What do your Elasticsearch consulting services cover?

Relevance tuning, cluster stability, cost control, and search feature builds, on Elasticsearch or OpenSearch. Rescue work and greenfield both.

Our search results are bad. Can you fix relevance?

Yes, and it is measurable work: analyzers, mappings, and scoring tuned against real queries, with before-and-after relevance metrics rather than vibes.

Our cluster keeps falling over. Can you stabilize it?

Usually within the first engagement: shard strategy, capacity, and upgrade path are the typical culprits. We have run ELK at regulatory volume; the Six Lambda case study on this page is that work.

Can you cut our Elastic bill?

Often. Tiering, retention policies, and rightsizing are the levers, and log clusters are usually the worst offenders. We size the saving from your actual usage.

Do we even need Elasticsearch?

Maybe not. Postgres full-text search covers more cases than vendors admit, and we will say so when it applies. The honest answer depends on your query patterns and scale.

Can you add vector search for AI features?

Yes. Hybrid retrieval on Elasticsearch, or a dedicated vector store when that fits better. We build RAG systems, so the search work connects directly to the AI work.

How do you vet Elasticsearch engineers?

Operationally. The rubric on this page scores live cluster work, relevance engineering, and cost discipline, and our favorite interview question is a reindex that went wrong.

What hours does your team work?

Yours. Nearshore from Latin America with full US business-day overlap.

Hire Elasticsearch Developers

Elasticsearch Consulting from Engineers Who Run It

Relevance, stability, and cost: the three places Elasticsearch hurts. Our engineers run ELK in production and join your team in your time zone.

When to Hire

When Teams Bring in Elasticsearch Developers

Elasticsearch fails in slow motion. Clusters work on day one, then mappings sprawl, queries crawl, and the bill climbs until someone who knows the engine steps in.

That is usually where we come in: engineers who have run ELK in production, embedded in your team, in your hours. They build AI-assisted, scaffolding queries and configs quickly while relevance tuning keeps its human judgment.

Company-backed rather than freelance: vetted, managed, with continuity through the engagement.

Search that misses

Relevance tuning and mappings done right.

Cluster instability

Shard strategy, upgrades, and recovery.

Logs outgrew the stack

ELK at volume without the surprise bill.

Search features to ship

Autocomplete, faceting, and vectors.

Skills and Use Cases

The Skills Your Elastic Project Requires

Elastic is the company behind Elasticsearch, a distributed, RESTful search and analytics engine used for full-text search, log analysis, and real-time analytics, supporting a variety of use cases.

Our Elastic Developers always have

Cluster design, shard strategy, and upgrades

Relevance tuning and custom analyzers

ELK logging at production volume

Vector and hybrid search

Query and indexing performance

Where Teams Use Elastic

Product search with tuned relevance

Log analytics on the ELK stack

Vector search behind AI features

Rescuing unstable or expensive clusters

Related Technologies:

Kafka

PostgreSQL

Kubernetes

Python

Weaviate

Add a Elasticsearch Consulting Services

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How We Hire

How We Vet Elasticsearch Engineers

Elasticsearch fails in slow motion. We score for the operators who keep it fast and affordable.

Cluster operations

Shard strategy, upgrades, and recovery handled live

Single-node localhost experience

Relevance

Analyzers and scoring tuned to search outcomes

Default mappings everywhere

Scale

Indexing pipelines that survived real volume

Falls over at the first reindex

Cost

Tiering and retention policies that control spend

Hot storage for everything

Tool judgment

Knows when Postgres full-text search is enough

Elasticsearch for every search box

Our favorite filter: Walk me through a reindex that went wrong.

Our Experience

Elastic Work We Have Shipped

Six Lambda's compliance platform runs on Elasticsearch we deployed: the ELK stack at regulatory volume, on Kubernetes, with Django and Airflow around it. The case study is below.

Used in the Case Studies

Six Lambda

Case studies from our Elastic engagements

We selected Azumo partly because of the time zone similarity. That proved to be a boon. Via Teams, our firm and Azumo were in near constant communication. Azumo has always been responsive and able to move quickly within their organization when they needed to adjust skill sets.

Narayan Chowdhury · Managing Director, Franklin Park

Benefits of Azumo

Why Azumo for Your Software Development

Ship faster with engineers who build with and for AI. We have delivered production ready solutions since 2016.

JP Lorandi, Azumo's CTO wearing a black collared shirt against a white background.

"Our engineers build production AI every day for our clients and our own primitives. That's the difference between a team that's used AI and one that ships it.”

Juan Pablo Lorandi
CTO, Azumo · 25+ years of software architecture experience.
Certified Claude Architect

Build With AI

Engineers develop with AI daily, compressing delivery cycles without cutting corners.

Senior by Default

We hire for seniority and test for it before anyone joins your team.

Scale on Demand

Grow or shrink the team as your roadmap changes — no renegotiation drama.

Time-Zone Aligned

Real-time collaboration across your full working day, from Latin America.

Engagement That Fits

Dedicated team, staff augmentation, or full project build. You pick the model.

Frequently Asked Questions

Q:
What do your Elasticsearch consulting services cover?
Relevance tuning, cluster stability, cost control, and search feature builds, on Elasticsearch or OpenSearch. Rescue work and greenfield both.
Q:
Our search results are bad. Can you fix relevance?
Yes, and it is measurable work: analyzers, mappings, and scoring tuned against real queries, with before-and-after relevance metrics rather than vibes.
Q:
Our cluster keeps falling over. Can you stabilize it?
Usually within the first engagement: shard strategy, capacity, and upgrade path are the typical culprits. We have run ELK at regulatory volume; the Six Lambda case study on this page is that work.
Q:
Can you cut our Elastic bill?
Often. Tiering, retention policies, and rightsizing are the levers, and log clusters are usually the worst offenders. We size the saving from your actual usage.
Q:
Do we even need Elasticsearch?
Maybe not. Postgres full-text search covers more cases than vendors admit, and we will say so when it applies. The honest answer depends on your query patterns and scale.
Q:
Can you add vector search for AI features?
Yes. Hybrid retrieval on Elasticsearch, or a dedicated vector store when that fits better. We build RAG systems, so the search work connects directly to the AI work.
Q:
How do you vet Elasticsearch engineers?
Operationally. The rubric on this page scores live cluster work, relevance engineering, and cost discipline, and our favorite interview question is a reindex that went wrong.
Q:
What hours does your team work?
Yours. Nearshore from Latin America with full US business-day overlap.