Search & recommendation systems
People rarely search the way a catalog is written. They make typos, use everyday words instead of product names and mix Turkish and English in the same query.
We build search that works out what someone wants from what they type, and recommendations that suggest related items, alternatives or next steps for that person.
What search and recommendation systems do
A search system takes a short, often messy query and works out what the person meant. It then finds the matching products, documents or content, even for descriptive queries such as "red summer dress" or "cheap wireless headphones".
Recommendations do not wait for a query. They suggest what someone is likely to want next, based on what they and similar people looked at, bought or read. For both, the measure is how often people find what they need.
What we build
Product search
Search for online stores and catalogs that handles typos, synonyms and descriptive queries, and ranks the most relevant items first.
Turkish-aware search
Search that understands Turkish word endings and spelling variants, and copes with queries that mix Turkish and English.
Content and document search
Meaning-based search across articles, help pages or internal documents. People still get results when they do not know the exact wording.
Recommendations
Suggestions based on behavior and item details, such as "you may also like" and "often bought together" lists, personalized picks, or similar items when a product is out of stock or someone is comparing options.
Search analytics
Reports on what people search for, which searches find nothing and where they give up. They show what to fix in the catalog and content.
When it's a good fit
In a large catalog that people search often, every failed search is a lost sale or wasted time, and that is where better search and recommendations pay off most. Something simpler will usually do when:
- the catalog is small and people can browse it easily
- your platform's built-in search already returns the right results for most queries
- there is too little traffic or behavior data to learn from yet
How a project runs
-
Collect search logs
We start from what people type and click, including searches that returned nothing. This shows where the current system fails.
-
Build a test set
We take queries from your logs, rare and awkward ones included, and pair them with the results people should see. Every later change is measured against this set.
-
Improve query understanding and ranking
We improve how queries are understood and how results are ordered, measuring each step against the test set and the current system.
-
Test with real users
We run the new version against the current one on live traffic. It takes over only if people find what they need more often with it.
New items and business rules
New items with no history
A new product has no clicks or sales yet. Its description and attributes are enough for search and recommendations to show it from day one.
Your business rules still apply
Ranking can account for stock, margins, campaigns and items you never want to promote. Your commercial decisions set the limits it works within.
What you receive
- A search and recommendation service connected to your site, app or catalog
- A test set built from your search logs
- Code and documentation, handed over to your team
Frequently asked questions
What data do we need to improve site search?
Your catalog or content, and the search and click logs you already collect. If logs are limited, we can start from the catalog and improve as data builds up.
How soon will search results improve?
Early on, we compare the new search with your current one on searches taken from your logs. How soon improvements reach your users depends on your platform and release process.
Will it work with our e-commerce platform?
In most cases. Search and recommendations run as a separate service that your site or app calls, and the rest of your platform does not need to change.
Where do our search logs and customer data stay?
They can stay with you. Your catalog, search logs and customer behavior data can be processed on your servers or private cloud, and personal data is handled under the data protection rules that apply to you.
Does search need maintenance after launch?
Yes. Catalogs and customer habits change over time. Your team can measure each change against the test set, and we can keep helping if you prefer.
Work with us
Tell us in a few lines what you're working on. We'll get back to you.
From our blog
All articles
How to build an AI chatbot with your own data
Connecting documents to a model is the easy part. Learn what a company chatbot needs: source rules, system connections, access control, handover, and testing.
Read article
When should you use synthetic data?
Synthetic data can help when real data is scarce, sensitive, or unavailable. When is it worth using, and how can you tell whether it improves results?
Read article
How do you prepare data for LLM fine-tuning?
Turn existing data into useful fine-tuning examples: format, chat templates, cleaning, deduplication, redaction, synthetic data, and pre-training checks.
Read article