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Data platforms and AI systems built to run in production.

Data engineering, lakehouses, analytics and AI for companies in Europe, North America, the Gulf and Central Asia. Work with us as a project team, or add our engineers to yours.

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Your first call is 30 minutes with a senior engineer.

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Platforms our engineers build on

Listing a technology does not imply a vendor partnership or certification.

  • AWS
  • Microsoft Azure
  • Google Cloud
  • Databricks
  • Snowflake
  • Microsoft Fabric
  • Apache Spark
  • Apache Airflow
  • dbt
  • Apache Kafka
  • OpenAI
  • Anthropic Claude
  • MLflow
  • Power BI
  • Kubernetes
  • Terraform
6

service lines, each with its own page

40

tools across 7 platform layers, mapped below

1wk

between demos of working code in your repositories

3

data tests on every pipeline: schema, freshness and volume

Six service lines, scoped like a statement of work

  1. Data Engineering

    Scope

    Ingestion, orchestration and pipelines with tests and monitoring built in.

    stackAirflow · dbt · Kafka · Spark · Fivetran

    Deliverables

    • Batch and streaming pipelines
    • Orchestration and CI/CD
    • Data quality tests
    Discuss your pipelines

  2. Data Warehouse & Lakehouse

    Scope

    Medallion lakehouses and cloud warehouses, designed or migrated off legacy systems.

    stackDatabricks · Snowflake · Fabric · BigQuery · Iceberg

    Deliverables

    • Architecture and migration
    • Bronze, silver and gold layers
    • Cost and performance tuning
    Discuss your platform

  3. Data Analytics & BI

    Scope

    Shared metric definitions, a semantic layer and the dashboards built on them.

    stackdbt · Power BI · Looker · Tableau

    Deliverables

    • dbt models and metrics
    • Semantic layer
    • Dashboards and reports
    Discuss your reporting

  5. Cloud Data Solutions

    Scope

    Data infrastructure on AWS, Azure and Google Cloud with cost and access controls.

    stackAWS · Azure · Google Cloud · Terraform · Kubernetes

    Deliverables

    • Landing zones and IaC
    • Identity and access control
    • FinOps reporting
    Discuss your cloud

  6. IT Outsourcing

    Scope

    Data, analytics and AI engineers who join your team, reviewed by our architects.

    works inyour repositories, tickets and stand-ups

    Deliverables

    • Dedicated engineers
    • Squads for projects
    • Senior code review
    Request engineers

Our delivery standard, as config

Every engagement runs on the same delivery standard. It is short enough to read in a minute and specific enough to hold us to.

  1. 1CallWe talk through your current platform, your constraints and the decision you need to make.
  2. 2AssessmentWe review sources, architecture, costs and risks, then send a written plan.
  3. 3BuildWe build in your repositories, with tests and monitoring from day one, and demo the work every week.
  4. 4Handover or runYou get runbooks, recorded training and open formats, so your team can run it alone. Or we keep running it with you.
Book a consultation
delivery.yamlmain
delivery:
code_ownership: client # repos live in your org
review: senior_architect # every merge request
tests: [schema, freshness, volume]
observability: day_one # alert on every failed test
docs: [runbook, architecture_diagram]
demos: weekly
ai:
evaluation: every_release
tracing: production
handover:
training: recorded
lock_in: none # open formats, your cloud

Every AI answer traced and costed

We build retrieval assistants, agents and ML models on governed data, test them before each release and trace them in production.

  • retrieve · rerankRetrieval assistants and agentsAnswers cite your documents and respect each user’s permissions.
  • faithfulness ≥ targetEvaluation before every releaseEach release runs against a test set and has to meet agreed quality scores.
  • guardrail.*Guardrails and approvalsAccess follows your identity system, and a person approves any risky action.
  • $ / answer · p95 sTraced and costed in productionOne dashboard shows the latency, cost and quality of every request.
Discuss your AI use case
trace · contracts-assistant · answerexample
answer_request1.84 s
guardrail.input0.07 s
retrieve0.21 s
rerank0.09 s
llm.generate1.32 s
guardrail.output0.12 s
0.94faithfulness
$0.004cost / answer
3sources cited
Illustrative trace with example values, drawn to scale on a 0 to 1.84 s axis.

The stack, layer by layer

Cloud

  • AWS
  • Microsoft Azure
  • Google Cloud

Used by service lines 05

Lakehouse & warehouse

  • Databricks
  • Snowflake
  • Microsoft Fabric
  • BigQuery
  • Redshift
  • Apache Iceberg
  • Delta Lake

Used by service lines 02

Pipelines & streaming

  • Apache Airflow
  • dbt
  • Azure Data Factory
  • Fivetran
  • Apache Kafka
  • Apache Spark
  • Flink

Used by service lines 01

AI & LLM

  • OpenAI
  • Anthropic Claude
  • Amazon Bedrock
  • Azure AI Foundry
  • Vertex AI
  • LangChain
  • LangGraph
  • MCP

Used by service lines 04

ML & LLMOps

  • MLflow
  • SageMaker
  • Databricks Mosaic AI
  • Langfuse
  • Weights & Biases

Used by service lines 04

Governance

  • Unity Catalog
  • Microsoft Purview
  • Great Expectations
  • Monte Carlo

Used by service lines 02 · 03

BI & infrastructure

  • Power BI
  • Looker
  • Tableau
  • Terraform
  • Kubernetes
  • GitHub Actions

Used by service lines 03 · 05

Not on the list?

Tell us your stack in the form and we match engineers to it.

Describe your stack

Build data and AI systems with us

We hire data, analytics and AI engineers for international projects, and we train new engineers through our internship.

The internship is free and selective.

Apply for a role

  • Senior Data Engineer

    Tashkent or remoteFull-time
    View role

  • AI / LLM Engineer

    Tashkent or remoteFull-time
    View role

  • Data & AI Engineering Internship

    1 November 2026 to 31 January 2027Internship · free

    New engineers build real pipelines under our architects’ review and can move into project teams.

    Apply for the internship

Before you book

Not answered here? Ask in the request form.

  1. Who owns the code and the data?

    You do. Code lives in your repositories and data stays in your cloud, in open formats.

  2. Can your engineers work inside our team and tools?

    Yes. They join your stand-ups, repositories and ticketing, and our senior architects review their merge requests.

  3. How do you handle access to our systems and data?

    We sign an NDA before any access. Our engineers then get least-privilege accounts through your identity system, so you can see and revoke each one.

  4. What happens in the first consultation?

    We go through your stack, goals and constraints, and say plainly if we are not the right fit. Afterwards you get a short written summary of next steps.

  5. How is pricing set?

    We price each engagement from the written plan we send after the assessment, which sets out the scope, the team and the timeline.

Book a consultation

Pick a time for 30 minutes with a senior engineer, or send the form and we’ll reply.

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