In an period the place knowledge privateness and effectivity are paramount, funding analysts and institutional researchers could more and more be asking: Can we harness the ability of generative AI with out compromising delicate knowledge? The reply is a convincing sure.
This submit describes a customizable, open-source framework that analysts can adapt for safe, native deployment. It showcases a hands-on implementation of a privately hosted massive language mannequin (LLM) utility, personalized to help with reviewing and querying funding analysis paperwork. The result’s a safe, cost-effective AI analysis assistant, one that may parse hundreds of pages in seconds and by no means sends your knowledge to the cloud or the web. I exploit AI to reinforce the method of funding evaluation by way of partial automation, additionally mentioned in an Enterprising Investor submit on utilizing AI to reinforce funding evaluation.
This chatbot-style software permits analysts to question advanced analysis supplies in plain language with out ever exposing delicate knowledge to the cloud.
The Case for “Non-public GPT”
For professionals working in buy-side funding analysis — whether or not in equities, fastened revenue, or multi-asset methods — the usage of ChatGPT and related instruments raises a serious concern: confidentiality. Importing analysis stories, funding memos, or draft providing paperwork to a cloud-based AI software is normally not an possibility.
That’s the place “Non-public GPT” is available in: a framework constructed completely on open-source parts, operating domestically by yourself machine. There’s no reliance on utility programming interface (API) keys, no want for an web connection, and no danger of information leakage.
This toolkit leverages:
- Python scripts for ingestion and embedding of textual content paperwork
- Ollama, an open-source platform for internet hosting native LLMs on the pc
- Streamlit for constructing a user-friendly interface
- Mistral, DeepSeek, and different open-source fashions for answering questions in pure language
The underlying Python code for this instance is publicly housed within the Github repository right here. Further steering on step-by-step implementation of the technical features on this mission is offered on this supporting doc.
Querying Analysis Like a Chatbot With out the Cloud
Step one on this implementation is launching a Python-based digital atmosphere on a private pc. This helps to take care of a singular model of packages and utilities that feed into this utility alone. In consequence, settings and configuration of packages utilized in Python for different functions and applications stay undisturbed. As soon as put in, a script reads and embeds funding paperwork utilizing an embedding mannequin. These embeddings enable LLMs to grasp the doc’s content material at a granular stage, aiming to seize semantic which means.
As a result of the mannequin is hosted by way of Ollama on a neighborhood machine, the paperwork stay safe and don’t depart the analyst’s pc. That is notably vital when coping with proprietary analysis, personal financials like in personal fairness transactions or inside funding notes.
A Sensible Demonstration: Analyzing Funding Paperwork
The prototype focuses on digesting long-form funding paperwork akin to earnings name transcripts, analyst stories, and providing statements. As soon as the TXT doc is loaded into the designated folder of the private pc, the mannequin processes it and turns into able to work together. This implementation helps all kinds of doc sorts starting from Microsoft Phrase (.docx), web site pages (.html) to PowerPoint shows (.pptx). The analyst can start querying the doc by way of the chosen mannequin in a easy chatbot-style interface rendered in a neighborhood net browser.
Utilizing an online browser-based interface powered by Streamlit, the analyst can start querying the doc by way of the chosen mannequin. Though this launches a web-browser, the applying doesn’t work together with the web. The browser-based rendering is used on this instance to reveal a handy consumer interface. This might be modified to a command-line interface or different downstream manifestations. For instance, after ingesting an earnings name transcript of AAPL, one could merely ask:
“What does Tim Prepare dinner do at AAPL?”
Inside seconds, the LLM parses the content material from the transcript and returns:
“…Timothy Donald Prepare dinner is the Chief Government Officer (CEO) of Apple Inc…”
This result’s cross-verified inside the software, which additionally exhibits precisely which pages the data was pulled from. Utilizing a mouse click on, the consumer can increase the “Supply” objects listed beneath every response within the browser-based interface. Totally different sources feeding into that reply are rank-ordered based mostly on relevance/significance. This system could be modified to record a unique variety of supply references. This function enhances transparency and belief within the mannequin’s outputs.
