the workflow itself.
Can vary a lot from run to run.
Designed to arrive at a specific output structure.
Usually gives you a conversation or an answer.
Produces condensed outputs built for decision-making.
But why not trust the target prices published by banks and equity research firms?
Because they are fundamentally biased.
Do you think that the best opportunities are published for free?
Do you think that $20 per month blog subscriptions are built to make you rich?
That’s the problem, the best research is hidden from the public or gatekeeped as hell.
Historically speaking, equity research analysts give vastly positive projections.
The reason is simple, companies pressure rating firms on one hand and there are hidden incentives for publishing biased projections, that oftentimes even the analyst himself doesn’t believe.
Note: There are many outstanding and hard working equity research analysts in the world, here the reference is made to the misaligned incentives and the way in which investment advice is given, not to the skills of the people.
How can it help a beginner or part-time investor?
It gives a beginner a more structured way to think before acting on a stock idea.
It reduces dependence on shallow one-shot answers by forcing a clearer analytical path.
Read more about how Tesseract GPT can help a beginner investor in this article of the blog.
Article link
And how can it help an experienced investor?
It speeds up repeatable research without removing the investor’s own judgment and framework.
It gives a disciplined base process that can still be shaped around personal methodology.
Read more about how Tesseract GPT can help an experienced investor in this article of the blog.
Article link
Can it actually help an analyst? Yes, but only in its advanced variant.
The advanced version is more suitable when deeper structure, customization, and support material are required.
It is better suited to analysts who want condensed research outputs rather than casual conversational answers.
Read more about how Tesseract GPT can help a professional analyst in this article of the blog.
Article link
How does it save time?
3 practical scenarios
Kill bad ideas fast
Filter weak setups earlier, so you spend less time following ideas that were never worth serious attention.
Accelerate promising research
Move through a serious investment case with more speed, without collapsing the structure needed for good analysis.
Research more at scale
Screen a larger set of opportunities in a disciplined way, then narrow your focus to the strongest candidates.
What happens after you buy?
- You open the folder with all the files inside.
- You choose one of the three paths: Easy, Standard, or Advanced, based on your preferred setup.
- You write the name of your ticker.
- You wait for the analysis. Nothing more.
Replace with your direct image link later
Who it is not for
- Not for SPY, QQQ, or VIX traders.
- Not for equity day traders. There will be a specific product for them.
- Not for people wanting instant trade alerts.
- Not for people who won’t read a one-page memo.
The first client of this system was its creator
Before it became a product, this workflow was built for direct personal use as a trader and investor. The goal was simple: remove noise, force structure, and reach a more disciplined conclusion than generic AI tools ever could.
A workflow like this should be trusted in practice before it is ever offered to others.
The system was shaped by a real problem, and three months of research and optimization
It all started with one client, an investor who had underperformed the S&P 500 for the last 10 years, except 2023.
After he got tired from me telling him that I would not consider his preferred tech stock as a good investment he eventually found solace at ChatGPT and started using it as an alternative.
I just told him “Ok, please just share some screenshots of the conversation with me, will review them for free”.
He did and what I saw was a mediocre inconclusive slop that eventually guided him towards a “buy”.
Then I checked the sources, it was a mix of Yahoo Finance, Reddit, Wikipedia, and Jim Cramer’s opinions as a cherry on top.
That was the moment I realized that I had to do something…
Choose the plan that fits your workflow.
Different tiers for different levels of ownership, customization, and analytical depth.
GPT
Best for starting with the core experience
Access to the GPT plus a lighter support layer built to mirror the logic of the full chain.
- Access to the GPT
- 5 supporting folders
- Tutorial included
Basic
Best for the full chain itself
Adds the full chain and the first real layer of workflow customization.
- Everything in GPT
- Full ProChain access
- Chain customization guide
Advanced
Best for deeper backend control
Adds the full resource layer, deeper documentation, and ongoing maintenance logic.
- Everything in Basic
- All resources included
- 400+ pages of documentation
- Maintenance protocol
Professional
Best for maximum control
The most complete version, with advanced build logic, API guidance, and further accuracy support.
- Everything in Advanced
- API instructions
- Accuracy guidelines
- Advanced chain-building guide
Compare what each plan includes
Each higher tier includes the features of the tiers below it.
| Includes | GPT | Basic | Advanced | Professional |
|---|---|---|---|---|
| Core Access | ||||
| Access to GPT | ✓ | ✓ | ✓ | ✓ |
| 5 supporting folders | ✓ | ✓ | ✓ | ✓ |
| Tutorial included | ✓ | ✓ | ✓ | ✓ |
| Workflow Control | ||||
| Full ProChain access | ✕ | ✓ | ✓ | ✓ |
| Chain customization guide | ✕ | ✓ | ✓ | ✓ |
| Research Backend | ||||
| Full resource library | ✕ | ✕ | ✓ | ✓ |
| 400+ pages of documentation | ✕ | ✕ | ✓ | ✓ |
| Resource customization guide | ✕ | ✕ | ✓ | ✓ |
| Maintenance protocol | ✕ | ✕ | ✓ | ✓ |
| Advanced Build Layer | ||||
| API instructions | ✕ | ✕ | ✕ | ✓ |
| Guidelines for further accuracy | ✕ | ✕ | ✕ | ✓ |
| Advanced chain-building guide | ✕ | ✕ | ✕ | ✓ |
Custom Solutions
Need something tailored to your exact workflow?
For custom research workflows, special packaging, or a more tailored setup, get in touch and we’ll discuss the right structure together.
An example of output difference
Generalistic LLM
Tesseract GPT 2.0
What investors actually need
The modern investor can find the P/E ratio, current cash reserves, or the last quarter revenues all by himself.
He can browse the news on his own.
He can search for countless articles.
He has so many tools at his disposal.
What he needs is guidance and interpretation.
What he needs is not more overload. It’s not more charts or more graphics. Actually, those things make it even worse.
That’s something that only a vertical agent can give in the AI space at this moment.
Why? Because the vertical agent is specialized in only one task, and analyzing stocks is something so deep and so complicated that it cannot be left in the hands of a generalistic LLM while at the same time expecting something that’s not a worthless collection of sentences and numbers blasted into so-called investment research.
Maximum power for one purpose.
This whole system is built with only one intention: to give the investor the maximum level of equity research that an LLM can give at this moment in time in history.
It is hard to believe that there is a more accurate, deeper way to do this specific task without enterprise capabilities or exclusive data.
Got questions? Do not hesitate to ask. Usually answering in less than 24 hours.
Frequently Asked Questions
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