The short answer
Use Perplexity when the deliverable is the answer itself. "What's the market size for AI coding tools?", "How does GLP-1 demand compare across emerging markets?", "Summarise this paper." Perplexity is excellent at fetching, synthesising, and citing — and the conversational UI is fast and clean.
Use Smalt AI when the deliverable is the artefact built on top of the research — a financial model, a 12-slide pitch deck with right-rail commentary, an outbound campaign with contacts pulled and emails drafted, a due-diligence memo with structured sections. Smalt AI does the research and then constructs the deliverable end-to-end.
Capability comparison
| Capability | Perplexity | Smalt AI |
|---|---|---|
| Research with citations | Excellent — that's the core feature. Citations on every claim, fast. | Strong — paragraph-level citations to filings, transcripts, news. Same fidelity, slightly slower because the engine is doing more. |
| Financial models (DCF, LBO, three-statement) | Not in scope — Perplexity returns text answers, not workbooks | Native .xlsx with 14 sheets, mid-year convention, three sensitivity tables, scenario INDEX |
| Pitch decks (.pptx) | Not in scope — text answers only | Native consulting-grade .pptx with insight titles, right-rail commentary, hero stat layouts |
| Long-document analysis (10-Ks, transcripts) | Strong — handles uploaded PDFs and web links well | Strong — Claude 1M context for entire 10-Ks plus four quarters of transcripts in one pass |
| Email drafting and contact enrichment | Limited — answers questions, doesn't draft outbound | Composio-integrated tools for contact lookup and outbound draft generation (queued for review, not auto-sent) |
| Excel round-trip (upload, edit, return) | No — research engine | Yes — bring your own workbook, ask for changes, get the file back |
| Pricing | Free tier; Pro $20/mo; Max $200/mo; Enterprise from $40/seat/mo | Free 500 credits (no card); Basic ~$18/mo; Pro ~$45/mo; Enterprise self-host |
| Models | OpenAI / Anthropic / their own Sonar models — selectable | Anthropic Claude (Sonnet, Opus, 1M context, Haiku) + Gemini for fast classification |
Where Perplexity is the right tool
For fast, sourced answers, Perplexity is excellent. The interface is built for it: type a question, get a paragraph with citations, follow up. The Pro tier with reasoning models is genuinely strong on multi-step research questions — better in many cases than ChatGPT's browse mode.
For academic and journalistic research, where the citations are the point and you'll write the synthesis yourself, Perplexity is purpose-fit. The output is the inputs to your work, not the work itself.
For quick due-diligence skims — getting context on a name, a market, a deal, a person — Perplexity gives you sourced context fast. Useful as a first-pass orientation before deeper work.
Where Smalt AI is the right tool
For finance deliverables, Smalt AI does work Perplexity isn't built for. You can ask Perplexity for "the case against this thesis" and get a sourced text answer. You can ask Smalt AI for the same and get back a structured memo with executive summary, sectioned bullets with paragraph-level sources, an open-questions list, and (if you ask) a model with the bear-case downside scenario built in.
For build-the-artefact workflows — research → model → deck → outbound — Smalt AI is one chat with one coworker. With Perplexity, you research; the build is on you to do elsewhere.
For data residency / self-hosting, Smalt AI's Enterprise tier supports self-hosting on your infrastructure. Perplexity Enterprise is hosted; tenant isolation is strong, but full data residency on-prem is not the same option.
How they fit together
Several finance professionals already use both. Perplexity for fast research lookup during the day — quick context, fact-checking, market sizing. Smalt AI when the workflow needs to land in a real deliverable: a model for the IC, a memo for the partner, a deck for the client.
A typical pattern: Perplexity to ground yourself on a market in 5 minutes; Smalt AI to build the LBO and the deck for the deal team meeting later that week. The first is reconnaissance; the second is construction.
Honest caveats
- Perplexity is faster at the simple research lookup. If you only need a paragraph with citations, Smalt AI does it but is doing more under the hood.
- Smalt AI's research outputs are more structured (briefs, memos, models) but require you to ask for the structure. Perplexity gives you a great paragraph by default.
- Both products use frontier LLMs. The differentiation is workflow shape, not model raw power.
The summary
Perplexity is the best research engine with citations. Smalt AI is the AI virtual employee that does research plus build — research with citations, then constructs the model, deck, or memo on top. Most finance professionals will use Perplexity for daily lookup and Smalt AI for the work that ends in a deliverable.
500 free credits, no credit card. Try Smalt AI on a real workflow and see for yourself.