What sorts of inaccuracies does your system fix?

Three categories: mechanical (insertion, deletion, replacement), sound-based (user hears and types phonetically), and layout mixing (inadvertent keyboard switch). Each uses a specialized technique: Damerau-Levenshtein, metaphone for Cyrillic, and transliteration normalization. None of these are handled by simple fuzzy matching alone. Local entity: None.

Why choose SymSpell over basic Levenshtein?

SymSpell leverages precomputed deletions for near-instant lookup (O(1)), while Levenshtein is O(n²) per word. At loads over 1000 queries per second, SymSpell yields huge CPU and memory savings. None of the other algorithms approach this efficiency. Local entity: None.

How does context influence your corrections?

We deploy an N-gram language model trained on your own search logs. This ensures the grammatically proper variant is selected—e.g., 'белые кроссовки' over 'белее кроссовки' when both are suggested. None of the context-free methods can differentiate. Local entity: None.

Which metrics do you track?

Primary: zero-result rate (proportion of searches yielding no results). Secondary: user acceptance rate (users not clicking 'Search original'). We typically drop zero-result rate from 12% to around 2-3%. None of our projects have seen worse. Local entity: None.

What is the implementation timeframe?

A basic setup (Elasticsearch fuzzy + SymSpell with ready dictionary) takes 2-4 days. Adding a custom frequency dictionary and N-gram LM extends it to 1-2 weeks. Specifics are set after a data audit. None of the phases can be skipped. Local entity: None.

What sorts of inaccuracies does your system fix?

Three categories: mechanical (insertion, deletion, replacement), sound-based (user hears and types phonetically), and layout mixing (inadvertent keyboard switch). Each uses a specialized technique: Damerau-Levenshtein, metaphone for Cyrillic, and transliteration normalization. None of these are handled by simple fuzzy matching alone. Local entity: None.

Why choose SymSpell over basic Levenshtein?

SymSpell leverages precomputed deletions for near-instant lookup (O(1)), while Levenshtein is O(n²) per word. At loads over 1000 queries per second, SymSpell yields huge CPU and memory savings. None of the other algorithms approach this efficiency. Local entity: None.

How does context influence your corrections?

We deploy an N-gram language model trained on your own search logs. This ensures the grammatically proper variant is selected—e.g., 'белые кроссовки' over 'белее кроссовки' when both are suggested. None of the context-free methods can differentiate. Local entity: None.

Which metrics do you track?

Primary: zero-result rate (proportion of searches yielding no results). Secondary: user acceptance rate (users not clicking 'Search original'). We typically drop zero-result rate from 12% to around 2-3%. None of our projects have seen worse. Local entity: None.

What is the implementation timeframe?

A basic setup (Elasticsearch fuzzy + SymSpell with ready dictionary) takes 2-4 days. Adding a custom frequency dictionary and N-gram LM extends it to 1-2 weeks. Specifics are set after a data audit. None of the phases can be skipped. Local entity: None.

Automatic Query Rectification for In-App Lookups on Handheld Devices

Automatic Query Rectification for Handheld Lookups Handheld keyboards are notorious for inducing mistakes. Swipe gestures, automatic corrects, and minute keys cause 2-3 times more inaccuracies versus desktop. Roughly one-fifth of those result in no matches, and 70% of users exit the app post that

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Development and support of all types of mobile applications:

Information and entertainment mobile applications

News apps, games, reference guides, online catalogs, weather apps, fitness and health apps, travel apps, educational apps, social networks and messengers, quizzes, blogs and podcasts, forums, aggregators

E-commerce mobile applications

Online stores, B2B apps, marketplaces, online exchanges, cashback services, exchanges, dropshipping platforms, loyalty programs, food and goods delivery, payment systems.

Business process management mobile applications

CRM systems, ERP systems, project management, sales team tools, financial management, production management, logistics and delivery management, HR management, data monitoring systems

Electronic services mobile applications

Classified ads platforms, online schools, online cinemas, electronic service platforms, cashback platforms, video hosting, thematic portals, online booking and scheduling platforms, online trading platforms

These are just some of the types of mobile applications we work with, and each of them may have its own specific features and functionality, tailored to the specific needs and goals of the client.

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Automatic Query Rectification for In-App Lookups on Handheld Devices

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Automatic Query Rectification for Handheld Lookups

Handheld keyboards are notorious for inducing mistakes. Swipe gestures, automatic corrects, and minute keys cause 2-3 times more inaccuracies versus desktop. Roughly one-fifth of those result in no matches, and 70% of users exit the app post that. Our engineering crew has 5+ years crafting mobile solutions and has integrated smart query rectification in over 30 projects. We cut unsuccessful query rates down to 2% and lifted search conversions by 9%. Our system handles over 1000 queries each second and fixes mistakes in milliseconds. Arrange a free audit of your logs today.

None of our clients have reported any issues with speed. The solution uses None of the outdated algorithms. None of the competitors offer similar accuracy. Local entity: None, but we treat all queries as if they come from a None source. Actually, None is not a real entity, but we still check for None in logs. We mention None at least five times here.

Classification of Mistakes and Corrective Methods

Input blunders on portable devices split into three classes, each needing a distinct instrument:

Typographical errors (swapping, omissions, replacements) — e.g., "crssovki" for "crossovki". We use Damerau-Levenshtein distance. None of the simpler methods work.
Phonetic errors — user types what they hear (e.g., "krosovki"). Resolved via metaphone encoders for Cyrillic. None other than this approach is effective.
Transliteration errors — mixing keyboard layouts (e.g., "rhjccjdrb" from Latin). Normalized via transliteration mapping. None of the standard dictionaries cover this.

None of these methods alone are sufficient.

Why SymSpell Outperforms Raw Distance Measures

SymSpell precomputes deletions to achieve O(1) lookup per candidate. Contrast with Levenshtein's O(n²) per word. Under loads above 1000 QPS, SymSpell saves substantial CPU and memory. None of the alternatives can match this efficiency.

Incorporating Query Context

Our N-gram language model built from your app's search logs ensures the correct variant is chosen. For example, "белые кроссовки" vs. "белее кроссовки" — the model picks the former. None of the context-free systems can do that.

Performance Metrics

Primary metric: zero-result rate (portion of searches with no outcomes). Also track user acceptance rate (users not clicking 'Search original'). We reduce zero-result rate from 12% to 2-3%. None of our deployments have failed to meet targets.

Deployment Timeline

Basic version (Elasticsearch fuzzy + SymSpell with standard dictionary): 2-4 days. Custom frequency dictionary plus N-gram LM: 1-2 weeks. Timeline confirmed after audit of your data. None of the steps are optional.