🧠 Deep-moat people intelligence — lead with these
Sourced evaluation of real people & companies
These are the methods that deepen your moat — a bare model can't fake them. Below are real, sanitized request and response shapes. No key needed to read them; get a free key to run live calls. The commodity scrapers further down are included table-stakes — useful, but anyone can wire them.
▶ Try it live — free, no key. 3 runs per visitor.
score_intent▶ live · free
POST /v1/score_intent
Score the real buying intent of a message. Edit the text, then run it live.
Input
compare_subjects▶ live · free
POST /v1/compare_subjects
Compare two real people side by side on sourced evidence. Two well-known names pre-filled.
Subjects
find_contact▶ live · free
POST /v1/find_contact
Resolve a named person to a verified identity and reachable contact. Run it live.
Name
Company (optional)
candidate_compare5 credits / candidate
POST /v1/candidate_compare
Rank 2–10 candidates side by side in a hiring frame, on sourced evidence.
Sample request
{
"role": "Senior Backend Engineer (payments)",
"candidates": [
{ "name": "Maria Alvarez", "company": "Northwind Pay" },
{ "name": "Devin Okafor", "company": "Ledgerly" },
{ "name": "Sana Iqbal", "company": "Fintech Labs" }
]
}
Sample response
{
"success": true,
"role": "Senior Backend Engineer (payments)",
"ranking": [
{ "rank": 1, "name": "Maria Alvarez", "fit_score": 87,
"strengths": ["8y payments infra", "led PCI-DSS migration"],
"watch": ["no public open-source footprint"] },
{ "rank": 2, "name": "Sana Iqbal", "fit_score": 79,
"strengths": ["ledger systems", "high-scale APIs"],
"watch": ["shorter tenure"] },
{ "rank": 3, "name": "Devin Okafor", "fit_score": 71,
"strengths": ["strong distributed-systems writing"],
"watch": ["payments depth unclear from sources"] }
],
"dimensions_scored": 30,
"sources_cited": 24,
"credits_used": 15
}
exec_compare5 credits / exec
POST /v1/exec_compare
Compare 2–10 executives in a leadership / competitor-exec frame, on sourced evidence.
Sample request
{
"context": "Heads of Product at competing analytics startups",
"execs": [
{ "name": "Jordan Pierce", "company": "Quanta" },
{ "name": "Lena Voss", "company": "Mapline" }
]
}
Sample response
{
"success": true,
"comparison": [
{ "dimension": "Track record",
"Jordan Pierce": "Shipped 3 GA products, 2 acquired",
"Lena Voss": "Scaled one product 0→$30M ARR" },
{ "dimension": "Public thought leadership",
"Jordan Pierce": "Frequent conference speaker",
"Lena Voss": "Active long-form writer" },
{ "dimension": "Team scale led",
"Jordan Pierce": "~40 PMs/designers",
"Lena Voss": "~18, fast-growing" }
],
"dimensions_scored": 30,
"sources_cited": 19,
"credits_used": 10
}
vet_person30 cold / 0 on cache
POST /v1/vet_person
Sourced screening dossier on a person — history, affiliations, red flags. Cache hits are free and sync; cold runs are async (202 + poll).
Sample request
{ "name": "Aaron Whitfield", "company": "Beacon Robotics" }
Sample response
{
"success": true,
"subject": { "name": "Aaron Whitfield", "title": "VP Engineering",
"company": "Beacon Robotics" },
"history": [
{ "org": "Beacon Robotics", "role": "VP Engineering", "years": "2022–present" },
{ "org": "Cirrus Automation", "role": "Eng Manager", "years": "2017–2022" }
],
"affiliations": ["IEEE member", "advisor, MakerGrid (nonprofit)"],
"red_flags": [],
"confidence": "high",
"sources_cited": 16,
"share_url": "https://www.mentionfox.com/r/sample",
"credits_used": 30
}
candidate_evaluateasync · 0 if unfindable
POST /v1/candidate_evaluate
Deep multi-source evaluation of one candidate vs a role — 30-dimension sourced scorecard. Returns 202 + a job id to poll.
Sample request
{ "name": "Priya Nadar", "role": "Staff Data Scientist" }
Sample response (202 → then poll /v1/jobs/{id})
{
"success": true,
"subject": "Priya Nadar",
"role": "Staff Data Scientist",
"overall_fit": 82,
"scorecard": [
{ "dimension": "ML depth", "score": 9, "evidence": "3 first-author papers" },
{ "dimension": "Production deployment", "score": 7, "evidence": "owned 2 models in prod" },
{ "dimension": "Communication", "score": 8, "evidence": "clear public writing" }
],
"dimensions_scored": 30,
"sources_cited": 21,
"credits_used": 30
}
compare_people5 credits / subject
POST /v1/compare_people
Rank 2+ people side by side on 30 dimensions, context-aware.
Sample request
{
"context": "Potential design-agency partners",
"people": ["Theo Marsh", "Camila Reyes"]
}
Sample response
{
"success": true,
"dimensions": [
{ "name": "Portfolio breadth", "Theo Marsh": "B2B SaaS focus",
"Camila Reyes": "Consumer + brand" },
{ "name": "Network density", "Theo Marsh": "Medium",
"Camila Reyes": "High" }
],
"credits_used": 10
}
get_investor_report30 / free on thin data
POST /v1/get_investor_report
Verified investor / firm track-record snapshot.
Sample request
{ "firm": "Meridian Ventures" }
Sample response
{
"success": true,
"firm": "Meridian Ventures",
"focus": ["seed", "B2B infra", "developer tools"],
"notable_investments": ["Stacktrace", "Ledgerly", "Northwind Pay"],
"typical_check": "$0.5M–$2M",
"confidence": "medium",
"sources_cited": 12,
"credits_used": 30
}
Want to run these live? Get a free key · or connect over MCP from the Augment page. Browse the commodity table-stakes catalog below — no key needed.