TL;DR: A patent research firm serving enterprise clients was receiving between 80 and 120 search requests every month. As its client base grew and new requests continued to arrive, the backlog increased and turnaround times became harder to maintain. The firm integrated the PQAI Prior Art Search API into its internal workflow to support the initial prior art review. This case study explains how the integration helped the firm reduce its backlog, accelerate the early stages of each search, and respond to clients faster.

Patent research firms are expected to deliver two things consistently: Thorough research and predictable turnaround.

Maintaining both becomes difficult when the firm serves enterprise clients that submit a steady volume of requests.

This was the situation faced by a prominent patent research firm in California. It was receiving between 100-120 patentability search requests every month. Its researchers were experienced, and the firm was known for its thorough searches and detailed reports.

However, as the firm added more clients, the backlog began to grow.  Every assignment required considerable groundwork. Researchers first had to understand the invention, identify the central technical concepts, and find the right direction for the search.

The firm had considered AI-enabled prior art search tools. However, it needed greater control over how confidential invention data was handled and how the search fitted into its existing process.

As request volumes increased, researchers spent more time on this initial work. New assignments arrived before older ones had moved forward, and the backlog continued to grow.

The firm was looking for ways to accelerate the beginning of each search without reducing the role of its researchers.

Where the Search Process Started to Slow Down

The invention disclosures submitted by clients varied considerably. With the rise of AI writing tools, the firm also began receiving disclosures that ran up to 60 pages.

Contrary to what one might expect, a longer disclosure did not necessarily make the search easier. Researchers first had to read the document, understand what the invention was trying to achieve, and extract the central technical idea that needed to be searched.

Only then could they begin identifying relevant terminology, classifications, and prior art.

This work required time before the detailed search had even started. With a high volume of disclosures coming in, the process became increasingly difficult to manage.

Why a Prior Art Search API Made More Sense

The firm could have used standalone AI tools to accelerate the initial review. However, there was one important concern.

The disclosures contained confidential and unpublished invention information. The firm did not want researchers uploading this information to standalone tools with unclear data retention or reuse practices.

At the same time, the growing volume meant that continuing with the existing process was no longer practical.

The firm already had an internal system through which it received and managed invention disclosures. Client documents, researcher inputs, and final reports remained within the same workflow.

It therefore needed a solution that could work within this system rather than require sensitive information to be moved elsewhere. The firm concluded that a prior art search API would be the best way to accelerate the early search process while keeping its existing workflow intact.

Why the Firm Chose the PQAI Patent Search API?

The firm evaluated several patent search APIs before making a decision. Three tools made it to the final shortlist. 

Their first requirement was natural language search. The firm needed an API that could accept a plain language description and surface relevant prior art without requiring researchers to construct complex queries. The quality of the returned references was equally important.

Security was another important criterion. The firm needed clarity on how confidential invention information would be handled. It also wanted the search to remain within its existing system.

PQAI also had credible external validation. The firm had read that PQAI ranked second in an independent study conducted by the French research journal BASES. They had also seen PQAI recommended in a video by patent attorney Jeff Schell.

The firm had also heard about PQAI directly from one of its enterprise clients. Some of the client’s inventors had already used the PQAI search platform independently and included references found through it in their invention disclosures. Together, these factors gave the research firm enough confidence to move forward with PQAI.

They requested that we connect the PQAI Patent Search API to their internal system for managing invention disclosures.

How the PQAI API Fitted Into the Existing Workflow

This is how the client envisioned the workflow. Once the researcher had extracted the central technical idea from a disclosure, that description could be sent to the PQAI API from within the same internal system.

The API would return an initial set of relevant patent references. The firm also required it to return related CPC classifications and technical concepts that could help researchers understand the possible direction of the search.

These results were added to the existing assignment.

When researchers opened the request, they already had an initial view of the prior art landscape. They could review the references, identify useful classifications, refine the search strategy, and move into detailed research sooner. The API simply gave them a stronger starting point. Plus, they did not need to log in to another platform. The first set of results was already available when they opened the assignment.

The Result: Turnaround Improved as the Backlog Reduced

The impact became visible within the first few months of using the API. Before the integration, the firm took around five working days to complete an average search request. After the API became part of the workflow, the average turnaround time decreased by 35%. 

Researchers no longer had to establish the initial search direction entirely from scratch. They opened each assignment with relevant references, CPC classifications, and technical concepts already available. This allowed them to move into reference validation and detailed searching sooner.

As requests began moving through the system faster, the backlog gradually reduced. The additional researcher capacity also allowed the firm to handle approximately 20% more requests without immediately expanding the team.

For enterprise clients, this meant receiving completed search results sooner, giving them more time to review the findings and determine the next steps. 

Key Outcomes → Average search turnaround reduced by 35% → The firm created capacity to handle approximately 20% more requests → The growing backlog reduced as assignments moved through the workflow faster

Why the PQAI Prior Art Search API Fits Patent Research Firms

For patent research firms, a growing volume of work can quickly become a bottleneck. Repeating the same groundwork across a high number of requests makes the process even more tedious and can lead to further delays.

Thankfully, the PQAI Prior Art Search API brings the initial search layer directly into the firm’s existing system. Researchers get a useful starting point without moving confidential disclosures to another platform or changing how the final search is conducted.

With its secure data handling and easy integration, the PQAI API can help patent research firms reduce backlog, respond faster, and take on more requests while keeping experienced researchers in control of the final validation.

If the early search stage is slowing down your team, reach out to us to discuss your requirements, and we can help you explore the right implementation approach.