AI Research Infrastructure
Your Research Team Spends 60% of Its Time Assembling Data.
Not Analyzing It.
Custom AI infrastructure that encodes how your firm actually thinks. Not a SaaS dashboard. Not a chatbot.
See If It FitsWhat your current tools can't do
The Integration Gap
71% of fund managers say combining data from multiple sources is their most frustrating challenge.
Your fund subscribes to 15-40 data vendors. Each has its own dashboard, its own schema, its own entity identifiers. Your analysts are the integration layer.
Exabel 2026, 100 PMs, $2T AUM
The Missing 20%
Off-the-shelf tools handle the generic 80%. The remaining 20%, the part specific to your investment methodology, is where alpha lives.
No SaaS product will ever encode that. It requires infrastructure built around how your firm thinks.
Resonanz Capital analysis of hedge fund AI adoption
The Deliverability Problem
Rogo, Hebbia, AlphaSense: useful for quick lookups, poor at producing deliverable output.
"Doesn't actually produce anything I can submit to a client or partner." Real quote, real analyst, major bulge bracket.
Analyst reviews, Wall Street Oasis and G2
Infrastructure, not a product
We do not sell seats or dashboards. We build the infrastructure layer between your data and your team's methodology. Your analysts get structured output. Your process stays yours.
Pre-Earnings Automation
Overnight processing of management commentary, revenue trends, margin evolution, segment data, and guidance revisions across your entire coverage universe. Structured output ready before market open.
Accuracy benchmarked: 89-91% with structured data sources (Daloopa benchmark), compared to 20-71% with web search approaches.
Cross-Source Synthesis
Connect to data sources your fund already pays for: FactSet, S&P Global, Daloopa, Morningstar, Moody's, LSEG, PitchBook.
30+ financial data connectors via the Model Context Protocol (MCP), the open standard adopted by Anthropic and major data providers in 2026. One synthesis layer across all sources.
Portfolio Intelligence
13F analysis with conviction tier mapping, theme extraction, and cross-quarter rotation detection. IPO lockup tracking with insider filing correlation.
Databases have a 78% error rate on lockup dates. Automated filing analysis catches what calendars miss. Segment-level analysis at scale: the numbers inside the numbers.
Proprietary Methodology Encoding
Your firm has a way of evaluating companies that is different from every other firm. That methodology is your edge.
We encode it: your risk scoring, your sector rotation logic, your coverage frameworks, your portfolio attribution models. The output reflects how your team thinks, not how a generic model thinks.
Credibility
We built AI research infrastructure that caught the attention of Tiger Global Management in New York. Their CTO and five team members reviewed our approach to financial analysis automation. We are now focused on bringing this capability to European investment firms.
Practitioner Research
10 in-depth posts that demonstrate we understand the workflows before we write a line of code.
The 71% Problem: Why Combining Alternative Data Sources Is Still the Biggest Pain in Finance
Exabel surveyed 100 portfolio managers overseeing $2T in AUM. The top frustration was not data quality. It was data integration.
Pre-Earnings Research: From 160 Hours to Overnight Automation
A 50-stock coverage universe means 200 earnings reports per year. Most teams still process them manually.
Build vs Buy in 2026: What Analysts Actually Say About AI Research Tools
We collected analyst reviews from Wall Street Oasis, G2, and direct interviews. The pattern is clear.
What Real Analysts Say About AI Research Tools (Unfiltered)
Rogo, Hebbia, AlphaSense: useful for quick lookups, poor at producing deliverable output.
How the Smartest Money Is Rotating from AI Models to AI Infrastructure
The alpha is not in which model you use. It is in how you connect models to your proprietary data and methodology.
The Financial MCP Ecosystem: A Practitioner's Guide
30+ financial data connectors via the Model Context Protocol. Here is what actually works in production.
Want to see what this looks like for your coverage universe?
Start a ConversationHonest fit assessment
Good fit
- Independent asset managers and hedge funds with 5-50 analysts
- Firms that subscribe to multiple data vendors and struggle to synthesize across them
- Research teams that have outgrown SaaS tools and need infrastructure that reflects their methodology
- CTOs and Heads of Research evaluating build vs buy for AI research infrastructure
- PE/VC firms doing high-volume due diligence across a deal pipeline
Not a fit
- Firms looking for a cheaper alternative to Rogo or AlphaSense (we are not a SaaS tool)
- Teams that need a simple chatbot interface for ad hoc questions
- Organizations where data security policies prohibit any external AI integration
- Anyone expecting "plug and play" without a discovery phase
From first conversation to production
Discovery 1 week
We study your current research workflow: tools, data sources, team structure, output formats. We identify the 3-5 workflows where custom automation delivers the highest ROI.
No commitment. If it is not a fit, we will tell you in the first call.
Build 2-4 weeks
We connect to your existing data subscriptions (no new vendor contracts required). We encode your firm's analytical methodology into the system.
You test with real data on real positions. Not a demo dataset.
Production
Overnight automated research across your full coverage universe. Structured output in your team's preferred format, waiting before market open.
Ongoing refinement as your methodology evolves.
About
BetterAI builds custom AI research infrastructure for European investment firms.
Our team has spent 20 years building production systems. The last two, we have focused on autonomous AI agents for financial workflows: data integration, analysis automation, and overnight research pipelines.
We spent months studying how investment firms actually use AI tools. The 10 practitioner posts on this site are the result. They are not marketing content. They are proof that we understand the workflows before we write a line of code.
Based in Europe. Built for European firms who want the capabilities that New York hedge funds are building in-house, without building a 20-person AI team to get there.
Your data stays in your infrastructure. We build on your systems, not ours.
Frequently Asked Questions
What is custom AI research infrastructure?
Custom AI research infrastructure is a system built specifically for your firm's investment methodology. Unlike SaaS tools that offer the same interface to every user, custom infrastructure encodes your risk scoring, sector rotation logic, coverage frameworks, and output formats. It connects to your existing data subscriptions and produces structured analysis that reflects how your team thinks.
How is this different from Rogo, Hebbia, or AlphaSense?
Those platforms are general-purpose research tools designed for quick lookups and ad hoc queries. They handle the generic 80% well. Custom infrastructure handles the remaining 20% that is specific to your firm's methodology, the part where alpha lives. We build systems that produce deliverable output your analysts can submit to clients and partners, not just search results.
What data sources can you connect to?
We connect to data sources your fund already pays for: FactSet, S&P Global, Daloopa, Morningstar, Moody's, LSEG, PitchBook, and more. We use 30+ financial data connectors via the Model Context Protocol (MCP), the open standard adopted by Anthropic and major data providers in 2026. No new vendor contracts required.
How long does implementation take?
Discovery takes one week. We study your current workflow, tools, data sources, and output formats. The build phase takes two to four weeks: connecting to your data subscriptions, encoding your methodology, and testing with real data on real positions. You are in production within five weeks of the first conversation.
Is my data secure?
Your data stays in your infrastructure. We build on your systems, not ours. We do not store, retain, or have ongoing access to your proprietary data, positions, or research output after the build is complete.
Start a conversation
Or email us directly at
contact@letaido.it