How the BRAIN module accelerates keyword research for SEO

August 31, 2026 · Bartłomiej Spasowski, Founder, modulla · EN

What keyword research with the BRAIN module is and how it affects SEO effectiveness

Keyword research using the BRAIN system is a process in which a multi-layered data architecture — encompassing brand context, content history, and user behaviour data — automatically generates SEO recommendations tailored to a specific client. Instead of manually filtering spreadsheets, the agency receives ready-made semantic clusters, content priorities, and structural suggestions before its specialist has typed a single query.

The problem: traditional keyword research can't keep up with the pace of the market

For years, phrase research followed the same pattern: export from a tool, manual sorting, a subjective selection of a dozen topics, a brief to the copywriter. With one client, that's manageable. With ten, it becomes a bottleneck that slows down the entire content pipeline.

But the problem runs deeper than scale. Traditional research answers the question: "Which phrases have volume?" Today the more relevant question is: "Which phrases lead to visibility in an environment where AI generates the answers?" That's a fundamental difference.

Search engines are no longer link indexes. According to Senuto's 2025 data, Google AI Overviews appeared in 24.17% of all queries in Polish Google [30]. For simple informational queries ("Know Simple"), that figure jumped to 57.82% [30]. This means that nearly every other simple user question receives an answer without ever leaving the results page — and your client loses traffic before they can even begin to compete.

An agency that optimises client websites for the old click-through model is building positions in a shrinking pie.

How the market is solving this problem: from research to semantic orchestration

The best agencies haven't abandoned keyword research. They've changed its architecture. Instead of treating research as a one-off project stage, they treat it as a continuous process fed by real-time data and client context stored in the system.

Three layers of modern research

Client profiling (Business DNA). Before generating any recommendation, the system needs to know who it's working for: industry, personas, commercial goals, existing content, campaign history. Without this layer, AI produces generic phrase lists that anyone could receive. Platforms such as Sintra Brain AI allow these profiles to be stored for multiple clients simultaneously and automatically detect conflicts in imported data [15].
Semantic clustering. Modern tools group keywords not alphabetically or by volume, but by intent and topical co-occurrence. LSI (Latent Semantic Indexing) makes it possible to map secondary phrases with a dependency coefficient, protecting against keyword cannibalisation and building topical authority [8].
GEO-readiness scoring. Algorithms assess not only a phrase's potential in the classic SERP, but its susceptibility to being cited by AI Overviews and conversational systems. That's the difference between optimising for positions 1–10 and optimising to be a source in an AI-generated answer.

Automation that makes the difference

Deploying AI for keyword research works on several levels simultaneously:

Real-time question extraction from search engines. Tools such as the Senuto MCP Server create a bridge between the local work environment and current Polish Google results, eliminating the lag between market conditions and the agency's recommendations [8].
Automatic internal linking. Enterprise systems analyse the semantic similarity of subpages using vector text representations and automatically suggest link structures with contextual anchors, ensuring value flows between pages [8].
Continuous content audit. AI agents monitor topical gaps, thin sections, and structural shortcomings around the clock, rather than waiting for a monthly report [8].

Market data: what is happening to organic traffic in Poland

The Senuto report published in the first half of 2025, based on an analysis of 17.7 million queries and over 1,400 Polish domains [30], paints a sobering picture of the scale of change:


Metric	YoY change (May 2025)	YoY change (June 2025)
Organic click volume	-6.6% (from 91.2M to 85.2M) [10]	-19.4% (from 91.5M to 73.8M) [10]
Number of SERP impressions	+13.2% (up to 2.66B) [10]	+3.4% (up to 2.45B) [10]

Pages are visible, but clicks are evaporating. In the first half of 2025 alone, over just two months, more than 23.7 million clicks disappeared from Polish websites [10]. At the same time, agencies that have learned to optimise clients for AI citation are playing a very different game.

Being cited as a source in AI Overviews can recover up to 61% of potentially lost organic traffic (Senuto data, 2025) [30]. A key observation: reaching this layer does not require a top-3 ranking. The average organic position of a cited domain is 6.73 [30]. Smaller, semantically well-optimised sites can effectively bypass market giants.

Local content has a structural advantage here: 96.03% of citations in Polish AI Overviews come from local Polish domains [30]. This is a real barrier to entry for English-language competitors — one that many agencies have yet to exploit consciously.


Query type	AI Overview frequency	Typical CTR drop
Know Simple (simple facts)	57.82% [30]	up to -40% [30]
Know (broad information)	31.37% [30]	-19.5% to -34.6% [30]
Do (transactional)	7.16% [30]	marginal
Visit-in-Person (local)	2.21% [30]	marginal

With its characteristic elegance, the market has found a way to simultaneously increase page visibility and strip away their traffic. An agency that understands this can turn the paradox into a client advantage.

Practical applications: what the BRAIN module changes in agency work

Client onboarding in under 50% of the previous time

One of the hidden costs of SEO agency work is the time needed to get up to speed with a new client: industry, history, tone of communication, previous campaigns, quarterly goals. AI systems based on a persistent client profile can cut this stage by up to 50% [16]. Instead of explaining the same things in every brief, the agency builds the profile once and continuously expands it.

