Update README.md

README.md

@@ -100,6 +100,14 @@ By default, caching is enabled. If you want to use the method with multiple diff
     natas.is_correctly_spelled("cat") #The result will be served from the cache
     natas.is_correctly_spelled("cat", cache=False) #The word will be looked up again
 
+# Business solutions
+
+
+<img src="https://rootroo.com/cropped-logo-01-png/" alt="Rootroo logo" width="128px" height="128px">
+
+Non-standard historical or OCRed data can be a mess to deal with when you want to squeeze all the juice out of your corpora. Let us help! [Rootroo offers consulting related to non-standard data](https://rootroo.com/). We have a strong academic background in the state-of-the-art AI solutions for every NLP need. Just contact us, we won't bite.
+
+
 # Cite
 
 If you use the library, please cite one of the following publications depending on whether you used it for normalization or OCR correction.
@@ -143,4 +151,4 @@ Mika Hämäläinen, and Simon Hengchen. 2019. [From the Paft to the Fiiture: a F
     doi = "10.26615/978-954-452-056-4_051",
     pages = "431--436",
     abstract = "A great deal of historical corpora suffer from errors introduced by the OCR (optical character recognition) methods used in the digitization process. Correcting these errors manually is a time-consuming process and a great part of the automatic approaches have been relying on rules or supervised machine learning. We present a fully automatic unsupervised way of extracting parallel data for training a character-based sequence-to-sequence NMT (neural machine translation) model to conduct OCR error correction.",
-    }
+    }