Can AI Handle KYC? Grounding LLMs For Due Diligence Tools

A developer introduced the Portfolio Investigate API, a tool that grounds LLM outputs for KYC and due diligence by providing structured domain dossiers. The API aggregates WHOIS, IP geolocation, company registries, and sanctions data into a single report, with an LLM Ask endpoint for grounded follow-up questions. The developer demonstrated how to use the API to reduce hallucination risks in compliance tools.

ai kyc compliance duediligence api llm fintech rapidapi ChatGPT can spin up a KYC dashboard in an afternoon. It will generate React components, SQL schemas, and swagger documentation that look production-ready. But ask it whether fintech-example.io is a legitimate payment processor or a sanctions-evasion shell, and it will confidently fabricate ownership records, misread registrar data, or hallucinate a clean bill of health. That is the gap AI cannot close on its own: grounding . Large language models reason over tokens, not truth. A reliable due-diligence or compliance tool must anchor every LLM answer in real, verifiable, timestamped data—WHOIS records, IP geolocation, company registries, email infrastructure, and sanctions lists. This article shows how to use the Portfolio Investigate API to feed your AI agents factual domain dossiers and compliance verdicts, turning a prototype into something a compliance officer can actually trust. LLMs are autocomplete engines. They predict what words should come next based on training data, not live facts. In a KYC context, that creates three failure modes: The fix is not to abandon LLMs. It is to constrain them: give them a structured evidence packet first, then let them summarize, classify, and answer natural-language questions on top of it. That evidence packet is exactly what Portfolio Investigate API returns. Portfolio Investigate API is a one-call domain investigation report. It aggregates five underlying portfolio APIs into a single dossier: The response includes a plain-English verdict and an LLM Ask endpoint so you can ask follow-up questions grounded in the dossier rather than in the model's imagination. Let us start with the core report. The API returns a structured dossier you can store, display, or pass to an LLM as context. curl -X GET "https://portfolio-investigate.p.rapidapi.com/investigate/fintech-example.io" \ -H "X-RapidAPI-Key: $RAPIDAPI KEY" \ -H "X-RapidAPI-Host: portfolio-investigate.p.rapidapi.com" A trimmed response might look like this: { "domain": "fintech-example.io", "risk score": 42, "verdict": "MODERATE RISK", "summary": "Domain registered 6 months ago via privacy-protected WHOIS. Server hosted in a jurisdiction different from the stated company address. No sanctions matches found.", "whois": { "created": "2023-11-14", "registrar": "NameCheap, Inc.", "privacy": true, "name servers": "dns1.registrar-servers.com" }, "ip geo": { "country": "NL", "asn": "AS49981" }, "company": { "name": "Fintech Example B.V.", "jurisdiction": "Netherlands", "registry url": "..." }, "sanctions": { "matches": } } The verdict and risk score give your compliance UI an immediate signal. The underlying objects give an LLM the evidence it needs to explain why . You can subscribe to the API on RapidAPI: see RapidAPI . The GitHub repository with examples and client wrappers is at https://github.com/On13uka/portfolio-api . Here is a minimal Python workflow that fetches a dossier, then asks a natural-language question against it using the LLM Ask endpoint. python import requests import os RAPIDAPI KEY = os.environ "RAPIDAPI KEY" HOST = "portfolio-investigate.p.rapidapi.com" BASE = f"https://{HOST}" headers = { "X-RapidAPI-Key": RAPIDAPI KEY, "X-RapidAPI-Host": HOST, } def get dossier domain: str - dict: url = f"{BASE}/investigate/{domain}" r = requests.get url, headers=headers, timeout=30 r.raise for status return r.json def ask about domain domain: str, question: str - str: url = f"{BASE}/ask" payload = { "domain": domain, "question": question, } r = requests.post url, json=payload, headers=headers, timeout=60 r.raise for status return r.json "answer" Example usage domain = "fintech-example.io" dossier = get dossier domain print dossier "verdict" , dossier "risk score" answer = ask about domain domain, "Is this domain safe to onboard as a payment partner? List the specific risks." print answer The POST /ask endpoint is the key to safe LLM use. Instead of asking an open model a vague question, you are asking a model that has been fed the dossier as context. The answer is grounded in the WHOIS, IP, company, email, and sanctions data the API just retrieved. You can go further by combining the API with an orchestration layer. A simple compliance agent might: risk score 70 , flag for manual review. risk score <= 70 , use POST /ask to generate a structured rationale. php def review merchant merchant name: str, domain: str - dict: dossier = get dossier domain if dossier "risk score" 70: return { "decision": "MANUAL REVIEW", "reason": f"High risk score: {dossier 'risk score' }", "evidence": dossier, } rationale = ask about domain domain, "Write a one-paragraph compliance rationale for approving this merchant." return { "decision": "AUTO APPROVED", "rationale": rationale, "evidence": dossier, } Because the dossier is preserved, every automated decision is auditable. A regulator or internal reviewer can trace the rationale back to real data points, not to a black-box model. AI will not build a working KYC tool for you. It will scaffold the UI, draft the prompts, and speed up your iteration. But the hard part of compliance—verifying facts, cross-referencing records, and producing auditable verdicts—still requires a reliable data layer. The Portfolio Investigate API provides that layer. With one call you get a unified dossier drawn from WHOIS, IP geolocation, company registries, email infrastructure, and sanctions lists. With the POST /ask endpoint you can let an LLM reason over that evidence without drifting into hallucination. If you are building an AI-powered due-diligence or compliance product, start by grounding the model. Get the API at see RapidAPI and explore the sample code at https://github.com/On13uka/portfolio-api .