About
An AI investment team, working in the open
ThesisLoop publishes AI-generated investment theses on public companies — covering management credibility, business model, growth, and risk, plus how macro events map to each company. Every insight is traced back to the company's own primary-source filings, so you can open the document and check any claim yourself.
Who operates ThesisLoop
ThesisLoop is built and operated by SIGNALSTACKS TECHNOLOGIES PRIVATE LTD. We're a small team building research tooling for investors who want source-backed analysis instead of black-box scores. Our focus is Indian public equities, with every claim tied to a document you can open and check.
What ThesisLoop does
We turn dense company disclosure into a structured, cited thesis you can read in minutes — and verify line by line.
Five research pillars
Management Credibility, Business Model, Future Growth, Risk, and macro Scenarios — each analyzed by a dedicated pipeline.
Cited to the source
Every insight links back to the annual report, concall transcript, or investor deck it was drawn from.
Conviction at a glance
A per-pillar and blended conviction read summarizes where the evidence points — with the reasoning shown, not hidden.
From filings to thesis
Analysis is AI-generated from primary sources, end to end — we're explicit about that, and every claim stays verifiable against its source.
1. Read primary sources
Our pipelines ingest a company's own annual reports, quarterly concall transcripts, and investor presentations — not opinion or rumor.
2. Run five analysis pipelines
Management Credibility, Business Model, Future Growth, Risk, and macro Scenarios each extract findings and cite them back to the source document.
3. Curate and score
Extracted findings are grouped, ranked, and scored 0–100 per pillar — with each finding kept traceable to the document it came from.
4. Publish with citations
Every insight in a published thesis links to the exact document it came from, so you can verify it yourself.
Read the full research methodology for the pipelines, scoring rubric, and limitations.
Contact
Questions, corrections, or press? Email us at karan@thesisloop.ai. We take accuracy seriously — if a thesis misreads a source, tell us and we'll look into it.
Important disclaimer
AI-generated research for informational and educational purposes only — not personalized investment advice, a stock recommendation, or a guarantee of returns. ThesisLoop is not a SEBI-registered investment adviser or research analyst. Do your own diligence and consult a registered professional before making any investment decision.