The AI-ready foundation your models run on
The pipelines, lakehouse and serving layer that make AI possible — data engineered to be reliable, real-time and ready for every model, agent and copilot downstream.
the foundation your BI, ML & agents all run on
/ the difference a foundation makes
Same business.
Two outcomes.
AI reliability
of AI projects fail
pipeline uptime
Cost of data
lost every year
lower platform cost
Your team
of time lost to prep
of time handed back
/ how raw data becomes trusted
Watch raw data become AI-ready
/ what we engineer
Every stage of your platform
Built for production from day one — data flows through four engineered stages into a trusted, governed, cost-tuned platform.
Reliable pipelines
Observable ELT, streaming & CDC that flag drift before it bites.
Quality & observability
Contracts, freshness checks & lineage — trust every number to its source.
Governance by design
Cataloguing, access & PII control wired in from day one.
Cost & performance
Right-sized lakehouse + FinOps that cut spend, not capability.
/ the payoff
Build it once.
Reuse it forever.
A trusted foundation isn’t a cost centre — it’s leverage. Every capability you stack on top inherits its reliability, speed and governance for free.
the layer everything runs on
Trusted, governed data foundation
/ the foundation dividend
Time your team
gets back
Data people lose ~45% of their week to prep. Drag in your team and see the capacity a foundation hands back.
Capacity lost to data prep annually
You keep / year
$324.0KTeam stuck in data prep
2.7 FTEs handed backHours on data prep / year
5,184 hrs hrs returnedA trusted foundation hands 2.7 FTEs back — ≈ $324.0K of capacity a year.
/ your stack, our craft
Your stack.
Never locked in.
We’re tool-agnostic and pick for your fit, not ours — then document everything and hand it to your team.
Databricks / Lakehouse
Unified lakehouse with Delta, Unity Catalog and governed sharing.
Snowflake
Elastic warehousing with cost guardrails and zero-copy clones.
BigQuery
Serverless scale, partitioning and slot management tuned for spend.
Native cloud (AWS/Azure/GCP)
First-party services where they fit — no needless middleware.
Streaming (Kafka / Flink)
Real-time and CDC pipelines for data that can't wait for a batch.
dbt & orchestration
Tested, version-controlled transforms with lineage end to end.
/ the proof
A foundation,
quantified
It pays back in uptime, time handed to your people, and lower cloud cost:
pipeline uptime
AMDIM SLAs
of DS time lost to data prep
Anaconda survey
lower data-platform cost
AMDIM engagements
Data teams
45% of their time back
Analysts
fresh, trusted data
Finance
lower, predictable cloud cost
/ the depth
Beyond the basics —
the full toolbox
5 capabilities, each backed by a real toolbox — batch to real-time / AI-native. A taste below; the full library runs deep.
techniques · 10 disciplines
Ingest & stream
get every source in, reliably
Store & model
the lakehouse foundation
Transform & orchestrate
raw into trusted, on schedule
Trust
quality, tests & contracts
Govern & catalog
found, understood & safe
/ your industry
The hard problems
that actually pay
Every sector has a handful of problems that are genuinely hard — and genuinely worth it. Pick your industry for a taste; the full set lives on the industries hub.
Manufacturing
why it’s hard — Defects are rare events on fast lines; models run at the edge in real time, with near-zero tolerance for a missed fault.
$50B/yr lost to unplanned downtime (Deloitte); AI-in-manufacturing ~35% CAGR to 2030 (Grand View).
High-throughput sensor & PLC telemetry pipelines
Land millions of tags a second from PLCs, historians and edge devices into the lakehouse without dropping a reading.
Edge-model decisions are only as fresh as the stream feeding them — and OT data arrives faster than any nightly batch can hold.
evidencePoor data quality costs organisations ~$12.9M/yr on average (Gartner)
Contracts across OT/IT and legacy historians
Data contracts and schema-registry evolution so a firmware or tag change doesn't silently corrupt the quality feed.
200 sensors moved and one tag was renamed — downstream the batch model never noticed until scrap piled up.
Genealogy lineage from raw material to finished unit
Column-level lineage stitching MES, ERP and quality data into a queryable per-unit genealogy for recall containment.
When a defect ships, containment cost is set by how fast you can trace every unit that shared the bad lot.
/ why it pays
Outcomes you can measure
pipeline uptime
Reliable pipelines
Automated, observable ingestion and transformation with built-in data-quality tests.
data latency
Real-time by default
Streaming and CDC architectures so decisions run on fresh data, not yesterday's.
lower data cost
Cost-tuned platforms
Right-sized warehouse and lakehouse design that cuts cloud spend, not capability.
AMDIM SLAs · Anaconda survey · AMDIM engagements — verified, not invented.
/ what we deliver
End-to-end, not half-built.
/ before you commit
Fair questions
That’s exactly what we baseline first — most engagements open with the Data Readiness Scorecard, then stabilise and instrument what you already have before modernising incrementally. You never start with a blank-slate rebuild.
See where you stand in 5 minutes.
Take the Data Readiness Scorecard — a tailored score and the fastest path to impact.