Natural Language Processing for Product Teams

We build natural language processing for product teams.
Model Development

We focus on natural language processing.

We develop and deploy custom machine learning models to process text and audio. Common use cases include text extraction, text classification, text summarization, text generation, and document processing.
Text and audio
Prediction
Category
98% confidence
Model
Select
Product Integration

Exclusively for product teams.

We partner with product managers to design the right AI solution to integrate into the product, and we obsess over the human-to-machine user experience.
Jane Wyler
Absolutely, I just put a reminder in the Slack channel [tab]
Kara Olas
Could you please remember to export all Figma for engineering to review [tab]
Experienced Team

With a proven track record.

Our team of ML engineers and data scientists have founded and operated multiple machine learning startups over the years.
Our Team
Kara Smith
Weeks 1-2
Design
Ben Clinton
Weeks 2-5
Develop
Zach Parker
Weeks 3-6
Deploy
Launch

Benefits

With Multimodal, you will reduce your time-to-market for introducing natural language processing in your product.

Fast. Projects take as little as 3 months from start to finish.

10
import torch
11
from torch import nn
12
from torch.utils.data import DataLoader
13
from torchvision import datasets
14
from torchvision.transforms import ToTensor
15
16
Launch Path
Source
Infrastructure
Integrate
Ship It

Efficient. Our experience allows us to build AI at 50% the cost of newly formed teams.

10
import torch
11
from torch import nn
12
from torch.utils.data import DataLoader
13
from torchvision import datasets
14
from torchvision.transforms import ToTensor
15
16
Product Budget
ON TARGET
$23,544 saved
ROI positive
Multimodal

Reliable. Our job is not complete until the model is embedded into your product.

10
import torch
11
from torch import nn
12
from torch.utils.data import DataLoader
13
from torchvision import datasets
14
from torchvision.transforms import ToTensor
15
16
Final Run Through
Embed Model

Our Process

We build custom end-to-end natural language processing solutions for your product.

01
Explore use case and existing product.
02
Source & annotate data.
03
Set up compute infrastructure.
04
Select base pretrained model.
05
Fine-tune model for custom use case.
06
Integrate final model into product.

Integrates with your existing software stack

The NLP solution we develop will be embedded into your product, right alongside your existing software stack.

Work with Multimodal.

Find out how Multimodal can help you build natural language processing for your product.