Dawo.

The problem

Self-directed retail investors drown in data — earnings reports, analyst ratings, macro news, Reddit threads, YouTube videos — but have no way to synthesize it into a clear view of their own portfolio. They make decisions on fragments and vibes, then second-guess themselves when something moves.

The deeper problem is trust. 62% of investors use AI tools but only 23% trust them, and the gap is widening: every "AI advisor" telling people what to buy without showing the work makes the problem worse. The institutional-grade analysis (earnings quality, cash flow verification, liability stress tests) that would actually help is locked inside professional terminals (Bloomberg, FactSet) at price points retail can't touch.

The solution

Dawo flips the AI investing pattern from recommendation to research. It doesn't say "buy this"; it says "here's what's happening in what you already own, with sources, methodology, confidence, and evidence."

Core capabilities: AI-generated daily portfolio briefings (what changed, what matters, what to watch), earnings digest engine (reads full 10-K/10-Q filings and extracts only what's relevant to the user's holdings), news-to-portfolio impact mapper, and a conversational AI Analyst Chat that handles the long tail of questions a user might ask about their own portfolio — all grounded in sources the user can verify.

How it works

The user syncs their brokerage (or uploads a statement) → LLM agents ingest earnings data, SEC filings, macro signals, and benchmark data → synthesize into portfolio-level analysis with transparent reasoning chains → results surface as daily briefings, holding-level deep dives, and an AI Analyst Chat that handles freeform questions in plain language.

The AI Analyst Chat is the Universal Access Layer — a conversational interface that sits on top of the entire analysis engine. Users ask anything about their portfolio in plain language and get sourced, transparent, jargon-free answers with the methodology visible underneath. Every output cites the underlying filing, news article, or computation that produced it — no opaque "the AI thinks…" verdicts.

Who it's for

Primary buyer & end-user: the Self-Directed Retail Investor — 145M+ individuals managing their own brokerage accounts, with $10K–$500K portfolios, using Fidelity, Schwab, Vanguard, or similar. They're engaged enough to pick stocks but lack institutional-grade tools to analyze what they hold. Range: engaged beginners through experienced retail traders.

Advisor tier (B2B2C): registered investment advisors and independent financial advisors pay for the tool, run analysis on behalf of their clients, and benefit from the same transparent output and audit trail their clients increasingly demand. The advisor is buyer and primary user; the client is indirect end-user.

Why it matters

Retail investing is at record levels — >$300B annual inflows, growing 14% YoY — and the demographic curve is decisive: 30% of Gen Z is already investing in early adulthood (vs 9% Gen X, 6% Boomers). This generation expects AI-grade tools and is structurally distrustful of unsourced recommendations.

The direct addressable market is the $4.1B investment research software space (14% CAGR to $12B by 2032), but the strategic opportunity is much larger: displacing the 1–2% annual advisory fees that drive the $2.2 trillion wealth management industry. Dawo's "research with receipts" positioning is engineered to close the 23% trust ceiling that every black-box AI advisor keeps hitting — by treating transparency as the product, not a feature.

At a glance

Project

Dawo

Built by

Goutam Mahapatra

One-liner

AI portfolio intelligence with receipts — research-grade analysis, transparent reasoning, and a conversational AI Analyst for self-directed retail investors.