Search is no longer only a list of links. Customers still use Google, but increasingly ask full questions in ChatGPT, Gemini, Perplexity and other AI systems: “who should I choose?”, “which provider is best for my case?” or “how do these solutions compare?”. For a brand, that creates a new business problem. Being indexed is not enough. The company must be understandable, credible and present when a system constructs an answer or a supplier shortlist.
That is why Softech is developing Jesteś Polecany — a Recommendation Intelligence platform that combines Google and AI Search visibility analysis, recommendation-readiness assessment, demand-opportunity discovery and a continuous process for strengthening content, entity clarity, authority and local presence.
This is not a promise that “ChatGPT will recommend your company in seven days”. The opposite: one of the product principles is to separate what can be measured responsibly from what cannot be guaranteed.
The problem: visibility, citation and recommendation are different signals
Traditional SEO trained the market to think in keyword positions. Generative search is more complex. A brand may:
- rank in Google and still be absent from AI answers,
- be mentioned by a model without a citation to its own website,
- be used as a source without being recommended as a supplier,
- have a technically strong website but weak authority, proof, local relevance or intent fit,
- offer an excellent service but remain outside a shortlist because systems cannot clearly understand what the company does and why it is credible.
Jesteś Polecany therefore does not reduce the entire problem to a single “AI ranking”. The platform separates readiness, observed visibility, opportunities and recommendation monitoring. A single model response is not a stable ranking, and one screenshot is not a measurement protocol.
What is Jesteś Polecany?
Jesteś Polecany is a system for analysing and improving brand visibility and recommendation readiness across Google, AI Search and Local Search. It connects layers that are usually fragmented across multiple tools: technical readiness, content coverage, entity clarity, authority & proof, local context, customer-question research, competitive gaps and recommendation monitoring.
The key difference from a typical visibility tool is that the report is not the end of the workflow. The platform is designed to move from diagnosis to the next best action and then measure whether the action improved the situation.
| Typical SEO/visibility tool | Jesteś Polecany |
|---|---|
| Shows rankings, errors and charts | Connects readiness, demand, opportunities and monitoring |
| Optimizes around a keyword list | Studies questions, comparisons, local and commercial intent |
| Ends with a report | Moves toward Growth Assets and another measurement cycle |
| Treats the score as the final result | Uses the score as a diagnostic reference point |
| Often mixes visibility with recommendation | Separates readiness, mention, citation and supplier recommendation |
Polecany Score: one number without false precision
One component of the system is Polecany Score. It is not intended to imitate a ChatGPT position or predict revenue. The score organizes observable public signals so teams can quickly see where the largest readiness gap exists.
The current model evaluates areas such as:
- Technical readiness — indexability, canonicals, robots, sitemap, metadata, document structure and crawler accessibility,
- Content coverage — whether the site covers the offer, use cases, questions, comparisons and buying stages,
- Entity clarity — whether the brand, organization, services, locations and specializations are consistent and unambiguous,
- Authority & proof — case studies, authorship, data, external sources, references and evidence of expertise,
- Context signals — local relevance, industry context and conversion readiness.
A high score does not mean that a company captures all available demand. A website can be excellent and still miss important customer questions. That is why Polecany Score is connected to the next layer: the Opportunity Engine.
Opportunity Engine: where is the customer asking while the brand is absent?
The most useful business question is not “what is our position for 500 keywords?”. It is: where is the customer making a decision while our brand is absent from the conversation?
The Opportunity Engine identifies topics, questions, comparisons and local intents that may lead to a contact, quote or supplier shortlist. Each opportunity can be evaluated using intent, business value, competitive pressure, current gap and execution feasibility.
For a service business, for example, the system can distinguish a generic informational query from a local commercial query, a comparison question or an AI-search-style recommendation prompt. This helps teams avoid producing “more content” for its own sake and instead build assets where a real buyer-journey gap exists.
Recommendation Readiness Loop: Discover → Execute → Measure → Learn
Jesteś Polecany is designed as a loop rather than a one-off audit. Every cycle should leave the brand in a stronger position than the previous one.
- Discover. Analyse the site, competitors, customer questions, locations, comparisons and the places where answers are assembled.
