How Semantic Search Changes IP Workflows - Video 4

Tim

“If AI is only as good as the data behind it, then the real question becomes: where does the data come from, and how do you make it reliable enough for real-world decisions?

So where does Lighthouse IP come in, and what do we actually do?”

Kacper

“Lighthouse IP has been a patent data provider for over 30 years.

We are effectively the data backbone behind platforms that many IP professionals already use, often without even realising it.

What we have done now is take that data foundation and make it available in a new way: vectorised, structured, and accessible through APIs.

That means companies can build AI-powered search and analysis on top of data they can actually trust.

We are not asking companies to switch platforms.

We are providing the data layer that makes their existing tools or custom-built solutions more intelligent and reliable.”

Tim

“So you are basically saying we are not another platform?”

Kacper

“Exactly.

The IP market is full of platforms.

Everyone is selling new dashboards, interfaces, and “agentic” capabilities.

We are not doing that.

We are API-first.

Think of us as building blocks.

You can integrate our vectorised patent data directly into your own environment, whether that is your own AI model, Claude, ChatGPT, Perplexity, or a custom internal solution.

That means companies keep their own workflows, tools, and working methods, while gaining a far stronger data foundation underneath.”

Tim

“What is so important about the data side of this? Why does that matter more than the AI model itself?”

Kacper

“This is where most people misunderstand AI in IP.

They focus entirely on the model.

Which LLM are you using? How large is it? How fast is it?

But the model is only the engine.

The data is the fuel.

If you feed an advanced AI model incomplete, inconsistent, or unstructured data, you will still end up with poor results.

Our advantage comes from 30 years of structured, cleaned, and deduplicated patent data, including linked patent families across jurisdictions, corporate ownership trees, and normalised assignee names.

That is the difficult work that many AI vendors skip.”

Tim

“What makes your data different from what everybody else has?”

Kacper

“Three things.

First: coverage.

We maintain one of the world’s largest IP datasets, including full-text coverage across dozens of countries and over 170 patent authorities.

Second: structure.

We normalise company names, link related filings across jurisdictions, and maintain corporate ownership trees.

That is critical for understanding who owns what.

Third: longevity.

We have been doing this for over 30 years.

That historical depth and consistency cannot easily be replicated by simply scraping public databases today.”

Tim

“And what can companies actually do now with this type of data?”

Kacper

“Semantic search across the global patent landscape in any language, within seconds instead of weeks.

You can identify conceptually similar patents, discover white space in technology areas, and perform competitive landscape analysis based on meaning rather than keywords.

Because the data is structured and vectorised down to sentence level, you can retrieve concepts and claim language far more precisely than traditional approaches allow.

That level of detail is extremely difficult to achieve with existing tools.

We are talking about systems capable of handling billions of data points and making them searchable within seconds.”

Tim

“Okay, but what if a company already has its own tools?”

Kacper

“That is exactly where our APIs fit in.

They are designed to complement whatever environment a company already uses.

If a company has its own AI stack, it can use our vectorised data as the retrieval layer.

Many in-house legal teams are already building supplementary internal AI tools.

Giving those tools direct access to high-quality patent data creates a major advantage.

The key point is that there is no disruption.

Nobody needs to replace their existing workflows.

We simply add intelligent capabilities on top.”

Tim

“Okay, but what if a company wants to keep all of its information internally?”

Kacper

“The API is often just the starting point.

Increasingly, companies want to take the data fully in-house so they maintain complete sovereignty and control over access.

Everything stays behind their own firewall.

All searches remain private.

That is becoming increasingly important as IP information becomes more sensitive.”

Tim

“And if someone watching this wants to see AI vectorisation in action, what can they do?”

Kacper

“We covered a lot in this series, but ultimately seeing is believing.

The vector search capability Lighthouse has brought to market is genuinely different.

It is not simply another platform or another tool.

It is a fundamentally different way of working with IP.

And I genuinely think it deserves people’s attention.

Think of it like getting fitted for a custom suit.

We first need to understand your goals, workflows, and use cases before we can recommend the right setup and explain how it best integrates into your systems.”

Tim

“Kacper, thanks for explaining all of that to me.

We really covered a lot, from the basics of IP all the way to AI.

I already have many more questions, but I will save those for another time.

Thanks.”

Kacper

“Tim, thanks very much for joining me.

And thanks as well to everyone watching and listening.

There is an enormous amount of information out there.

As always, if you have questions, we are more than happy to share our knowledge.

I genuinely think this is an incredibly exciting time to work in both IP and AI.

So take a moment, appreciate it, and keep having fun.”