SIREN Enrich (LPR)

curl -X POST https://search.leads-siren.com/enrichment-service/siren-enrich \
  -H "x-api-key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "data": {
      "siren": "340822899"
    }
  }'

import requests

resp = requests.post(
    "https://search.leads-siren.com/enrichment-service/siren-enrich",
    headers={"x-api-key": "YOUR_API_KEY"},
    json={
        "data": {
            "siren": "340822899",
            "role_types": ["Gérant", "Président"]
        }
    }
)
print(resp.json())

const resp = await fetch(
  "https://search.leads-siren.com/enrichment-service/siren-enrich",
  {
    method: "POST",
    headers: {
      "x-api-key": "YOUR_API_KEY",
      "Content-Type": "application/json"
    },
    body: JSON.stringify({
      data: { siren: "340822899" }
    })
  }
);
const json = await resp.json();
console.log(json);

{
  "siren_origine": "340822899",
  "denomination_origine": "BELMON PATRICK",
  "code_naf_origine": "86.23Z",
  "total_records": 1,
  "resultats": [
    {
      "nom_complet": "Patrick, Jean-louis Belmon",
      "qualite": "Gérant",
      "departement": "87",
      "date_naissance": "1958-04-12",
      "adresse_input": "1 RUE VICTOR SCHOELCHER",
      "code_postal_input": "87000",
      "ville_input": "LIMOGES",
      "ville_naissance": "LIMOGES",
      "score": 40,
      "statut": "Probable",
      "telephone_mobile": ["+33612345678"],
      "telephone_autre": [],
      "emails": ["patrick.belmon@example.fr"],
      "adresse_trouvee": "1 RUE VICTOR SCHOELCHER",
      "ville_trouvee": "LIMOGES CEDEX",
      "code_postal_trouve": "87000",
      "hlr_mobile": "",
      "hlr_status": "",
      "lpr_debug": {
        "breakdown": {
          "name": 20,
          "postal": 20
        },
        "candidates_considered": 3,
        "dob_eliminated": 0
      }
    }
  ],
  "credits_used": {
    "total": 96
  }
}

{
  "siren_origine": "123456789",
  "denomination_origine": "EXAMPLE SARL",
  "code_naf_origine": "62.01Z",
  "total_records": 1,
  "resultats": [
    {
      "nom_complet": "Jean Dupont",
      "qualite": "Gérant",
      "departement": "75",
      "score": 25,
      "statut": "À vérifier",
      "telephone_mobile": [],
      "telephone_autre": [],
      "emails": [],
      "lpr_debug": { "breakdown": { "name": 20, "birth_city": 5 } }
    }
  ],
  "credits_used": { "total": 1 }
}

{
  "detail": "Missing siren"
}

{
  "detail": "Invalid API key"
}

{
  "detail": "Insufficient credits (have 12, need 96)"
}

POST

enrichment-service

siren-enrich

curl -X POST https://search.leads-siren.com/enrichment-service/siren-enrich \
  -H "x-api-key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "data": {
      "siren": "340822899"
    }
  }'

import requests

resp = requests.post(
    "https://search.leads-siren.com/enrichment-service/siren-enrich",
    headers={"x-api-key": "YOUR_API_KEY"},
    json={
        "data": {
            "siren": "340822899",
            "role_types": ["Gérant", "Président"]
        }
    }
)
print(resp.json())

const resp = await fetch(
  "https://search.leads-siren.com/enrichment-service/siren-enrich",
  {
    method: "POST",
    headers: {
      "x-api-key": "YOUR_API_KEY",
      "Content-Type": "application/json"
    },
    body: JSON.stringify({
      data: { siren: "340822899" }
    })
  }
);
const json = await resp.json();
console.log(json);

{
  "siren_origine": "340822899",
  "denomination_origine": "BELMON PATRICK",
  "code_naf_origine": "86.23Z",
  "total_records": 1,
  "resultats": [
    {
      "nom_complet": "Patrick, Jean-louis Belmon",
      "qualite": "Gérant",
      "departement": "87",
      "date_naissance": "1958-04-12",
      "adresse_input": "1 RUE VICTOR SCHOELCHER",
      "code_postal_input": "87000",
      "ville_input": "LIMOGES",
      "ville_naissance": "LIMOGES",
      "score": 40,
      "statut": "Probable",
      "telephone_mobile": ["+33612345678"],
      "telephone_autre": [],
      "emails": ["patrick.belmon@example.fr"],
      "adresse_trouvee": "1 RUE VICTOR SCHOELCHER",
      "ville_trouvee": "LIMOGES CEDEX",
      "code_postal_trouve": "87000",
      "hlr_mobile": "",
      "hlr_status": "",
      "lpr_debug": {
        "breakdown": {
          "name": 20,
          "postal": 20
        },
        "candidates_considered": 3,
        "dob_eliminated": 0
      }
    }
  ],
  "credits_used": {
    "total": 96
  }
}

