Upgrade done

This is a real product leap, not a coat of paint. You moved Knowledge from “faster earnings-call reader” to “research production system.” That is the right bet.40
The old pitch was transcripts, summaries, and chat. Useful, but that category is now crowded and cheap. The new site is arguing something harder: Knowledge reads transcripts, filings, and financials together, surfaces connections and contradictions, and hands back artifacts an analyst can actually drop into a process.92
That line is the whole company:
“We don’t find the trade. We replace the two hours of data gathering so you can start your analysis. You bring the judgment. We bring the content.”
If the product delivers that, you have something. If it just says that and the output is a pretty LLM essay, you don’t.
What I like
1. You stopped selling search and started selling work product.
Primers, IC one-pagers, QoQ comps, KPI trackers, credibility scorecards, long/short memos, Excel/Word/PDF/decks. That is how buyside people actually spend their week. Chat is a means. The memo is the unit of value.92
2. The source hierarchy is professionally correct.
Filings > earnings releases > transcripts > financials > web/news is how a good analyst already thinks. Encoding it as product policy is a trust signal. Most AI research tools treat a tweet and a 10-K as peers. You didn’t.92
3. Parallel agents plus Python is the right architecture.
Splitting transcripts / filings / web, then synthesizing, and running math in code instead of letting the model invent deltas. That is the difference between a demo and something that can survive an IC grilling.92
4. The jobs-to-be-done are sharp.
“Ramp a new name before lunch.” “A week of diligence before the open.” “Cover every name like it’s your only one.” Those are real pains during earnings season, not generic “AI insights” copy.92
5. Verdict language.
Supported / Unverified / Speculative is more adult than a confident paragraph with a footnote. Analysts need a system that will say “we don’t have this.”
6. Founder-market fit still shows.
This reads like it was written by someone who has sat in the seat, not a growth team that discovered “alpha” last quarter.
Where I would press you
Accuracy is the product now.
Once you ship IC one-pagers and KPI tables, a single invented margin or mis-attributed guidance quote is not a UX bug. It is a career-risk event for the user. The upgrade only works if citations are clickable to the exact passage, numbers are recomputed from source tables, and contradictions are shown, not smoothed over.
“Parallel research agents” is no longer a differentiator by itself.
Everyone is shipping agent swarms. Your edge is domain constraints: source ranking, forensic comparison across quarters, guidance-vs-actuals, and outputs that look like research, not a chatbot. Lean on that, not the word “agents.”
The free tier still looks like the old product.
Six AI messages a month cannot demonstrate “a week of diligence” or a 20-page primer. If the upgrade is the multi-source agent workflow, the first-run experience has to be that workflow, or people will bounce before they feel the leap.40
Private data is the moat, if it is real.
Public transcripts and filings are becoming a commodity layer. The version that wins is the one that also reads the user’s own notes, models, and broker files, then still respects the source hierarchy. The original DoTadda RMS DNA matters more now, not less.
Claims vs. proof on the homepage.
The sample Retail Eyewear primer is the right idea. I’d put more before/after on the site: 90 minutes of grind vs. the package that lands, with citations visible, and one example of a flagged contradiction. Buyers in this category are allergic to vibes.
Competitive reality
You are no longer competing with “another transcript tool.” You are competing with Hebbia-style document agents, AlphaSense/Tegus AI, Bloomberg/FactSet overlays, and a PM who just pastes a 10-K into a frontier model. You win if:
cross-source contradiction catching is better than a generic long-context model

numbers are computed, not narrated

the artifact is immediately usable

the workflow is faster than “I already have ChatGPT + EDGAR”

A small team shipping this stack is either a feature or a risk. Speed and taste are the feature. Eval discipline and reliability are the risk.
Bottom line
The upgrade is conceptually strong. You changed the job from consume the call to produce the work. That is the only direction that still has pricing power.
I would treat the next 90 days as a trust sprint, not a feature sprint: citation fidelity, numeric audits, contradiction examples, and a first-run that actually shows the new system. If those hold, this is no longer a nice analyst toy. It is infrastructure for coverage.
If you want a more specific teardown, send a sample primer plus one KPI tracker with sources and I’ll mark it the way an associate would before it goes to a PM.