The No-Code
AI automations platform

An integrated framework of no-code, low-code, and out of the box generative AI solutions to build AI search engines, assistants, chatbots, and automations.

An ecosystem to build, deploy,
and manage AI applications

An ecosystem to build, deploy and manage AI applications

Empowering development with no-code and code SDK

VectorShift combines a user-friendly No-code interface with a robust Code SDK. Effortlessly create applications using drag-and-drop, or dive into coding with seamless IDE integration. Enjoy flexibility and power, all in one platform.

VectorShift combines a user-friendly No-code interface with a robust Code SDK. Effortlessly create applications using drag-and-drop, or dive into coding with seamless IDE integration. Enjoy flexibility and power, all in one platform.

Instruction text

Describe this file to me

File input

JSON, CSV, PDF

OpenAI LLM

Model: gpt-4.0-turbo (processes input)

File loader

Reads the input file

Result

Generates output

pipeline_setup.py

from vectorshift.node import *

from vectorshift.pipeline import *

file_node = InputNode(name='file_input', input_type='file')

model_text_node = TextNode(text='Describe this file to me.')


fileloader_node = FileLoaderNode (file_input=file_node.output())

llm_node = OpenAI_LLMNode(

model='gpt-4.0-turbo',

system_input=model_text_node.output(),

prompt_input=fileloader_node.output()

)


output_node = OutputNode(

name='my_output',

output_type='text',

input=llm_node.output()

)

No-code

Build and deploy powerful applications with drag and drop components and customizable deployment interfaces. No coding required.

OpenAI LLM

Model: gpt-4.0-turbo (processes input)

File input

JSON, CSV, PDF

File loader

Reads the input file

Instruction text

Describe this file to me

Result

Generates output

Code SDK

Access all functionality of the VectorShift platform through your IDE through simple, intuitive APIs. Complete interoperability between No-code and Code SDK.

pipeline_setup.py

from vectorshift.node import *

from vectorshift.pipeline import *

file_node = InputNode(name='file_input', input_type='file')


model_text_node = TextNode(text='Describe this file to me.')


fileloader_node = FileLoaderNode (file_input=file_node.output())


llm_node = OpenAI_LLMNode(

model='gpt-4.0-turbo',

system_input=model_text_node.output(),

prompt_input=fileloader_node.output()

)


output_node = OutputNode(

name='my_output',

output_type='text',

input=llm_node.output()

)

Integrations and automations

Live-sync, set up action based triggers (e.g., receive an email), and automate actions (e.g., send a slack message) across your tool stack

Google Drive
OneDrive
SalesForce
Hubspot
Notion
Airtable

Integrations and automations

Live-sync, set up action based triggers (e.g., receive an email), and automate actions (e.g., send a slack message) across your tool stack

Google Drive
OneDrive
SalesForce
Hubspot
Notion
Airtable

Integrations and automations

Live-sync, set up action based triggers (e.g., receive an email), and automate actions (e.g., send a slack message) across your tool stack

Google Drive
OneDrive
SalesForce
Hubspot
Notion
Airtable

Large language models

Access the latest models through the VectorShift platform

OpenAI
Anthropic
Huggingface
Google
LLAMA
AWS
Mistral AI_

Large language models

Access the latest models through the VectorShift platform

OpenAI
Anthropic
Huggingface
Google
LLAMA
AWS
Mistral AI_

Large language models

Access the latest models through the VectorShift platform

OpenAI
Anthropic
Huggingface
Google
LLAMA
AWS
Mistral AI_

Leverage AI throughout your
company and products

Assistants

Assistants

Assistants

Integrate natural language search and live-sync databases such as Notion and Airtable to automate information retrieval.

Integrate natural language search and live-sync databases such as Notion and Airtable to automate information retrieval.

Integrate natural language search and live-sync databases such as Notion and Airtable to automate information retrieval.

+64

When was this contract started?

20230329-Product-Contract-Acme.pdf

The contract started on January 1, 2023.

When was this contract last modified?

20230329-Product-Contract-Acme.pdf

The contract was last modified by John D. on June 13, 2023. The modifications were done on page 3,4 and 16.

What’s the contract ceiling?

20230329-Product-Contract-Acme.pdf

The contract ceiling is USD$1,000,000.

Chatbot

Chatbot

Chatbot

Prototype, customize, and deploy a customer facing chatbot in minutes. Use cases including customer support, onboarding flow, lead collection, and white-glove advisory.

Prototype, customize, and deploy a customer facing chatbot in minutes. Use cases including customer support, onboarding flow, lead collection, and white-glove advisory.

Prototype, customize, and deploy a customer facing chatbot in minutes. Use cases including customer support, onboarding flow, lead collection, and white-glove advisory.

Workflow Automation

Workflow Automation

Workflow Automation

Workflow Automation

Automate the creation of marketing copy, personalized outbound emails, call summaries, and graphics at scale.

Automate the creation of marketing copy, personalized outbound emails, call summaries, and graphics at scale.

Automate the creation of marketing copy, personalized outbound emails, call summaries, and graphics at scale.

Automate the creation of marketing copy, personalized outbound emails, call summaries, and graphics at scale.

Website

Tables

PDFs

Videos

Audio

Document

Leverage AI across data of all formats

Summarize and answer questions about documents, videos, audio files, and websites. Analyze and compare documents seamlessly.

Website

Tables

PDFs

Videos

Audio

Document

How it works

VectorShift Docs

Unlock advanced features and detailed guides in our extensive documentation.

pipeline_setup.py

from vectorshift.node import *

from vectorshift.pipeline import *

file_node = InputNode(name='file_input', input_type='file')

model_text_node = TextNode(text='Describe this file to me.')

llm_node = OpenAI_LLMNode(

model='gpt-4.0-turbo',

system_input=model_text_node.output(),

prompt_input=fileloader_node.output()

)


output_node = OutputNode(

name='my_output',

output_type='text',

input=llm_node.output()

)

VectorShift Docs

Unlock advanced features and detailed guides in our extensive documentation.

pipeline_setup.py

from vectorshift.node import *

from vectorshift.pipeline import *

file_node = InputNode(name='file_input', input_type='file')

model_text_node = TextNode(text='Describe this file to me.')

llm_node = OpenAI_LLMNode(

model='gpt-4.0-turbo',

system_input=model_text_node.output(),

prompt_input=fileloader_node.output()

)


output_node = OutputNode(

name='my_output',

output_type='text',

input=llm_node.output()

)

FAQ

Who can use VectorShift?
Can I try VectorShift for free?
Is there a monthly plan available?
Can I use my own LLM API key?
Is VectorShift secure?
Where can I learn more about how to use VectorShift?
Can VectorShift integrate with my data?
Can VectorShift help built a solution for organization?

Get started today

Get started today

Get started today