💰
company → annual_price, tier_name
Enrich SaaS Pricing
Research pricing pages for hundreds of products. Extract tier names, annual prices, and feature lists as structured columns.
$6.68 • 99.6% success • 246 products
Agent Map
Deep research across a whole dataset
An agent researches each row on the web, then analyzes what it finds. Add columns of data you don't have yet.
▶ 2-min demo video coming soon
Case studies
Give your AI a team of agents
Claude Code
claude mcp add futuresearch --scope project --transport http https://mcp.futuresearch.ai/mcp Then ask Claude to research your data.
Python SDK
pip install futuresearch
from futuresearch.ops import agent_map
result = await agent_map(
task="Find the annual price
of the lowest paid tier",
input=products_df,
response_model=PricingInfo
)Pricing
Start with $20 in free credits. No credit card required. Pay only for what you use—costs scale with research complexity.
| Task | Rows | Cost/row | Success |
|---|---|---|---|
| SaaS pricing lookup | 246 | 2.7¢ | 99.6% |
| Job classification | 200 | 0.9¢ | 100% |
| Package metadata | 300 | 1.3¢ | — |
Why costs vary
Every row gets its own web research agent. Agents have degrees of freedom. They spend more tokens doing more research for harder tasks. Simple lookups finish quickly; complex research requires multiple page visits and reasoning steps.