{
  "siren_origine": "123456789",
  "denomination_origine": "EXAMPLE SARL",
  "code_naf_origine": "62.01Z",
  "total_records": 1,
  "resultats": [
    {
      "nom_complet": "Jean Dupont",
      "qualite": "Gérant",
      "departement": "75",
      "score": 25,
      "statut": "À vérifier",
      "telephone_mobile": [],
      "telephone_autre": [],
      "emails": [],
      "lpr_debug": { "breakdown": { "name": 20, "birth_city": 5 } }
    }
  ],
  "credits_used": { "total": 1 }
}

{
  "detail": "Missing siren"
}

{
  "detail": "Invalid API key"
}

{
  "detail": "Insufficient credits (have 12, need 96)"
}

Synchronously enrich a single SIREN through the Les Pages Rouges (LPR) matching pipeline. The endpoint pulls the company’s dirigeants from Pappers, searches LPR for each person, scores every candidate against the Pappers record, aggregates the strongest signals into a golden record per person, and returns the enriched rows in one response. The response embeds a confidence score (0–180+) and a tier statut (Certain / Probable / À vérifier) per row, plus a lpr_debug.breakdown that explains which signals fired (DOB, address, name, postal, city, combo).

Choosing between /siren-enrich and /enrich-single:Both endpoints take a SIREN and return contact info for its dirigeants. They use different matching engines and have different trade-offs.

	`/siren-enrich` (this endpoint)	`/enrich-single`
Matching engine	Les Pages Rouges (LPR) + Pappers pipeline	Legacy enrichment engine
Per-row confidence score	✅ `score` + `statut` (`Certain` / `Probable` / `À vérifier`)	❌ not exposed
DOB-mismatch elimination	✅ candidates with divergent DOBs are dropped	❌ no
Siege address fallback	✅ falls back to company siege when personal address is null	❌ no
`status_filter` (`current` / `former`)	⚠️ accepted but not honored	✅ supported
Audit/debug data per row	✅ `lpr_debug.breakdown`	❌ no

Use this endpoint for new integrations — the transparent score lets you gate on Certain / Probable matches and skip paying for low-confidence ones (contacts are only charged when score > 40). Use /enrich-single when you specifically need status_filter.

Request Body

object

Show properties

string

required

The 9-digit SIREN number of the company to enrich.

string[]

Filter dirigeants by role label. Only persons whose qualite exactly matches one of the provided values are returned.Valid values: "Président", "Directeur général", "Directeur général délégué", "Gérant", "Gérant et associé indéfiniment responsable", "Président du conseil d'administration", "Membre du directoire", "Chef d'entreprise", "Administrateur", "Membre du conseil de surveillance", "Commissaire aux comptes titulaire", "Commissaire aux comptes suppléant", "Membre", "Associé indéfiniment responsable", "Autre", "Liquidateur"

string

Reserved for parity with /enrich-single. Status filtering ("current" / "former" / "all") is not propagated through the LPR pipeline today — the value is accepted and logged but has no effect on the response. Use /enrich-single if you need status filtering.

Confidence Scoring

Each candidate document is scored independently, then the best one per person becomes the golden record. Signal weights:

Signal	Weight	Notes
Date of birth match	+70	A divergent DOB eliminates the candidate entirely.
Address match	+40	Falls back to the company `siege` when the personal address is null.
Name match	+20	Punctuation-tolerant token comparison (handles `"Patrick, Jean-louis Belmon"`).
Postal code match	+20
City match	+20	Token-subset comparison — accepts `LIMOGES CEDEX` ≈ `LIMOGES`.
Birth city match	+5
Name + Postal + City combo	+20	Bonus when all three location-anchored signals fire together.

The resulting score is mapped to a statut:

Score	`statut`	Meaning
`≥ 60`	`Certain`	High confidence — contact data returned.
`40 – 59`	`Probable`	Medium confidence — contact data returned.
`< 40`	`À vérifier`	Low confidence — `telephone_mobile`, `telephone_autre` and `emails` are returned empty to avoid charging for weak matches.

