Most industries have found a use for advanced AI products. However, the investment sector has shown a particular eagerness to embrace AI in recent years. Reports from McKinsey and Company show that between 2024 and 2025, frequent generative AI product use at finance-related businesses skyrocketed from seven to 44 percent. What’s more, nearly two-thirds of financial leaders said they planned to increase their spending on AI.
Yet McKinsey’s findings also showed an interesting trend: AI-fueled scaling seems to be stalling in the finance world. Yes, financial teams are incorporating AI into their workflows, but they haven’t been able to figure out how to leverage AI to massively expand their operation. Why? The data isn’t ready.
Take a typical mid-sized investment firm. Chances are strong the firm manages between 50 and 100 portfolio companies. Each firm report requires different data formatting, uses different fiscal calendars, represents different currencies, etc. Before any AI product can generate those insights, an analyst typically spends days normalizing spreadsheets. This makes it impossible for scalability because messy, unstructured data needs to be handled by a human first.
Limitations of traditional marketplace AI products
Why not just use an AI product to do it all? Plenty of AI tools (e.g., all those chatbots) are built to solve problems, but they aren’t necessarily built to work together. For instance, the generative AI prompts that one team member uses won’t be helpful to anyone else in the organization. On the contrary, the information that’s been fetched and analyzed by the AI software may remain siloed (e.g., stay with the team member) or simply be discarded.
To add to the complexity, most firms don’t consider how institutional knowledge factors into the equation. Remember: The expertise of senior firm professionals tends to stay with them and leave with them when they make an exit. There’s no AI to capture that knowledge, and no dashboard to derive insights from it.
Generative AI isn’t the only AI-based arena where this kind of siloing happens. Within AI agents, it occurs as well. As a result, an AI agent may serve in an assistive capacity in sales or customer support but will be limited in terms of how it affects other corporate operations.
Complicating all this is that most existing enterprise data platforms capable of solving these problems exist but can take months to install and deploy. Even then, they may not be fully ready to perform. And they’re very expensive, meaning ROI is hard to capture and investment is hard to justify.
The alternative option is building an in-house AI product, but it requires hiring and retaining a wide variety of engineers and specialists. Plus, it can take years to create a secure solution that complies with all data security requirements.
Ultimately, these real concerns have left mid-size firms too sophisticated to ignore AI but too resource-constrained to fully adopt it. This issue isn’t unsolvable, though, and DeepAuto.AI can prove it.
Integrated AI solutions across the fabric of an organization
DeepAuto has developed what it considers to be the world’s most dynamic, trustworthy enterprise “super agent” AI product. The product isn’t just a narrowly focused AI solution but a comprehensive one that’s capable of handling all of the operations for financial entities and investment firms.
What makes DeepAuto different? Speed, automation, and security. The entire stack is built on open-source technology, meaning no vendor lock-in and full deployment flex. It’s a genuine AI “brain” for business that doesn’t require months of ontology design or a team of consultants. And three of its brightest points are listed below.
1. Next-gen long-form document reasoning
Investment professionals constantly face the need to consider the information that’s available in massive documentation such as government filings, historic records, and portfolio reports. Although many AI systems use large-learning models (LLMs) to locate and parse keyword-based information from such documents, LLMs aren’t always good at going beyond basic retrieval. This is especially the case when lengthy documents include a variety of unstructured data elements such as images and tables.
DeepAuto’s product bucks this trend by dependably extracting and interpreting data that’s locked inside large documents. Then, the product goes a step further by analyzing the data against other data that’s been extrapolated from documents across the entire organization.
This capacity extends beyond financial documents. For instance, for EPC construction firms, the product analyzes thousands of dense engineering diagrams using multiple specialized vision AI models, trimming weeks of manual inspection into hours with zero misses.
Again, this is akin to the way the human brain finds connections between seemingly disparate information, but it’s happening within an organization. The system understands data and doesn’t just extrapolate it. This offers an advanced kind of data infrastructure that’s unmatched in the market.
2. Always-learning super AI agents
The second element of DeepAuto that makes it unique as a product for investment teams is the way it sees agentic AI.
Within the system, each specialist agent operates autonomously but shares context, which means no human needs to “play telephone” between departments. And the system doesn’t just respond to questions; it proactively surfaces risks and opportunities.
Businesses should not underestimate the revenue potential of having super AI agents working in the background. After all, when investment teams can make informed decisions faster without missing critical signals or experiencing exasperating manual-caused bottlenecks, they can deliver more sophisticated, consistent, and world-class customer experiences.
Over time, the system gets smarter. As the system encounters new information, it can loop this information back into its mapping rules. (This doesn’t happen with static AI solutions.) When the system sees that same information again, the product handles it automatically with near-100% accuracy.
3. Regular and reliable AI results
AI must be dependable for it to be transformative at the business level, especially in the area of asset management. DeepAuto has made its product self-evolving, and the constant feedback loops embedded in its learning models help improve the exactness of its predictions.
The agentic data lakehouse preserves all original documents so that it can fully rebuild downstream databases at any time. If a number looks incorrect, the system can trace it back to its original source.
For firms operating in regulatory industries, DeepAuto offers deployment flexibility because the whole stack is built on open-source technology. This allows it to run on server- or cloud-based systems without data leaking from the client’s environment.
Buoyed by this kind of confidence, one New York-based asset management firm with more than three billion dollars in investments has started to employ DeepAuto to move past the limitations of its slow, manual processes. Within weeks of deployment, the firm reduced its monthly reporting cycle from days to under an hour of human review time. Additionally, it increased coverage of portfolio company KPIs nearly threefold.
Built by world-class AI researchers and battle-tested engineers
Speed and security don’t come at the cost of sophistication. Led by world-class AI researcher Dr. Sungju Hwang, along with PhD-level AI scientists and more than 20 infrastructure engineers, DeepAuto has proven it’s possible to deliver financial institutions with a full-stack agentic AI platform that’s uncompromisingly fast, lightweight, and secure.
It’s understandable that a financial firm might not want to overhaul its tech stack just to get more value out of AI. With DeepAuto, they can bypass painful bottlenecks and start seeing ROI within weeks.
Real-time dashboards. Conversational AI. Automated reporting. It’s all a reality for financial firms with DeepAuto. And the firms that will lead 2026 and beyond won’t be those that adopt the most AI tools. They’ll be the ones that allow AI to do the work by being early adopters to the next-generation of AI solutions.
