Your AI assistant just became a financial analyst.
Connect Bigdata.com to Claude, ChatGPT, or Copilot in one click. Every answer is grounded in earnings transcripts, premium news, filings, and broker research, with a citation you can actually check.
No credit card required. $25 in free credit included.
Your LLM is confident. That's not the same as correct.
Ask Claude or ChatGPT a financial question without grounding and it is working from two things: training data that is already stale, and a web search that skims whatever ranks rather than what is authoritative. For anything touching a live earnings call, a filing, or an analyst's actual words, that is a gap you cannot afford.
From question to cited answer in three moves.
Connect
Add Bigdata as a connector inside Claude, ChatGPT, or Microsoft Copilot. No API keys, no glue code, no engineering ticket.
Ask
Start your prompt with “Using Bigdata” and ask in plain English: an earnings digest, a competitor brief, a filing summary, a sentiment check.
Get a cited answer
Your assistant searches transcripts, filings, premium news, and broker research, then answers with citations back to the source.
“Using Bigdata, give me an earnings digest for this company's latest quarter: beat or miss against consensus, segment performance, management tone on the call, and any revised guidance. Use inline citations.”
Revenue and EPS both came in ahead of consensus [1]. Cloud led segment growth [2]. Management struck a confident tone on AI demand while flagging capacity constraints [3]. Next-quarter guidance was revised upward [1][3].
Grounded, findable, and cheaper to run.
Stop the hallucinations.
Ground any model in high-quality context. Every answer traces back to a real source, so your agents reason on facts, not guesses.
Built to be found.
Every source is indexed and discoverable. Send a query; Bigdata.com retrieves the most relevant passages to answer it, drawing on 25+ years of content.
Cut the cost of inference.
Precise retrieval means smaller context windows and fewer input tokens, so the cost of AI stops ballooning and starts falling.
Millions of entities, all connected.
Entities such as companies are linked to their units and subsidiaries, down to factories and locations, mapped continuously so a query can follow the connections, not just match keywords.
Entities
Documents
Of history
One connector. The entire financial record.
Every source is indexed, enriched, and linked by company, so your assistant isn't guessing which document matters.
Earnings transcripts
Guidance calls, analyst Q&A, and investor meetings — not just the press release.
Premium news
Financial Times, MT Newswires, Benzinga, The Economist, Alliance News and more, since 2000.
Broker research
Named-analyst notes from major and non-Western houses.
Filings
SEC filings plus international regulatory filings, as published.
Expert networks
Primary research and specialist commentary via Knowledge Ridge.
Corporate comms
Annual reports, proxy statements, shareholder letters, M&A announcements, ESG disclosures.
Podcasts
Financial and market commentary, transcribed in real time.
Your own documents
Search your files alongside everything else, in the same conversation.
Built for the questions that move your work forward.
Read every transcript so you don't have to.
Point your assistant at a company and it pulls the calls, filings, and analyst notes that matter — ranked, summarised, and cited.
“Using Bigdata, compare this company's last three earnings calls. Show how guidance language changed quarter over quarter, which topics management stopped volunteering, and how named analysts pushed back in Q&A. Flag any metric that was redefined. Output as a short memo with inline citations.”
- TranscriptGuidance language, quarter over quarter· Last three calls
- FilingSegment performance against consensus· As reported
- Broker researchNamed-analyst reaction, not aggregate sentiment· Same week
Every line traces back to a source you can open. Where the evidence isn't there, it says so instead of filling the gap.
Know their move before the press release.
Track rivals across media, expert interviews, and filings to catch a strategy shift while it is still a signal.
“Using Bigdata, track how a named competitor's strategy language has shifted over the last six months across premium news, expert interviews, and regulatory filings. Separate confirmed announcements from directional signals, and tell me where sources disagree. Output as a timeline with citations.”
- TranscriptStrategy language that quietly changed· Last two quarters
- Expert networkA third-party read from people close to the market· Recent interviews
- FilingRegulatory disclosures, as published· As filed
Every line traces back to a source you can open. Where the evidence isn't there, it says so instead of filling the gap.
Catch the narrative before consensus does.
Surface emerging themes and sentiment shifts across the whole document set, while there is still room to act.
“Using Bigdata, identify the themes gaining momentum across this sector for the current quarter. Rank them by how much coverage has accelerated rather than by raw volume, name the companies driving each one, and separate genuine shifts from single-source noise. Output as a ranked list with citations.”
- Premium newsThemes ranked by acceleration, not volume· This quarter
- TranscriptSentiment shift broken down by company· Latest calls
- PodcastCommentary you would not have read in time· This week
Every line traces back to a source you can open. Where the evidence isn't there, it says so instead of filling the gap.
Walk in already knowing what they care about.
Generate a live, sourced brief on any prospect in seconds — results, stated priorities, and the risks they flagged themselves.
“Using Bigdata, build a pre-call brief on a named prospect: latest reported results, the priorities management has stated publicly, and any operational pain points they flagged themselves. Note anything a direct competitor of theirs announced recently. Output as five bullets with citations.”
- TranscriptStated priorities, quoted from the call· Latest call
- FilingRecent results and revisions· As reported
- Premium newsPublic pain points worth opening with· Last 30 days
Every line traces back to a source you can open. Where the evidence isn't there, it says so instead of filling the gap.
We send the signal. And leave the noise out.
Independently evaluated with and without the connector: the gains land on factual accuracy and source attribution, which is exactly where financial research cannot afford to guess.
| Evaluation dimension | Claude (web search only) | Claude + Bigdata |
|---|---|---|
| Factual accuracy | 7.5 | 9.0 |
| Source quality & attribution | 7.5 | 9.5 |
| Completeness & coverage | 8.5 | 9.0 |
| Analyst coverage quality | 7.5 | 9.0 |
| Overall average | 8.4 | 8.8 |
Independent evaluation. Web search still edges ahead on stylistic dimensions like structure and polish — the gains are where research accuracy matters.
Already where you already work.
Add the connector once and it follows you across every assistant you use.
Microsoft Copilot
Bring the same grounded, cited data into Copilot alongside your work.
Integration guideBuilding your own agent or workflow instead?
Connect via APIYou're two minutes from your first grounded answer.
1
Create a free account. No credit card required.
2
$25 in free credit is applied to your account automatically.
3
Your connector activates inside Claude, ChatGPT, or Copilot immediately.
4
Start any financial prompt with “Using Bigdata” and ask away.
Remember: once connected, start your prompt with “Using Bigdata” so your assistant knows to search the grounded index instead of guessing.
Built for institutions that can't afford to guess.
Financial institutions can extend the same connector across an entire agentic platform, with SOC 2 Type II, ISO 27001, data residency controls, and a guarantee that your data is never used to train models.
Stop guessing. Start citing.
Connect Bigdata.com to your assistant and turn your next financial question into a sourced answer.