Research scaled without scaling the team

Automatic phrase clustering, intent detection, and content structure mapping make it possible to serve more clients without proportional headcount growth. Low-value-added work — sorting and filtering data — is taken over by the pipeline. The specialist focuses on interpretation and decision-making.

Optimisation for citation, not just for position

Content optimised for AI Overviews has a specific profile. Research indicates that the optimal length is between 9,500 and 12,499 characters, and the most frequently cited materials are between 6 and 12 months old [30]. These are concrete parameters the agency can build into its brief template.

Structure matters equally: a direct answer at the start of a section, H2/H3 headings phrased as questions, data in tables and lists. The RAG systems that power AI Overviews prefer exactly this format when extracting fragments for citation.

Visibility monitoring in the generative ecosystem

Traditional GSC measures clicks and impressions in the classic SERP. New GSC beta reports have started measuring impressions generated by AI Overviews [30]. Agencies supplement this with external tools (Brand Radar, Snoika) that monitor whether and how a client's brand appears in responses from ChatGPT, Gemini, Perplexity, or DeepSeek [4].

This is a shift in reporting philosophy: from "where are we in the TOP 10" to "are we a cited authority in AI responses."

The most common implementation pitfalls

Deploying AI for keyword research carries real risks worth naming directly.

Generic content instead of original. AI systems synthesise what already exists. If the brief and prompt don't force a unique angle, the output is predictable and uninteresting to both search engines and readers. Market research indicates that Google actively devalues mass-generated content produced without quality control [21]. The solution is human-in-the-loop: AI as the skeleton, an editor as the voice.
Chasing a perfect score in the tool. The optimisation score in editors such as Surfer SEO is a guideline, not an end in itself [36]. Stuffing text with keywords to hit 100/100 destroys readability and can trigger quality filters [36].
Failure to track AI visibility. CTR from GSC measures only clicks on classic results. Ignoring citation monitoring in AI Overviews gives an incomplete picture of campaign effectiveness and makes proper ROI reporting impossible.

There is no better proof that AI in SEO demands a quality process than the fact that the market already distinguishes between agencies with a verification gate and those without one. Clients lost to poor AI-generated content quality are accumulating faster than clients gained through research automation.

The BRAIN module vs. classic SEO tools: a comparison of approaches


Area	Traditional approach	modulla	BRAIN
Phrase research	Manual export and filtering	Automatic semantic clustering with intent
Client context	Brief written from scratch for every task	Persistent profile (Business DNA) built once, continuously expanded
Optimisation goal	Position in TOP 10	Citation in AI Overview + organic position
Monitoring	GSC + monthly report	Continuous AI agent audit + GSC beta reports + Brand Radar
Client onboarding	Week-long data-gathering process	Shortened by up to 50% thanks to a structured profile [16]
Content quality	Depends on copywriter availability	Depends on the process: AI + human verification

How to start: an action framework for agencies

Moving from ad-hoc AI use to a repeatable research process doesn't require changing everything at once. The market has developed a sequence that minimises risk.

Build the client profile before you generate anything. Goal, persona, existing content, tone of communication, campaign history. Without this layer, every AI recommendation will be too generic.
Define the topical structure before you start looking for phrases. Semantic clustering is only effective when you already have a hypothesis about which topical areas you want to cover.
Introduce a quality gate for every output. No content leaves the pipeline without human verification. This isn't an option for the cautious; it's a standard driven by E-E-A-T requirements and rising algorithmic expectations [21].
Monitor citations, not just positions. Add AI Overviews and chatbot visibility tracking to the client's standard reporting set.

FAQ: keyword research and the BRAIN module

Does the BRAIN module replace an SEO specialist?

No. It replaces manual filtering, sorting, and the repetitive generation of briefs. The specialist remains responsible for interpreting data, making strategic decisions, and verifying the quality of outputs. The market makes it clear that agencies treating AI as an unsupervised generator lose clients over content quality — they don't gain them [21].

How do AI Overviews affect client traffic from informational content?

Informational content is hit hardest: simple factual queries trigger AI Overviews in 57.82% of cases [30], which can mean a CTR drop of up to 40% [30]. The strategic response is to optimise for citation within the AIO block, not to abandon informational content — because cited domains recover up to 61% of potentially lost traffic [30].

Do Polish sites have a chance of being cited in AI Overviews against large global services?

Yes, and this is one of the non-obvious pieces of good news. As many as 96.03% of citations in Polish AI Overviews come from local Polish domains [30]. The algorithm favours local context, so well-optimised Polish content has a structural advantage over English-language competition in Polish Google.

What content length favours citation by AI Overviews?

Research points to an optimal range of between 9,500 and 12,499 characters [30]. Beyond length, content age matters: materials between 6 and 12 months old achieve the highest citation effectiveness [30]. Content that is too fresh or too old is cited less frequently by AI.

If you'd like to check whether your current research process is adapted to the realities of generative search, get in touch with our team. We'll show you what an AI ecosystem visibility audit looks like and which content areas of your agency's clients have the greatest traffic recovery potential.