Credit Cost

Dynamic — calculated after the pipeline runs and only deducted if your balance covers it:

1 credit per dirigeant returned (Pappers lookup)
10 credits per person with email found, only for rows with score > 40
100 credits per person with phone found (telephone_mobile or telephone_autre), only for rows with score > 40

If the computed cost exceeds your balance the response is still returned, but no credits are deducted (the request is logged as insufficient credits).

curl -X POST https://search.leads-siren.com/enrichment-service/siren-enrich \
  -H "x-api-key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "data": {
      "siren": "340822899"
    }
  }'

import requests

resp = requests.post(
    "https://search.leads-siren.com/enrichment-service/siren-enrich",
    headers={"x-api-key": "YOUR_API_KEY"},
    json={
        "data": {
            "siren": "340822899",
            "role_types": ["Gérant", "Président"]
        }
    }
)
print(resp.json())

const resp = await fetch(
  "https://search.leads-siren.com/enrichment-service/siren-enrich",
  {
    method: "POST",
    headers: {
      "x-api-key": "YOUR_API_KEY",
      "Content-Type": "application/json"
    },
    body: JSON.stringify({
      data: { siren: "340822899" }
    })
  }
);
const json = await resp.json();
console.log(json);

{
  "siren_origine": "340822899",
  "denomination_origine": "BELMON PATRICK",
  "code_naf_origine": "86.23Z",
  "total_records": 1,
  "resultats": [
    {
      "nom_complet": "Patrick, Jean-louis Belmon",
      "qualite": "Gérant",
      "departement": "87",
      "date_naissance": "1958-04-12",
      "adresse_input": "1 RUE VICTOR SCHOELCHER",
      "code_postal_input": "87000",
      "ville_input": "LIMOGES",
      "ville_naissance": "LIMOGES",
      "score": 40,
      "statut": "Probable",
      "telephone_mobile": ["+33612345678"],
      "telephone_autre": [],
      "emails": ["patrick.belmon@example.fr"],
      "adresse_trouvee": "1 RUE VICTOR SCHOELCHER",
      "ville_trouvee": "LIMOGES CEDEX",
      "code_postal_trouve": "87000",
      "hlr_mobile": "",
      "hlr_status": "",
      "lpr_debug": {
        "breakdown": {
          "name": 20,
          "postal": 20
        },
        "candidates_considered": 3,
        "dob_eliminated": 0
      }
    }
  ],
  "credits_used": {
    "total": 96
  }
}

{
  "siren_origine": "123456789",
  "denomination_origine": "EXAMPLE SARL",
  "code_naf_origine": "62.01Z",
  "total_records": 1,
  "resultats": [
    {
      "nom_complet": "Jean Dupont",
      "qualite": "Gérant",
      "departement": "75",
      "score": 25,
      "statut": "À vérifier",
      "telephone_mobile": [],
      "telephone_autre": [],
      "emails": [],
      "lpr_debug": { "breakdown": { "name": 20, "birth_city": 5 } }
    }
  ],
  "credits_used": { "total": 1 }
}

{
  "detail": "Missing siren"
}

{
  "detail": "Invalid API key"
}

{
  "detail": "Insufficient credits (have 12, need 96)"
}

Response Fields

The top-level payload:

Field	Type	Description
`siren_origine`	string	The SIREN you queried.
`denomination_origine`	string	Company name from Pappers.
`code_naf_origine`	string	NAF/APE activity code from Pappers.
`total_records`	integer	Number of `resultats` returned (post-filter).
`resultats`	array	One row per dirigeant; see below.
`credits_used.total`	integer	Total credits deducted for this request.

Each row in resultats:

Field	Type	Description
`nom_complet`	string	Display name from Pappers.
`qualite`	string	Role label (e.g. `Gérant`).
`departement`	string	French department code derived from input postal code.
`date_naissance`	string	Formatted DOB from Pappers (`YYYY-MM-DD`).
`adresse_input` / `code_postal_input` / `ville_input`	string	Address actually fed into the matcher (personal address, with siege fallback for `EI` rows).
`ville_naissance`	string	Birth city from Pappers.
`score`	integer	Confidence score (see table above).
`statut`	string	`Certain` / `Probable` / `À vérifier`.
`telephone_mobile` / `telephone_autre`	string[]	Phone numbers from the golden record. Empty for `À vérifier` rows.
`emails`	string[]	Email addresses from the golden record. Empty for `À vérifier` rows.
`adresse_trouvee` / `code_postal_trouve` / `ville_trouvee`	string	Address actually matched on the LPR side.
`hlr_mobile` / `hlr_status`	string	Reserved for HLR enrichment (not populated by this endpoint).
`lpr_debug.breakdown`	object	Per-signal score contributions (e.g. `{"name": 20, "postal": 20, "combo_name_postal_city": 20}`). Useful for debugging why a row landed at a given tier.

Getting Started

Search

Enrichment

Results & Downloads

Request Body

Confidence Scoring

Credit Cost

Response Fields

​Request Body

​Confidence Scoring

​Credit Cost

​Response Fields

Request Body

Confidence Scoring

Credit Cost

Response Fields