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The Fastest Way to Build
AI Apps and Workflows

Automate workflows and back office processes without code

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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.

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()

)

Marketplace: Pre-built use cases

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Agents: execute complex tasks

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Search: use the optimal data retrieval method

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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_

Leverage AI throughout your
company and products

Assistants

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

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

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

Outbound

Copy

Summaries

Analytics

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.

How it works

  • 1
    Start with
    a template

    Leverage dozens of pre-built templates for end use cases - ranging from research report generators to resume screeners.

  • 2
    Connect
    data

    Allow your AI application to leverage raw data in any format (websites, documents, or CSVs) or directly connect with your database.

  • 3
    Intuitive drag and drop builder

    Build and rapidly iterate on your application’s architecture with a large library of drag and drop components. Transfer your work seamlessly between no-code and our python SDK.

  • 4
    Customize and deploy to end users

    Export a chatbot or generate an API endpoint instantly. Customize the look and feel of the application.

Enterprise solutions

We leverage our secure infrastructure and development platform to build and deploy high-ROI AI solutions for your organizations.

Learn more

Report generation

High volume chatbot

Personalized email outbound

RFP and proposal generators

Knowledge search

VectorShift Docs

Unlock advanced features and detailed guides in our extensive documentation.

Browse 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()

)

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