Building Enterprise Search? Here's How To Approach Your Integration Strategy

There are more enterprise AI search platforms being built, and everyone is chasing the same vision: creating a unified knowledge layer that makes company information instantly accessible. The early players such as Glean started with a pure search experience, but there is now a convergence between a pure Enterprise Search experience and an AI knowledge chatbot experience.

But regardless of the interface, the common thread between these products is the importance of integrations - and that’s being commoditized.

Note: Want to see an Enterprise Search demo with AI actions built in? Check it out here.

Ready to build hundreds of integrations?

The modern enterprise tech stack is already massive (over 473+ according to a 2023 report) and only growing larger. Yes the pendulum swings back and forth between tech stack consolidation and buying a ton of point solutions, but the general trend persists. Companies routinely use hundreds of applications across departments, anywhere from Slack and Google Workspace to industry-specific tools and custom internal applications. As a result, the list of integrations Enterprise Search products need to support will never end - with each new customer comes additional integrations that need to be supported.

This presents a critical challenge: is it sustainable to build (and even more so maintain) all of these integrations without turning your engineering team into an integration factory?

Glean might be able to justify throwing teams of engineers on it given their recent $200M round, but that won’t be the case for most companies in the space. However, many engineering teams still want to own integrations, which brings us to the philosophical question: Are integrations core product or an ancillary feature?

Core vs. Non-Core

My take is that the competitive advantage in Enterprise Search doesn't lie in building yet another connector to yet another project management tool. Instead, it comes down to the technology behind the search & retrieval strategy (indexing, ranking, permissions, and relevance), and the additional benefits that can be layered on top of that (ie. agentic workflows - I’ll get to that).

While integration coverage is critical and necessary, it is already becoming commoditized. That’s why it doesn’t make sense for most Enterprise Search companies to pour millions of dollars in engineering salaries to eventually maintain what will essentially be a tables-takes, non-differentiating feature.

The core focus and secret sauce for Enterprise Search companies is in how they pre-process, chunk, index, and surface the data, not the ingestion piece of the puzzle.

Rolling Your Own Integrations

Building integrations in-house might seem like the right choice initially. After all, you want full control over your data pipeline. But let's break down what this really means:

• Years of engineering devoted to building new integration connectors

• Compounding maintenance to handle 3rd-party API changes and versioning

• Infrastructure efforts to ensure your ingestion pipelines can scale to handle enterprise data loads

• Support burden for integration-related issues, unless you invest in building robust logging and observability

• Inability to ship new connectors quickly enough to win competitive deals

Serverless Integration Infrastructure

With the rise of RAG and the demand for ingesting external data, there’s been a huge uptick in demand and urgency from product teams to prioritize integrations on their roadmap. This has benefitted us at Paragon, as AI companies including AI21, Pryon, and Frame use our platform as their platforms’ 3rd-party data ingestion pipelines. They’re now shipping new integration connectors and ingestion pipelines in days, which has enabled them to accelerate their roadmap significantly as their engineers can focus almost entirely on their core product.

How is this possible? Well the majority of the complexities that come with integrations can be offloaded from your engineering team, including:

• Authentication (credential management and token refresh)

• Webhook listeners (to receive real-time events and updates)

• Auto-scaling serverless infrastructure to handle high volumes of data

• Smart rate limit handling to avoid data loss

• Building robust monitoring & observability on integrations for support teams

Fun fact: Given that many of these customers sell to enterprises, they’re deploying Paragon on-prem alongside their own on-prem deployments to their customers.

Beyond Search: The Age of AI Agents

But ingesting all of this external data it is just half of the story. Ultimately the long term opportunity doesn’t stop at just building RAG chatbot with some indexing capabilities for search. A search for information is typically just a step in the process of completing some task or project, whether it’s compiling notes, building a project plan, determining action items, sending an email with the retrieved context - the list goes on.

The opportunity lies in taking on that next layer of work, and AI agents are well positioned as a critical component in solving for that next layer.

Our customers are already doing this today, using Paragon's integration infrastructure to build function tools for their AI agents to automate work across multiple applications. Frame is a good example - they built universal search and document indexing as the foundational layer for their product, and are now equipping AI agents with all that context so they can take appropriate actions on behalf of users in those 3rd party apps. This represents the next frontier in enterprise search - moving from passive information retrieval to active agentic automation.

Curious to see how it works? Check out this Enterprise Search demo we put together that has AI actions built in.

Conclusion

The enterprise search space is evolving rapidly, and the winners will be those who can both provide comprehensive coverage AND deliver superior search experiences. By leveraging modern integration infrastructure, you can achieve both while maintaining focus on your core value proposition.

The question isn't whether to build or buy integrations anymore. It's about how quickly you can move beyond basic integration challenges to deliver the next generation of AI-powered search experiences your customers are beginning to expect.

If you’re looking to win in the Enterprise Search market, consider building your integrations with Paragon so you can go-to-market and scale quickly without having to worry about the tech debt and maintenance. Book a demo to see how we can help solve your specific set of integration use cases.