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services/data engineering & analytics

clean, governed data your agents can trust.

We build the pipelines, warehouses, and governance that turn scattered data into a reliable foundation for analytics, for decisions, and for the AI agents that depend on it.

Book a discovery callhow we work
the gap

Agents need clean, governed,
accessible data.
Most don’t have it yet.

We build the pipelines, models, and governance that make your data trustworthy, then keep it that way as it scales.

what we deliver.

01

Data pipelines & ELT/ETL: batch and streaming, from source systems to a governed warehouse.

02

Lakehouse & warehouse architecture: modelled, documented, and query-ready.

03

Data governance & quality: lineage, testing, and observability you can audit.

04

Analytics & BI: metrics layers and dashboards your teams actually use.

05

Vector & feature stores: the retrieval and feature infrastructure that powers RAG and ML.

the stack.

headline technologies
Azure SQLSQL ServerADFdbtSnowflakeDatabricks
show full stackhide full stack
AzureAirflowPython
how we build · five phases, one loop

the agentic sdlc.

Agents run the build, people own the gates where judgment matters, and every release feeds what we learn back into the next one.

01/feature

specify

Stakeholder interviews, spec + acceptance criteria

UNDER THE HOODspec.md + acceptance/*.feature + success criteria
HUMAN GATEPM/BA confirms the spec traces back to validated evidence
02/design

design

Two tracks, in parallel

UX TRACKDetailed flows, states, usability checksFinal designs + UX context the agents build from
TECH TRACKArchitecture, key technical decisions, securitySystem design + a record of every key decision
HUMAN GATEDesigner signs off the UX checkpoint; Senior Developer approves the architecture
03/implement

build

Agent-run: plan, implement, test

plansubagent TDDe2e wiring
UNDER THE HOODplan.md, code + tests, 1 commit/task
AGENT CHECKSuite green, no blocked tasks. Human sign-off only for large or high-risk changes
04/implement

verify

Code, experience and AI behaviour

UNDER THE HOODreview-report.md, ux-review.md, approved PR
HUMAN GATESenior Developer + Designer are final approvers
05manual

ship + learn

Deploy, observe, measure use, retro

UNDER THE HOODmerged PR, adoption metrics, context updates
HUMAN GATEProduct Owner accepts the outcome, measured by use
production learning flows back into specs and UX context

five phases, four human gates. each phase produces a named artefact; each human gate is owned by a named person.

HUMAN GATEowned by a named person
AGENT CHECKautomated, escalates when risk is high

Gates scale with risk: low-risk changes pass specify, verify and ship; high-risk work passes every gate plus security review.

proof

One source of truth, trusted enough to build on.

We consolidated operational data from legacy and modern systems into a governed Azure SQL warehouse, rebuilt the ADF pipelines that feed it, and delivered a shared model now used for Power BI reporting.

see our work

how we engage.

01

free proof of concept

We build a working agent or integration against your real use case. No cost, no commitment. You evaluate outcomes, not slideware.

02

milestone-based delivery

Production build-out priced against agreed milestones and success criteria. You pay for results.

Longer horizon? Dedicated teams: long-term product engineering and AI enablement capacity, embedded with your organization.

start with an hour.

A discovery call, then a free proof of concept. You evaluate outcomes, not slideware.

Book a discovery call