What makes Visor an expert at integrations?

Visor has been working on integrations for many years.

Through our RelayIQ integration technology, we are able to create bumpers that reliably offer valid inputs from SaaS data into our core product, a project portfolio management tool that is popular among users at many SaaS apps.

Our integration tech has been tested on thousands of teams. We are popular with companies in the aerospace, renewable energies, and software development sectors. Teams at large S&P 500 brands, government agencies, and smaller startups also rely on Visor’s integration tech to visualize and share their SaaS data.

Our engineers have experience working at places like Salesforce and Amazon. They have seen the pitfalls of integrations themselves, and have thoughtfully constructions technology that circumvents the normal cause of automation and integration failures.

Because of recent LLM advancements, we have now made our technology available to other companies who want to connect SaaS data to their AI agents.

How does Visor connect AI agents to SaaS data?

Visor’s CloudStore technology allows our engineers to achieve a 10x engineering velocity over large tech companies, making it possible for us to capitalize on emerging technology that unlocks AI’s potential.

Therefore, when JSON-ready LLMs started to emerge in late 2024, we jumped at the chance to push the boundaries on this technology.

Prior to this breakthrough, you couldn’t rely on LLMs to generate text that would fit a specific format. Now that they can, the models have the ability to talk to other computers through a format (JSON) that allows us to work with SaaS data like never before with AI agents. We even launched our own AI Smart Templates in Visor’s project portfolio management tool using this technology; this tech has been out in the wild and used by thousands of users.

We realized other companies would similarly scale their AI efforts in a deeper, richer, and faster way if they had access to an SDK that unlocks Visor’s ability to make SaaS data workable for AI agents.

What does Visor achieve that most APIs can’t?

While LLMs can now control SaaS apps, there is a big missing piece: APIs can be rigid and unforgiving.

The APIs used to control these SaaS apps are very rigid about what JSON can do.

For example: when you talk to an API, you can’t accidentally leave out the zero in the date. If it’s March 6, you have to make it 03/06/2024 (note the 0). These kinds of details vary based on the SaaS data structure you’re working with. Therefore, it’s easy for APIs to error due to invalid inputs.

Another example: Imagine you’re sending a status to Jira but with the wrong ID number; it’ll fail.

TLDR: LLMs need to be reigned to ensure there are only valid inputs while they control and deal with with SaaS apps. You need to have a really good understanding of what these SaaS apps expect these formats to be. Visor offers that.

Will Visor increase the success rate of my AI agents performing complex automations?

AI agents need valid inputs to autonomously make decisions in complex workflows and automations. Visor helps increase the success rate of AI agents by ensuring the SaaS data they work with are in a format that agents can actually understand.

What are concrete examples of how Visor’s integration tech works for AI agents?

We build the technology a level deeper than everyone else.

A lot of tech in this space is building for developers who can figure out the quirks of integrations. However, because the backbone of Visor’s product prior to 2025 dealt with non-developer customers who just needed their SaaS data to work, we have made our tech usable for less technical audiences.

This history optimizing for non-technical customers has made it possible to translate SaaS data to AI agents also.

Example: When a Salesforce contact asks for a company, Salesforce will deliver an ID number; Visor will translate it to the company name for the end user (whether human or AI agent.) Other apps will just provide the ID number. –

SaaS apps often use ID numbers because company names (and other values) change. In addition, they have other quirks like having character length limits or needing a “0” in dates.

Because we have developed deep integrations with complex SaaS companies, we have learned a lot through trial and error. You cannot expect AI to read documentation about a SaaS company’s API to figure out how to practically use their data. Visor bridges that gap, allowing AI agents to work with usable data that reduces failures, increases success rates, and unleashes a new class of AI agents.