Building Intelligence That Actually Thinks: Weekly Progress Update

Most AI startups slap GPT-4 into a chatbotand call it innovation. We're doing something fundamentally different atPlutonal, and this week's progress shows exactly why that matters.

The Problem Nobody Talks About: AI ThatForgets Everything

Here's something that'll make youappreciate the complexity of what we're building. When you ask ChatGPT aquestion, it doesn't actually "know" anything. It generates textbased on patterns. Ask it about a specific market event from last week, andunless that exact information was in its training data, it's guessing. Educatedguessing, sure, but guessing nonetheless.

This is catastrophic for financialintelligence. Markets aren't about patterns from 2023. They're about whathappened three minutes ago, how that correlates with similar events from 2008,and what institutional players are doing right now based on options flow datathat's still warm.

So we're building something called aRetrieval-Augmented Generation pipeline. Think of it like this: instead ofteaching an AI to memorise every financial fact (impossible), we're teaching itto be a brilliant researcher that knows exactly where to look and how tosynthesise information in real-time.

This Week: Training Our System onInstitutional Knowledge

We started ingesting 35+ financial researchpapers into our knowledge system. But here's where it gets interesting - we'renot just dumping PDFs into a database. We're using vector embeddings to createa semantic understanding of financial concepts.

What does that actually mean? Traditionaldatabases search for exact matches. You search for "marketvolatility" and you get documents with those exact words. Vector databasesunderstand meaning. Search for "market volatility" and it'llalso surface papers about "price fluctuations," "riskdispersion," or "uncertainty measures" because it understandsthese concepts are related in semantic space.

This is how institutional research teamswork - they don't just keyword search, they understand conceptualrelationships. That's what we're building into Plutonal's foundation.

The Challenge: When AI Agents Need toWork Together

Most people think building AI is aboutmaking one really smart model. It's not. It's about orchestration.

We completed the first full implementationof our orchestration system this week, which acts as the conductor for multiplespecialised agents. Think of it like a hospital - you don't want one doctor whoknows a bit about everything. You want specialists who collaborate on complexcases.

But getting AI agents to actuallycollaborate is genuinely difficult. Each agent needs to know when to speak up,when to defer to another agent's expertise, and how to integrate their insightswithout creating contradictions. We spent this week running trial-and-errortests to tune this orchestration.

The breakthrough came when we implementedwhat's called a "mixture of experts" system with precision scoring.Each agent doesn't just give an answer - it provides a confidence score basedon how well the question matches its domain expertise. The system then weightsresponses accordingly. It's elegant, and it mirrors how actual research teamsoperate.

Real-Time Data: The India Challenge

Getting US market data is relativelystraightforward. Getting real-time Indian market data that's actually reliable?That's a different beast entirely.

We submitted our application to an Indianmarket data provider this week for both real-time and historical data. Thismatters because most fintech platforms ignore India entirely or treat it as anafterthought. We're building dual-market intelligence from day one becauseretail investors in Mumbai deserve the same analytical capabilities as those inManhattan.

The architectural challenge here isfascinating. US markets and Indian markets operate on different time zones,different regulatory frameworks, and different liquidity patterns. Our agentsneed to understand these contextual differences when analysing cross-marketcorrelations. You can't just take a model trained on US data and apply it toIndian markets - the underlying market microstructure is fundamentallydifferent.

The Frontend: Where Intelligence MeetsExperience

Significant progress on the user interfacethis week, and this is worth explaining because it reveals our designphilosophy.

Most financial platforms overwhelm you withdata. Bloomberg Terminal syndrome - every pixel filled with numbers and charts.We're building something different. The Seek feature we completed this week isdesigned to present complex quantitative analysis in a way that doesn't requirea PhD to understand.

The technical challenge is this: our AIagents are performing sophisticated statistical analysis on the backend -Granger causality tests, volatility clustering analysis, factor modeldecompositions. But users don't want to see equations. They want insights. Sothe interface work isn't just about making things look pretty - it's abouttranslating mathematical rigour into visual clarity.

