> ## Documentation Index
> Fetch the complete documentation index at: https://docs.istari.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Find lookalikes

> Given one or more reference organizations, return similar organizations, for competitive mapping, M&A screening, or prospecting.

**Inputs:** 1–3 reference `domains` · optional `location`. **Returns:** organizations similar to the references by embedding similarity.

Use this when you have a known organization and want "more like this", competitors, acquisition targets, or lookalike prospects for an ABM list.

## The recipe

Set `ISTARI_API_KEY`, then swap in your reference `domains` and optional `country`. The request maps to `POST /v2/search` with `similar_to`.

```python theme={null}
import os
import requests

API_KEY = os.environ["ISTARI_API_KEY"]


def find_lookalikes(domains: list[str], country: str | None = "Germany") -> list[dict]:
    body = {
        "similar_to": domains,
        "excludes": domains,  # always exclude your references
        "filters": {"country": [country]} if country else {},
        "columns": ["domain", "name", "country", "nace_code"],
        "min_score": 0.55,  # raise toward 0.6 if results look loose
        "size": 20,
        "dedup": True,
    }
    resp = requests.post(
        "https://api.istari.ai/v2/search",
        headers={"x-api-key": API_KEY, "Content-Type": "application/json"},
        json=body,
    )
    return resp.json()["data"]


for row in find_lookalikes(["n26.com"]):
    print(row["domain"], "-", row["name"])
```

## What each lever does

| Lever                                                                 | Role                                                                                                                  |
| --------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------- |
| `similar_to`                                                          | The reference set. Pass 2–3 domains to define a "centroid" (e.g. three competitors) and find the cluster around them. |
| `excludes`                                                            | Removes the references from results.                                                                                  |
| `min_score`                                                           | Tightens similarity. Default `0.35` is broad; `0.55–0.65` keeps only close matches; `0.8+` near-duplicates only.      |
| `filters.country` / `filters.nace_code` / `filters.organization_size` | Optional post-filters to scope the lookalikes.                                                                        |

## Validated examples

Embedding similarity is the corpus's strongest signal — it holds for famous brands *and* unknown SMEs:

| Reference                        | Returns                                                             |
| -------------------------------- | ------------------------------------------------------------------- |
| `n26.com` (neobank)              | Trade Republic, C24 Bank, ING, Evergreen, Gini                      |
| `celonis.com` (process mining)   | Mimica, mindzie, iGrafx, ARIS, Wang Fan Xin                         |
| `bipack.it` (corrugated-box SME) | CARPACK (MX), Scatolificio Valverde (IT), Cartoembal (ES), SBC (BR) |

## Notes

* **Exclusion is by exact domain.** A reference's sister domains can still appear (here `number26.de`, N26's alternate, surfaced). Add every known domain to `excludes`.
* **Multi-domain queries are fused** with reciprocal-rank fusion: great for "organizations like A, B *and* C".
* **To steer** toward or away from concepts (boost/penalize terms, repel specific organizations), use [`find_similar_with_steering`](/mcp/tools).
* Pair with [`POST /v2/fetch`](/api/goi-fetch-stats) to hydrate full profiles for the shortlist.