- Execute. Build specific Growth Assets: service and location pages, comparison pages, expert answers, structured data, entity signals, proof and other elements needed to improve visibility.
- Measure. Track changes in readiness, coverage, brand presence, competitive visibility and — in the monitoring layer — actual model responses.
- Learn. Use the result to reprioritize the next cycle instead of assuming the original plan was always correct.
This is close to how Softech designs modern SaaS products: data should lead to decisions and actions, not just another dashboard. The underlying engineering patterns are explored further in our guide to AI-native SaaS architecture and our SaaS development service.
AI Search, Google, Local Search and Authority as one system
Companies no longer compete in one channel. The same buyer may discover a brand in Google, ask ChatGPT for alternatives, validate reviews in Maps, read a comparison page and only then send an enquiry.
The platform therefore groups signals into four practical areas:
- Google Search — visibility for informational, commercial and comparison queries,
- AI Visibility — brand and source presence in generative answers,
- Local Search — local intent, location consistency and regional authority,
- Authority — expertise evidence, entity consistency, case studies, sources and corroboration.
This does not make classical SEO irrelevant. Technical accessibility, information architecture and a strong content base remain foundational. What changes is the objective: teams increasingly optimize not only for a click but also for brand understanding and inclusion in a recommendation process.
AI + experts: automate scale, not responsibility
The product automates research, change detection, large-scale opportunity analysis and repetitive operations. It is not designed to autonomously “produce SEO” without quality control.
Strategy, brand judgment, business priorities, publication of important content and decisions that affect market positioning stay under human supervision. In practice, the model is AI speed + expert judgement. We use the same philosophy in AI automation projects: models can accelerate analysis and execution, but the surrounding system should define clear responsibility boundaries.
Which companies benefit most?
Jesteś Polecany creates the most value where recommendation precedes a meaningful purchasing decision and where public signals can be systematically improved.
- Service businesses — where buyers ask who to trust and which provider to choose before making contact.
- Local & multi-location brands — where demand depends on a city, region, branch or local authority.
- B2B companies — where the buying cycle is longer and expertise, comparison and proof influence the shortlist.
- Manufacturers — where products are compared through specifications, applications, documentation and brand credibility.
Not every company needs broad AI monitoring from day one. The process starts with a Scan and Growth Review so the scope can be matched to actual opportunities rather than a generic package.
The methodological boundary: readiness ≠ recommendation
Jesteś Polecany does not sell deterministic outcomes. Generative-model responses vary with prompts, model versions, session context, timing and source availability.
Public website readiness is therefore measured separately from actual model responses. Reliable recommendation monitoring requires a defined prompt set, intent segmentation, model/time recording and classification of the observed presence: mention, citation and supplier recommendation. A trend measured with a repeatable protocol is more useful than one isolated answer.
Polecany Score is not a ChatGPT ranking. It describes whether a brand has public signals that help systems find, understand and evaluate it; the monitoring layer then measures what actually appears in model responses.
The full principles are published on the Jesteś Polecany methodology page.
Why build a platform instead of another report?
Many tools diagnose problems well. Execution remains the bottleneck: what should happen first, which asset should be built, how should technical work connect to content and authority, and how should the impact be validated later?
Our goal is to close that gap. Jesteś Polecany is being built as an operating system for Recommendation Intelligence — from the initial scan, through Opportunity Map and Growth Plan, to execution, review and the next cycle. In the next stage, a dedicated case study will show how this product logic maps to the application architecture, data models, scan engine and recommendation index.
What should you remember?
- Google visibility and AI recommendation are not the same thing.
- A brand needs technical accessibility as well as content coverage, entity clarity, authority and proof.
- Polecany Score is a diagnostic reference point, not a magical “does ChatGPT recommend me?” number.
- The Opportunity Engine identifies decision moments where demand exists but the brand is underrepresented.
- The core operating loop is Discover → Execute → Measure → Learn.
- AI accelerates analysis and operations, while strategy and accountability remain supervised by experts.
If you want to see how your company looks from the perspective of the new search environment, start with the free scan at JesteśPolecany.pl.