AI Research

Bardeen's research team seeks to improve human productivity through workflow automation. In particular, we are exploring ways to accurately and reliably complete long sequences of actions with minimal specification from end-users.

Our small team of researchers has developed novel approaches to prompt construction, message generation, and process automation synthesis used by thousands of companies around the world.

Expressive vs. Abstract

At its core, an automation system is a programming environment. As much as we aim to abstract away low-level details, but those details often creep back, through control constructs, types, or the quirks of web APIs. How can we let users access the full expressiveness of a programming system, without being programmers themselves?

Encoding Knowledge Work Beyond Web Services

An old story tells of a technician who was hired to fix a machine. They came, turned a single screw, and then billed a large sum. The large sum wasn’t for the work itself, but for knowing which screw to turn. Indeed, human work, and office knowledge work specifically, is often about the person’s own knowledge and decision making, which is part of the workflow and can be expressed in simple rules, API calls, or clicks. If someone wishes to automate their workflow, they need to be able to describe that knowledge and decision process to the machine. Hence, how can users encode those steps of the workflow in a way that can be automated?

Trustworthy Automation In Natural Language

With the advent of large language models, it is now possible to quickly build high quality semantic parsers even for complex tasks. Yet, accuracy is not perfect, and natural language is inherently ambiguous.

How can users be confident that their prompt was understood correctly and that the automation will implement their desired goal, down to their intended details?

Automation Without Thinking

Beyond accuracy, the main limitation of using natural language as input modality is that it requires the user to formulate a clear goal in writing. Meanwhile, many users are out there performing tasks by hand day in and day out, without even realizing that those tasks can be automated.

Can we automatically identify what parts of the user’s workflow can be automated, and then suggest the right automation to do so?

Scaling To All Services With Code Generation

There are many thousands of services out there that users would like to interface with. Many of those services have public APIs, but those APIs are messy, bespoke, inconsistent. At the same time, the code to access these services is in large part boilerplace, which LLMs are very good at.

Therefore, can we use LLMs to automatically generate the code to access an API from Bardeen?

Scaling to the Web Beyond APIs

Many automation tools have 5,000+ services, and Alexa 200,000+ skills, the web has billions of websites that have no APIs, and users want to make use of those websites in their workflows.

How can we enable users to interact with all those websites in their automations?

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