Mannequin Switching and Configuration for Enhanced Efficiency
One standout function is the power to change between completely different LLMs with a single click on. The demonstration reveals the aptitude to cycle amongst open-source LLMs like Mistral, Mixtral, Llama, and DeepSeek. This exhibits that completely different fashions could be plugged into the identical structure to check efficiency or enhance outcomes. Ollama is an open-source software program package deal that may be put in domestically and facilitates this flexibility. As extra open-source fashions grow to be out there (or current ones get up to date), Ollama permits downloading/updating them accordingly.
This flexibility is essential. It permits analysts to check which fashions finest swimsuit the nuances of a specific process at hand, i.e., authorized language, monetary disclosures, or analysis summaries, all without having entry to paid APIs or enterprise-wide licenses.
There are different dimensions of the mannequin that may be modified to focus on higher efficiency for a given process/objective. These configurations are sometimes managed by a standalone file, sometimes named as “config.py,” as on this mission. For instance, the similarity threshold amongst chunks of textual content in a doc could also be modulated to determine very shut matches by utilizing excessive worth (say, larger than 0.9). This helps to scale back noise however could miss semantically associated outcomes if the brink is simply too tight for a selected context.
Likewise, the minimal chunk size can be utilized to determine and weed out very brief chunks of textual content which can be unhelpful or deceptive. Vital issues additionally come up from the alternatives of the scale of chunk and overlap amongst chunks of textual content. Collectively, these decide how the doc is break up into items for evaluation. Bigger chunk sizes enable for extra context per reply, however may additionally dilute the main target of the subject within the ultimate response. The quantity of overlap ensures easy continuity amongst subsequent chunks. This ensures the mannequin can interpret info that spans throughout a number of components of the doc.
Lastly, the consumer should additionally decide what number of chunks of textual content among the many high objects retrieved for a question must be centered on for the ultimate reply. This results in a steadiness between pace and relevance. Utilizing too many goal chunks for every question response may decelerate the software and feed into potential distractions. Nonetheless, utilizing too few goal chunks could run the chance of lacking out vital context that won’t all the time be written/mentioned in shut geographic proximity inside the doc. At the side of the completely different fashions served by way of Ollama, the consumer could configure the best setting of those configuration parameters to swimsuit their process.
Scaling for Analysis Groups
Whereas the demonstration originated within the fairness analysis house, the implications are broader. Fastened revenue analysts can load providing statements and contractual paperwork associated to Treasury, company or municipal bonds. Macro researchers can ingest Federal Reserve speeches or financial outlook paperwork from central banks and third-party researchers. Portfolio groups can pre-load funding committee memos or inside stories. Purchase-side analysts could notably be utilizing massive volumes of analysis. For instance, the hedge fund, Marshall Wace, processes over 30 petabytes of information every day equating to just about 400 billion emails.
Accordingly, the general course of on this framework is scalable:
- Add extra paperwork to the folder
- Rerun the embedding script that ingests these paperwork
- Begin interacting/querying
All these steps could be executed in a safe, inside atmosphere that prices nothing to function past native computing sources.
Placing AI in Analysts’ Palms — Securely
The rise of generative AI needn’t imply surrendering knowledge management. By configuring open-source LLMs for personal, offline use, analysts can construct in-house functions just like the chatbot mentioned right here which can be simply as succesful — and infinitely safer — than some business alternate options.
This “Non-public GPT” idea empowers funding professionals to:
- Use AI for doc evaluation with out exposing delicate knowledge
- Scale back reliance on third-party instruments
- Tailor the system to particular analysis workflows
The complete codebase for this utility is accessible on GitHub and could be prolonged or tailor-made to be used throughout any institutional funding setting. There are a number of factors of flexibility afforded on this structure which allow the end-user to implement their alternative for a selected use case. Constructed-in options about inspecting the supply of responses helps verify the accuracy of this software, to keep away from widespread pitfalls of hallucination amongst LLMs. This repository is supposed to function a information and place to begin for constructing downstream, native functions which can be ‘fine-tuned’ to enterprise-wide or particular person wants.
Generative AI doesn’t should compromise privateness and knowledge safety. When used cautiously, it may possibly increase the capabilities of execs and assist them analyze info sooner and higher. Instruments like this put generative AI straight into the fingers of analysts — no third-party licenses, no knowledge compromise, and no trade-offs between perception and safety.
