AlphaSense reads the documents — Valuein delivers the auditable numbers.
AlphaSense is an AI search platform over ~500M documents (transcripts, expert/broker research, filings, news) with an MCP-first research agent. It's a different core competency from Valuein: qualitative document discovery vs structured, point-in-time numeric fundamentals you can backtest and audit.
The short version, including where AlphaSense is the better pick.
AlphaSense is unmatched for reading and searching qualitative content, but a quant can't backtest a factor on a document corpus. Valuein delivers the structured, point-in-time, survivorship-free numbers — each with a fact_id back to its filing — self-serve from $49/mo versus AlphaSense's enterprise seat pricing.
Search transcripts & expert content: Not our focus.
AlphaSense has unmatched qualitative breadth — transcripts, expert calls (Tegus), broker research, news
Analysts and PMs who use a document-search platform like AlphaSense for qualitative work and need a self-serve, auditable, point-in-time numeric fundamentals source to complement it.
What you get done, not just a feature checklist.
Grouped by what you are trying to do. Scroll sideways on a phone.
| Feature | Valuein | AlphaSense |
|---|---|---|
| Data integrity and audit | ||
| Backtesting | Survivorship-free PIT to 1993 | Not a backtesting data source |
| Per-fact Audit Trail | fact_id → SEC filing | Document-level citations |
| Agents and workflow | ||
| Agent Access | MCP for numeric tools + safety | MCP-first generative document search |
| Coverage and depth | ||
| Numeric Fundamentals | 120.5M+ point-in-time facts | Document search, not a numeric dataset |
| Access and pricing | ||
| Access | Self-serve, from $0/$49/mo | Enterprise seats, sales-led |
AlphaSense details and pricing are based on publicly available information as of June 2026 and may have changed; verify on their site. Where a competitor's figure is vendor-reported or estimated, we say so.
Install the SDK and read a company with no token and no card: the free sample runs the same code your plan will. Add a key later and nothing else changes.
from valuein_sdk import ValueinClient with ValueinClient() as client: # What a ticker or CIK resolves to. A CIK also reaches delisted companies. print(client.resolve(["AAPL", 320193])) # One company's rows from a table, with the point-in-time filter applied. facts = client.read_table( "fact", tickers="AAPL", columns=["standard_concept", "period_end", "fiscal_period", "numeric_value"], limit=10, ) print(facts) # Named SQL templates. Pass parameters as keyword arguments. print(client.list_templates()[:5]) df = client.run_template("fundamentals_by_ticker", ticker="AAPL") print(df.head()) # Links to the SEC filings behind the numbers. links = client.filing_links("AAPL", form_types=["10-K"], limit=3) print(links[["form_type", "filing_date", "document_url"]]) # Restatement Radar: figures a later filing changed (every plan). changed = client.restatements(ticker="AAPL", limit=5) print(changed[["standard_concept", "period_end", "first_value", "current_value", "disclosure_class"]])They solve different problems. AlphaSense is for searching qualitative documents — transcripts, expert calls, research. Valuein is for structured, point-in-time numeric fundamentals you can backtest and audit. Many research workflows use both: AlphaSense for the narrative, Valuein for the numbers.
Yes — Valuein's MCP exposes point-in-time fundamentals as agent tools, each figure carrying a fact_id back to its filing, with temperature-0 runs and human-in-the-loop guardrails so the numeric layer of an AI report is auditable.
Start with the free sample. No card, no sales call; pricing is public.