We also completed work on the Tribe pageand watchlist functionality. These aren't just cosmetic features - they'reabout creating a collaborative environment where users can share insights andtrack what matters to them without being buried in noise.

What Went Wrong: The RAG Pipeline

Let's talk about failure because that'swhere the learning happens.

We hit major issues in our RAG pipelinethis week. The system was retrieving relevant documents, but the contextmapping was off. An agent would ask about "institutional positioning intech stocks" and get back research papers about "market positioningstrategies" - related, but not quite right.

The problem was in our embedding model'schunking strategy. We were splitting documents at arbitrary boundaries (every500 tokens) rather than semantic boundaries (end of concepts or sections). Sothe system would retrieve half a thought, missing critical context.

We fixed it by implementing semanticchunking - the system now understands document structure and splits at naturalboundaries. Retrieval relevance improved by roughly 40% in our tests. This isthe kind of detail that separates production systems from demos.

The Reinforcement Learning Experiment

Here's something genuinely exciting that wecompleted this week: integrating reinforcement learning into our agentarchitecture.

Most AI systems are static. They're trainedonce and then deployed. They don't actually learn from their mistakes inproduction. We're building something different - agents that improve throughuse.

Here's how it works: when an agent providesanalysis and a user engages with that analysis (asks follow-up questions,exports the data, acts on the insight), that's a positive signal. When a userdismisses the analysis or contradicts it, that's a negative signal. Over time,the agents learn which types of analysis users find genuinely valuable versuswhich are technically correct but practically useless.

We're using experiment tracking systems,which means we can compare different agent configurations, see which performsbetter in real-world usage, and continuously improve the system. This is howyou build intelligence that actually gets smarter over time rather than juststaying at training-time performance.

Building Multiple AnalyticalCapabilities

This week we also made progress on severalspecialised analytical systems. One focuses on sentiment analysis usingmultiple natural language processing techniques - not just scanning Twitter formood, but actually understanding the semantic content of financial news,earnings calls, and regulatory filings to gauge market sentiment withprecision.

Another system we're developing handlesmacroeconomic analysis - connecting central bank policies, currency movements,interest rate changes, and fiscal policies across both US and Indian markets.The complexity here is that macro factors don't operate in isolation. A Fedrate decision ripples through currency markets, which affects emerging marketflows, which impacts Indian equity valuations. Our system needs to understandthese cascading effects.

We've also enhanced our analyticalinfrastructure with hybrid configurations across all our specialised systems.This means each one can operate independently when needed but also sharelearned insights through our knowledge graph. When one system discovers acorrelation pattern, that knowledge becomes available to the others.

Why This Matters

Everything we're building this week servesone goal: eliminating the information asymmetry that systematicallydisadvantages retail investors.

Hedge funds spend millions on Bloombergterminals, alternative data providers, and teams of quants. They can correlatedark pool activity with options flow, identify institutional positioning beforeit becomes public, and spot macro trends before they hit CNBC.

We're encoding that same analyticalcapability into AI agents that any investor can access through a simpleconversation. Not dumbed-down analysis. The actual quantitative rigour thatinstitutional players use.

That's why we're not rushing to launch.We're taking the time to build the infrastructure properly - the vectordatabases, the agent orchestration, the reinforcement learning loops, thesemantic understanding. Because when we do launch, this needs to work atinstitutional grade, not startup demo quality.

Next Week's Focus

We're diving deeper into macroeconomicanalytical capabilities and completing the sentiment analysis integration. Moreimportantly, we're beginning to connect these pieces - moving from individualsystems working in isolation to a coordinated intelligence that can handlecomplex, multi-faceted market questions.

The goal is to reach a point where youcould ask Plutonal something like "How might the recent Fed decisionimpact Indian tech stocks given the current rupee volatility?" and getback analysis that synthesises monetary policy research, currency correlationpatterns, sector-specific factors, and institutional positioning data.

That's not a chatbot response. That'sinstitutional-grade intelligence.

And we're building it for everyone.