Services

Data & Cloud

AI-ready data, on infrastructure the business controls. Twistag designs, builds and runs the platforms, pipelines and integrations that reports, teams and agents read from every day.

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Millionsof data flows processed every day across client platforms.
50+enterprise integrations built between core systems.
100%of code, infrastructure and data owned by the client.
ISO 27001practice behind every platform Twistag delivers.

What changes for the business

Most AI stalls on data: spread across systems, out of date and owned by no one. Twistag builds the layer underneath, so reports, teams and agents all work from the same current numbers.

What Twistag builds

Three parts of one working system. Most engagements combine all three.

01 · AI-ready data platforms

One trusted source for every number.

Pipelines, warehouses and retrieval indexes that keep data current and in one place. Reports, dashboards and multi-agent systems all read from the same layer.

Map the sourcesERP, CRM, files and event streams, ranked by value.

Build the pipelinesdbt, Airflow and Airbyte, tested on every run.

Modernise the warehouseSnowflake, BigQuery, Databricks or Redshift, sized to the work.

Add retrievalVector storage, embeddings and RAG pipelines for agents.

Explore AI-Ready Data Platforms

02 · Strategy & governance

Data with an owner, a history and rules.

Quality, lineage and access controls are defined with the people who own each domain, so every figure can be explained and every agent only sees what it should.

Quality checksFreshness, completeness and accuracy tested daily.

LineageEvery figure traced to its source, for audit and AI training.

Access governanceSchema and role-based access, agents included.

GDPR and regulated dataPersonal data mapped, minimised and kept in the EU.

Explore Data Strategy & Governance

03 · System integration

Every core system in sync.

Agents and teams are only as useful as the systems they can reach. Twistag connects CRM, ERP, CDP and marketing platforms with real-time events, and builds the tool connections agents call.

Real-time syncEvent streams across CRM, ERP and CDP on Kafka or Kinesis.

Martech and adsIntegrations with ad, analytics and marketing platforms.

Agent tool connectionsAPIs and MCP servers agents can call safely.

Cloud as codeAWS, Google Cloud or Azure, defined in code and documented.

Explore System Integration

A concrete stair tower seen through a glass facade

Data use cases

Find where data pays back first.

Pick an industry and the teams to support. The list shows data projects companies like it are running, and what each one changes for the business.

1. IndustryQuestion 1 of 2

Security and compliance

Data stays where it belongs.

Personal and regulated data is mapped, minimised and kept in the client’s own cloud in the EU. Every access is logged, and every pipeline has an owner.

Network cables running into a server rack

EU data residency
Data stays in the client’s own cloud account, in EU regions.
GDPR by design
Personal data mapped, minimised and deleted on schedule.
Role-based access
Least-privilege access for people, services and agents.
Lineage and audit
Every change to data and schema recorded with its source.
Backups and recovery
Tested restores and documented recovery times.
ISO 27001 practice
Delivery follows ISO 27001 controls, with evidence ready for audits and procurement.

Approach

Every platform proves its value before it grows.

The audit and the first data product are small and fixed in time. The platform grows once the number agreed with leadership has moved.

01 · 2–3 weeks
Audit the data
Map sources, owners and quality, and what fixing each gap is worth, with the people who use the data.
02 · 6–8 weeks
Prove one data product
One pipeline or dataset in daily use, measured against the number agreed with leadership.
03 · Quarters
Build the platform
More sources, domains and teams on the same governed foundation, each faster than the last.
04 · Ongoing
Run and hand over
Monitored pipelines, runbooks and a client team that owns the platform end to end.

Start here

Not sure where to start?

Data Strategy & Governance: a value map of the data that matters, and one owner per key metric.

See Data Strategy & Governance

Plan already set

Data roadmap already set?

Three ways to bring a senior team in: a fixed-scope project, forward deployed engineers, or a partnership.

Ways to work together

Three ways to bring Twistag into a data plan that is already set.

Prove it

Fixed-scope
project

Best forIT and data leaders with a clear scope, budget and date: a migration, warehouse or platform, built and handed over.

Fixed price and cutover date, agreed before the start
Data quality checks written with the team up front
Infrastructure as code and documentation handed over in full

Send a project brief

Scale it

Forward Deployed
Engineering

Best forAn approved data roadmap that needs senior engineers who think like operators, inside the company’s own teams.

One to three senior data engineers, each with agents
Side by side with the analysts and operators
In the company’s cloud, tools and rituals

Explore FDE

With partners

Partners and
portfolios

Best forCompanies that build AI, data and product with Twistag: tech companies, consulting and PE firms.

Senior capacity for a tech company’s own roadmap
Tech delivery for consulting firm’s clients
AI playbooks rolled out across PE portcos

Explore a partnership

Results

Data platforms already at work.

Recent Data & Cloud work, measured the way each client’s leadership measures it.

50+sports organisationsDatatalksUnifying fan data across 50+ sports organisations

250K+contributorsDefined.aiScaling AI training data collection to 250K+ contributors

11 monthsto a live platformSource.appA production marketing attribution platform in 11 months

See all case studies

Clouds and platforms

Built on what the company already runs.

Twistag works across the major clouds and warehouses, and extends the platforms already in place before adding anything new.

Partner
ISO 27001Practice

Cloud: AWS, Google Cloud, Microsoft Azure
Warehouses: Snowflake, BigQuery, Databricks, Redshift
Pipelines and ETL: dbt, Apache Airflow, Airbyte
Streaming: Apache Kafka, Amazon Kinesis
Vector and search: Pinecone, pgvector, Elasticsearch, Supabase
AI models: Anthropic, OpenAI, AWS Bedrock

Questions

About Data & Cloud.

What does a data project cost?

Cost follows scope: the systems to connect, how mature the current data layer is, how much review is needed and how long the team stays. Discovery takes two to four weeks and a first data product six to twelve. A proposal follows within a week of the first call.

How soon can work start?

A thirty-minute call, a written proposal within seven days, and a discovery sprint of two to four weeks to confirm scope before the build.

Who owns what is built?

The client owns everything: source code, infrastructure as code, pipelines, models, evaluation datasets and documentation, handed over in full with training for the team.

Does the company need a data platform before starting with AI?

No. Twistag works with companies at any stage of maturity, and data preparation comes first wherever it is needed.

How does Twistag work with the internal team?

Inside the client’s own infrastructure and workflow, with shared backlogs, stand-ups and definition of done. Teams can be extended, or the work can be run end to end.

Can the internal team run the platform afterwards?

Yes. Build, train and transfer is one of Twistag’s operating principles: engineers pair during the build, every system is documented, and handover walkthroughs happen before the engagement ends.

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