PROFILE RECON
Channels Console API
system online
OSINT INTELLIGENCE ENGINE

Know exactly who
you're dealing with.

Feed it a phone number, a name, a photo, and a different handle per network. It fans out across X, Instagram, Facebook, News, YouTube, phone-identity and breach databases, then hands the raw signal to the AI intelligence engine — which returns a structured brief: background, profile summary, risks, and political leaning. As JSON. Through one API call.

Run a recon → Read the API
POST /api/recon

▸ subject input

nameSuumit Shah
x@suumitshah
instagram@suumitshah
phone+91 81045·····

▸ analysis · json

01

Channels

each channel is one upstream API · select per request or run them all

02

Live console

runs against this server · no auth required

Subject

Handles — one per network

Channels

Brief
Raw JSON
Fill in the subject, pick channels, hit execute. Pre-filled example:  suumitshah
03

The API

two endpoints · JSON in, JSON out · copy & run

GET/api/channels
Lists every channel, the input it consumes (phone, name, photoUrl, or handle:<network>), and whether its API key is configured on this server.
POST/api/recon
handles are keyed per network, so the X handle can differ from the Instagram handle. channels is optional — omit it to run every configured channel. Returns the per-channel raw results, the AI analysis brief, and meta.

▸ Copyable cURL — example: suumitshah

request body
{
  "phone": "+918104515596",
  "name": "Suumit Shah",
  "photoUrl": "https://unavatar.io/twitter/suumitshah",
  "handles": {
    "x": "suumitshah",
    "instagram": "suumitshah",
    "facebook": "suumitshah",
    "youtube": "suumitshah"
  },
  "channels": ["x","instagram","news","identity"]
}
analysis schema (response.analysis)
{
  "subject": { "name", "identity_confidence",
               "location", "occupation" },
  "profile_summary": "…",
  "background": "…",
  "key_facts": ["…"],
  "online_presence": [ {channel,summary,url} ],
  "political_leaning": { "label", "confidence",
                         "evidence" },
  "risk_assessment": { "overall",
     "risks": [ {type,severity,detail} ] },
  "notable_associations": ["…"],
  "data_gaps": ["…"],
  "sources": ["…"]
}