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Olympas
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Data engineering, applied AI, analytics and search

Models in production.
Not in notebooks.

Data engineering, applied AI, analytics and search, built by the same engineers who run the rest of the system. With the evaluation, guardrails and cost control to keep it running once real users arrive.

Start a projectSee the pipeline
Four layers of point clusters stacked in depth, the widest at the bottom and the brightest at the top, joined by sparse lines of points.
Capabilities, one pipeline
4
Pipeline stages, sources to product
5
Years building mission-critical software
20+
Full
Lifecycle, from discovery to operations
01

The practice

The plumbing first. Then the model.

Most AI work fails before the model: data that arrives late, in the wrong shape, that nobody trusts. We build the pipelines, the warehouse and the semantic layer first, so the model has something true to learn from.

Then we ship AI that survives contact with real users: generation grounded in your own data, with evaluation, guardrails, cost control and monitoring for drift.

The plumbing first, then the model. A demo is not a product until it survives real users.

02

The pipeline

One pipeline, from sources to product.

Five stages, four capabilities. Each capability below shows which stages it works on, what is in it, and the stack we use.

A data pipeline in five stages: sources, stream, warehouse, model, product.

One pipeline, four capabilities

  1. SOURCES

    Systems, events, documents and streams, SAP or not.

  2. STREAM

    Batch and streaming ETL and ELT, change-data-capture and real-time sync.

  3. WAREHOUSE

    Warehouses and lakehouses, a semantic layer, quality, lineage and governance.

  4. MODEL

    LLM applications, retrieval-augmented generation, predictive models, training and evaluation pipelines.

  5. PRODUCT

    Embedded analytics, full-text and vector search, recommendations, monitoring for drift.

The four capabilities

  1. 01

    Data Engineering

    Pipelines that make data trustworthy.

    The plumbing beneath every dashboard and model: data that arrives on time, in the right shape, and that people believe.

    Typical stack

    • Snowflake
    • BigQuery
    • dbt
    • Spark
    • Kafka
    • Airflow

    Movement

    • Batch and streaming ETL and ELT

      Whether the requirement is nightly batch or sub-second streaming, we move data reliably, with the lineage and quality checks that let people trust what they see.

    • Data warehouses and lakehouses
    • Change-data-capture and real-time sync

    Trust

    • Data modeling and a semantic layer
    • Quality, lineage, and governance
  2. 02

    AI & Machine Learning

    Models in production, not just notebooks.

    AI features that survive contact with real users, with the evaluation, guardrails, and cost control to keep them that way.

    Typical stack

    • PyTorch
    • TensorFlow
    • LangChain
    • Hugging Face
    • MLflow

    Applied AI

    • LLM applications, retrieval-augmented generation, and agents

      We build AI that ships: generation grounded in your own data, with evaluation, guardrails, and cost controls, not a demo that falls over the moment a real user touches it.

    • Predictive models and recommendation systems
    • Computer vision and document intelligence

    MLOps

    • Training, evaluation, and deployment pipelines
    • Monitoring for drift and performance
  3. 03

    Analytics & BI

    Decisions from data, in real time.

    From raw events to a number a decision-maker can act on, without waiting on a ticket to the data team.

    Typical stack

    • Looker
    • Power BI
    • Tableau
    • dbt

    Insight

    • Dashboards and embedded analytics

      Analytics where the work happens: live metrics embedded in your product and your operators' tools, not a separate portal nobody opens.

    • Metrics and semantic layers
    • Self-serve exploration for non-technical teams

    Foundations

    • Event tracking and product analytics
    • Experimentation and A/B frameworks
  4. 04

    Search & Personalization

    Find and surface what matters.

    Search and recommendations that put the right result first, whether by keyword, by meaning, or by behavior.

    Typical stack

    • Elasticsearch
    • OpenSearch
    • Pinecone
    • Vector DBs

    Find

    • Full-text and vector search

      We combine classic keyword search with semantic, vector-based retrieval, so users find what they meant, not just what they typed.

    • Ranking, relevance, and tuning

    Personalize

    • Recommendation and personalization engines
    • Hybrid and semantic search
03

How we work

Discovery to operations, one accountable team.

We are not a body shop that hands you code and disappears. We take a problem from the first whiteboard to a system you can run, and we stay accountable for the outcome.

  1. Step 01

    Discover

    We learn your business, the constraints, and what success actually looks like, then write it down so everyone is solving the same problem.

  2. Step 02

    Design

    Architecture, interfaces, and a plan that de-risks the hard parts first, reviewed with you before a line of production code is written.

  3. Step 03

    Build

    Senior engineers shipping in short iterations, with automated testing and continuous delivery from day one, not day ninety.

  4. Step 04

    Ship & operate

    We deploy, instrument, and stay on to operate and improve, so the project does not become someone else's emergency.

04

Ways to work with us

Engagements that fit the problem.

Whether you need a whole product built or two senior engineers embedded in your team, the model bends to you.

  • 01

    Project delivery

    A defined outcome, a fixed scope, and a team that owns it end to end, from discovery to launch and beyond.

  • 02

    Dedicated teams

    A standing, cross-functional team that works as an extension of yours, with the continuity to own a product over years.

  • 03

    Staff augmentation

    Senior engineers embedded in your team and your process, adding capacity and expertise exactly where you need it.

  • 04

    Advisory & review

    Architecture review, technical due diligence, and fractional engineering leadership when you need a trusted second opinion.

05

The other five

One house. Six facets.

The same bench, held to the same bar, across the other four practices and the product that proves them.

Start a project

Bring us the data nobody trusts yet.

Tell us where the data lives, what you want it to decide, and what has already been tried. We will tell you, plainly, what to build first.

Start a projectSee the platform we built

A real engineer will read it and reply, usually within one business day.

Olympas LLC · 111 Calvin St, Savoy, IL · From global supply chains to the shop counter