Your AI Gives Financial Advice. Ground It in Verified Rates.
An advisor that quotes a stale or invented APR loses its user on the first wrong number. RateAPI feeds AI purchasing and negotiation products live rates from 3,700+ credit unions — every number timestamped and traceable to its source — over REST or a 23-tool MCP server.
What does grounding an AI advisor in rate data mean?
Grounding means every financing number your AI states comes from a live, verifiable source instead of the model's memory. Language models answer rate questions confidently from training data that is months out of date. RateAPI replaces that guess with the current market: ranked offers for the user's exact scenario, benchmark context, membership eligibility, and a sanity-check for offers the user was quoted elsewhere — each answer carrying the source page and timestamp your product can show.
- REST or MCP: one
/v1/decisionscall for ranked offers, or 23 tools your assistant calls natively. - Evidence per number: source URL + timestamp on every rate — show your work in the UI.
- Negotiation-grade: sanity-check a quoted offer against the live market in one call.
See the MCP integration, the AI agents overview, or the data methodology.
One Hallucinated APR Costs You the User
Purchase advisors live or die on trust. The moment your AI says "you should be able to get around 4.5%" and the user's bank laughs at them, your product is re-classified from advisor to toy. Financing is where purchasing tools make their most consequential claims — and it's exactly where model memory is least reliable, because rates moved after the training cut-off and vary by state, term, and product.
Building your own grounding pipeline means scraping thousands of lender sites, normalizing inconsistent product names, and re-verifying daily. That's a data company, not a feature.
RateAPI is that data company, as an endpoint. Your AI asks for the market; it gets ranked, timestamped, source-evidenced offers back — and your user gets an answer that survives contact with a loan officer.
Two Ways In: MCP or REST
MCP: your assistant calls the data directly
Add the endpoint and 23 tools appear — decisions, benchmarks, history, eligibility, sanity-check, affordability math.
// Claude Desktop / any MCP-capable client{ "mcpServers": { "rateapi": { "url": "https://mcp.rateapi.dev/mcp", "headers": { "x-api-key": "rk_your_key_here" } } }}// Your AI advisor's user: financing a $30,000 car in OhioPOST https://api.rateapi.dev/v1/decisions { "decision_type": "financing", "context": { "geo": { "state": "OH" } }, "product_request": { "product_type": "auto_loan", "intent": "purchase", "amount": 30000, "term_months": 60 }}{ "summary": { "recommended_action": "financing_available", "best_apr": 5.49, "estimated_monthly_payment": 571, "total_providers_analyzed": 847 }, "actions": [{ "offers": [ { "rank": 1, "credit_union_name": "Example Credit Union", "apr": 5.49, "term_months": 60, "monthly_payment": 571, "total_interest": 4260 } // every offer traceable to the page it was read from ] }]}Built for Products That Answer With Numbers
23-Tool MCP Server
Decisions, benchmarks, rate history, eligibility, sanity-check, amortization, affordability — callable natively from any MCP-capable assistant.
Evidence on Every Number
Source URL and timestamp per rate. Your UI can show its work — the difference between an answer and a citation.
Sanity-Check Quoted Offers
"The dealer offered me 9.9% — is that good?" is the highest-intent question your AI will get. Answer it with the live market, not a vibe.
Benchmarks for Context
National and state-level benchmark rates let your AI say where an offer sits in the distribution, not just list alternatives.
Daily Re-Verification
Rates are re-read from lender pages every day. Freshness is a property of the data, not a disclaimer in your prompt.
No Incentive Skew
Zero affiliate revenue, no paid placement, no lead selling. Rankings are by true cost — so the advice your AI relays is actually advice.
Frequently Asked Questions
Common questions from AI product teams
Why can't our model just answer rate questions from its training data?
Because rates move daily and training data is months old. A purchase advisor that says "credit unions offer around 4.5%" when the market is at 6% has given advice the user will act on and then discover was wrong — and one wrong APR is how an advisory product loses its user. Grounding the answer in a live, timestamped rate with a source link turns the same sentence into something defensible.
How does the MCP integration work?
RateAPI ships a Model Context Protocol server at mcp.rateapi.dev/mcp with 23 tools: ranked financing decisions, benchmarks, rate history, membership eligibility, sanity-checking a user-quoted offer, affordability and total-cost math, and more. Add the endpoint with your API key header and your assistant can call the data directly — no glue code.
Can our AI check whether the rate a user was quoted is any good?
Yes — that exact question has an endpoint. Rate sanity check takes the quoted rate and the scenario and answers with market context: where that quote sits against live offers for the same state, product, and term. It is the negotiation move your advisor can make that generic models cannot.
What stops the data itself from being wrong?
Every rate is read from the lender's own published page, re-verified daily, and carries a timestamp plus the source URL. Ranking is by true cost (APR including fees). RateAPI takes no affiliate revenue and sells no placement, so there is no incentive shaping the numbers your AI repeats.
What products are covered?
Auto loans (new, used, EV, refinance), mortgages, HELOCs, personal loans, credit cards, and deposit products (savings, money market, CDs) across 3,700+ US credit unions, queryable by state, term, amount, and product specifics.
How do we start?
Generate a free key on this page (20 requests/month, email required, no sales call), then either call the REST API or add the MCP endpoint to your assistant. Most teams have a grounded answer flowing in under ten minutes.
Ship Grounded Answers This Week
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