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Data & Cloud

The data platform underneath every AI system that works

We build and modernise the pipelines, warehouses, vector stores and cloud infrastructure that feed models with fresh, trustworthy data, and we automate the operations so it stays that way.

Talk to our expertsSee capabilities
PartitionedTime and tenant partitions so queries scan a fraction of the data
TestedQuality checks on every transformation before data reaches a model
OpenIceberg or Delta tables with no engine lock-in
Capabilities

What we deliver in Data Engineering & Cloud

Each capability is a scoped offering with clear deliverables. Combine them or start with one.

01

Data Pipelines & ETL/ELT

Batch and streaming pipelines on Spark, Airflow, Kafka and dbt with tests and lineage.

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02

Lakehouse & Warehouse Modernisation

Databricks, Snowflake and BigQuery platforms modelled for analytics and machine learning.

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03

Vector & Search Infrastructure

pgvector, Pinecone, Weaviate, Qdrant and Elasticsearch tuned for hybrid retrieval at scale.

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04

Cloud Migration & Validation

Move databases and workloads to AWS, GCP or Azure with automated validation at every step.

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05

DevOps & Platform Engineering

Kubernetes, CI/CD, infrastructure as code and observability for AI and application workloads.

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06

Cloud & AI Cost Optimisation

Cut cloud and model spend without cutting capability.

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How we work

AI doesn't fail at ideas. It fails at execution.

Every engagement follows a path from concept to scale with success defined up front, weekly releases and no surprises.

011–2 weeks

Discover

A 60-minute call and a deep-dive sprint that end in a scored opportunity map and a baseline.

021 week

Design

Architecture and stack chosen with a decision matrix and a model bake-off on your data.

034–8 weeks

Build

Weekly releases into your environment with evals, guardrails and traces from sprint one.

042–6 weeks

Scale

Hardening, staged rollout, cost controls and handover so your team runs it.

What every engagement is designed to

PartitionedTime and tenant partitions so queries scan a fraction of the data
TestedQuality checks on every transformation before data reaches a model
OpenIceberg or Delta tables with no engine lock-in
Architecture

Architectures we reach for in Data Engineering & Cloud

Reference patterns we adapt to your constraints. Each links to the full flow, components and trade-offs.

All architectures
Data

Lakehouse, semantic layer and analytics copilot

Raw data becomes governed metrics, then answers in plain language.

across every level

Hover or tap any part to see what it does. The light shows the order a request moves through.

Apache Iceberg / Delta Lakedbt / SQLMeshSnowflake / BigQuery / DatabricksCubeCortex Analyst / Genie
Full architecture
Tech stack

Building on proven, scalable foundations

The strength of any AI system lies in the technology behind it. We choose per workload, benchmark on your data, and build so you can switch.

  • Python
  • TypeScript
  • Node.js
  • Go
  • Rust
  • SQL
  • Bun
  • Swift
  • Kotlin
  • Solidity
FAQ

Get the clarity you deserve

Straight answers to the questions we hear most. Ask us anything else on a call.

AWS, Google Cloud and Azure, plus Databricks and Snowflake on top of them. We also run on-premise and hybrid setups where required.

Let's build intelligent systems that drive growth

Tachyon is the engineering partner for teams that need AI in production, not in a deck. Start with a free 60-minute discovery call.

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Contact

Talk to experts about your product idea

Every great partnership begins with a conversation. Whether you are exploring possibilities or ready to scale, tell us what you are actually trying to build.

Prefer to talk?

Pick a 60-minute slot. No pitch, just an engineer with honest answers.

Book a call

NDA available on request. We reply within one business day.