Your users deserve search that actually works.
We implement production-grade search platforms using Meilisearch, Elasticsearch, and Algolia — properly tuned full-text search, multilingual support, custom ranking, and semantic layers where they genuinely help. The right architecture for your data, not the trendiest one.
Production in 4–6 Weeks
From kickoff to live search. Not a prototype — a fully indexed, tuned, and monitored search platform integrated with your existing stack.
Engine-Agnostic Expertise
Meilisearch, Elasticsearch, Algolia, or hybrid architectures. We pick the right tool for your scale, budget, and performance requirements.
Multilingual by Default
Arabic, English, French, Francoarabic, mixed-script — our search implementations handle real-world language complexity out of the box.
Measurable ROI
Every engagement includes search analytics and A/B testing infrastructure. You'll know exactly how search impacts conversion and revenue.
Why In-House Search Underperforms
Most funded startups try to build search themselves. They spin up Elasticsearch, write some basic queries, and call it done. Six months later, users complain that searching for 'running shoes' returns kitchen appliances, Arabic queries return nothing, and the search team is a single engineer who's also doing three other things. The underlying engines are excellent — Elasticsearch, Meilisearch, and Algolia are all capable of outstanding results. The problem is that getting great search requires deep expertise in tokenization, analyzer chains, synonym management, ranking tuning, typo tolerance, multilingual morphology, and performance optimization. It's a specialization. The gap between a default deployment and a properly tuned one is enormous, and most generalist engineering teams don't have the search-specific experience to close it.
What We Build
We design and implement complete search platforms tailored to your data, users, and business goals. This starts with engine selection (Meilisearch for speed-critical applications, Elasticsearch for complex analytics, Algolia when you need managed infrastructure), then proper tuning — custom analyzers, tokenizers, synonym dictionaries, and ranking rules optimized for your specific domain and conversion metrics. For catalogs where textual matching alone isn't enough (ambiguous queries, cross-lingual intent), we layer on semantic search using vector embeddings. But we don't default to AI when a properly tuned relevancy engine gets the job done — whether that's Elasticsearch's scoring, or Meilisearch and Algolia's bucketed ranking across word proximity, typo tolerance, exactness, and custom attribute signals. We also handle multilingual support including Arabic, Francoarabic, and mixed-script queries. Every implementation includes a real-time indexing pipeline, search analytics dashboard, and A/B testing infrastructure so you can measure impact from day one.
Built for Funded Companies Moving Fast
You raised capital to build your product, not to spend six months figuring out BM25 tuning. We've implemented search for catalogs with 12M+ products, platforms with 20M+ monthly users, and multilingual applications spanning Arabic, English, and French. Our typical engagement goes from kickoff to production in 4–6 weeks. You get a senior search engineer embedded in your team for the duration — not a junior dev reading docs for the first time. We work within your existing infrastructure, plug into your CI/CD pipeline, and leave you with a system your team can maintain independently.
The Numbers That Matter
Search isn't a feature — it's a revenue lever. Our implementations have driven search relevance from 34% to 91%, increased search-driven conversion rates by 2.4x, and generated $18M+ in attributable annual revenue for a single client. We instrument everything: NDCG@10 for relevance scoring, click-through rates by query type, search-to-purchase attribution, and zero-result rate reduction. You'll know exactly what your search investment returns because we build the measurement infrastructure alongside the search platform.
Technology Stack
Frequently Asked Questions
Which search engine should I use — Meilisearch, Elasticsearch, or Algolia?
It depends on your use case. Meilisearch excels at speed and developer experience for e-commerce and content search. Elasticsearch is better for complex analytics and log search at scale. Algolia offers a managed solution with excellent out-of-the-box UI components. We'll recommend the right engine (or a hybrid approach) based on your catalog size, query patterns, and budget during our discovery call.
Can you improve our existing search without replacing the engine?
Absolutely. Many engagements start with optimizing an existing Elasticsearch or Algolia setup — better ranking rules, synonym management, query understanding, and analytics. We only recommend engine migration when the current stack genuinely can't meet your requirements.
How do you handle Arabic and multilingual search?
Arabic search requires specialized handling: morphological analysis (Arabic words have complex root forms), dialect normalization, transliteration support (Arabizi/Francoarabic), and right-to-left indexing. We use custom tokenizers, cross-lingual embeddings, and language-specific ranking models trained on Arabic data. This isn't something you can bolt onto a standard English search setup.
What does a typical engagement cost?
Search implementation engagements typically range from $15K–$60K depending on catalog size, language requirements, and integration complexity. We scope every project during a free discovery call and provide a fixed-price quote — no hourly surprises.
Ready to get started?
Book a free 30-minute consultation. We'll discuss your specific needs and outline a clear path forward.
Book a Free Consultation →