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.