Search that stays fast at a billion documents.
We tune, scale, and migrate Elasticsearch for data-heavy products. Consulting, cluster optimization, version upgrades, and hybrid keyword plus vector search. Built to run in production, not in a demo.
Your search felt fine at launch. Then the data grew.
Now the dashboards crawl. CPU spikes for no clear reason. A query that took 50 milliseconds now takes two seconds. Your team adds more nodes and the bill goes up, but the slow queries stay slow.
Or you are stuck on an old version. The codebase has search logic tangled into business logic. Every upgrade feels like it could break the product. So you wait. The version gap gets wider each year.
Maybe the problem is relevance. Users search, and the right result sits on page three. They stop trusting the search box. They open a support ticket instead.
These are not "add more hardware" problems. They are engineering problems. That is what we fix.
Most slow Elasticsearch clusters are not under-provisioned. They are misconfigured.
We have run Elasticsearch across every kind of workload.
Consulting
Just having Elasticsearch does not guarantee good relevance or fast queries. We review your data model, your mappings, and your queries. Then we tell you what to change and why.
Implementation
We build search from the ground up. Fuzzy matching, stemming, synonyms, faceting, and typeahead that returns in one round trip. Custom query DSL when the standard query is not enough.
Cluster optimization and performance tuning
This is our most common engagement. We find the CPU spikes, the heavy aggregations, and the mappings doing too much work. We tune ingestion to use CPU, memory, and disk well. The goal is sub-second queries on hardware you already pay for.
Version migrations and upgrades
Old version to new, with no downtime and no regressions. We have moved clusters from 0.9 forward, across major breaking changes, on live products with large customer bases. We test the new version against real traffic before we cut over.
Hybrid keyword plus vector search
Keyword search is precise. Vector search understands meaning. Most real products need both. We combine them so users find the right thing whether they type the exact term or just describe it.
Relevance tuning
We make the best result show up first. That means boost factors per field, custom scoring, and tuning against what your users actually search for. On one platform we tuned relevance across more than 15 fields.
Search over unstructured and transcribed data
Not all data is neat rows. We index documents, logs, and transcribed audio so it all becomes searchable. We have built pipelines that record calls, transcribe them, normalize the text, and make every word findable.
Query path: from a typed query to a ranked result
A single query fans out across the cluster, runs keyword and vector retrieval in parallel, then merges through a scoring layer to a ranked result in sub-second time.
This is credible because we have shipped it.
Three things make this credible.
Our founder wrote the book. Vishal Shukla authored Elasticsearch for Hadoop (Packt, 2015). We have worked with every version of Elasticsearch since 0.9. That is not "experience with" a tool. We wrote about how it works.
We run search at 1.3 billion documents. For ThreatDefence, a security analytics product, we tuned a production cluster holding 1.289 billion active documents, ingesting 10 to 30 million more each day. Their dashboards had slowed and CPU was spiking. We fixed the mappings, removed analyzers doing needless work, and applied proper handling for time-series data. This was a focused optimization engagement, and it shows what well-tuned Elasticsearch can carry.
We built search inside a market-leading platform. For Allego, the Gartner-recognized revenue enablement platform, we built the in-app search engine from scratch. It searches across millions of learning items and transcribed sales calls in sub-second time. We added LinkedIn-style typeahead, configurable relevance, and topic detection using Elasticsearch percolators. The result cut sales-manager review time by 10x. See the 8-year Allego engineering partnership.
We have also done large version upgrades the careful way. On one platform we moved 50+ business modules and six years of production data from Elasticsearch 5.x to 7.x with zero downtime. We invented a mirror-mode pattern to send live traffic to the new cluster, find every bug, and cut over clean. Cluster performance more than doubled.
We work across the search and vector stack.
We have used Elasticsearch for full-text search, log aggregation, analytics, and graph-backed queries. If you are weighing your data layer options, our notes on schemaless databases and graph databases cover trade-offs we have lived through.
Start small. Prove it. Scale.
Search problems are easier to fix in the right order. A short fixed-fee review tells you what is slow and why. A fixed-scope pilot fixes one painful use case in production. Then a senior team owns your search infrastructure for years, the way we have for Allego.
- 01 Assess
Architecture & Readiness Assessment
A fixed-scope engagement. You get an architecture blueprint, risk analysis, roadmap, and ROI estimate.
- 02 Prove
Pilot / Proof-of-Value Build
One real use case, shipped to production, with monitoring in place.
- 03 Scale
Embedded Engineering Partnership
A senior team owns modules end to end, for years. The Allego and benelog model.