AI & data platform

One governed foundation. Every decision built on top of it.

Most analytics stalls because the data is untrusted and the model never leaves the notebook. We build the whole chain — ingestion, governance, machine learning, generative AI, and the surface where the decision actually gets made.

0.0%

Pipeline reliability

0 wks

Time to first model

Governed data products

0 min

Decision latency

Reference architecture

The blueprint we deploy on cloud data platforms — sources on the left, governed medallion warehouse and ML in the middle, consumption on the right, with governance and shared services running underneath.

Click any block to inspect it — hover for a quick definition

Sources

Data foundation & analytics — cloud platform

Orchestration & scheduling

Storage & processing

ML & data science

Data governance — knowledge catalog

Consumption layer

Source to landing

Campaign input

Marketing campaign extracts land as scheduled batch files with offer, channel and audience keys.

Sample data flow

campaign_extract_2026_08_19.parquet

campaign_id: CMP-88213
channel: email · paid_social
audience_size: 412,880
offer_code: SAVE15-AUG

SFTP dropCSV / ParquetDaily 02:00 UTC

How this layer flows

Extract

Validate contract

Tokenise PII

Land immutably

Contracts checked at the door — bad data is quarantined, not propagated.

Try it

Paste a requirement. Watch it become a pipeline.

A simulation of how we sequence an engagement — ingestion, enrichment, warehouse, GenAI and vector search, then the insight and the action. It runs entirely in your browser and mirrors the real five-stage delivery loop.

Ingestion

Enrichment

Warehouse

GenAI

Insights

The five stages the demo walks through.

Simulated pipeline — no data leaves your browser

Paste a requirement in plain language

1IngestionSources land under contract
2EnrichmentAirflow validates and conforms
3WarehouseBronze → Silver → Gold
4GenAI & vector searchModels trained and grounded
5Insights & actionDelivered where work happens

Watch & explore

The pipeline, explained visually

Why decision culture beats reporting culture

How we frame a business question before a single pipeline is written.

Source

Landing

Bronze

Silver

Gold

Model

Decision

Ingestion to insight, animated

Watch a record travel from landing through Bronze, Silver, Gold and into a decision.

Inside an AI-first delivery pod

One team frames, engineers, models and ships — no hand-offs over the wall.

The man-machine loop

Models propose, people decide, feedback retrains. The loop is the product.

Layers

Four layers, built and operated by one team

Sources

Campaign input, enterprise data warehouses, and third-party identity providers land as batch or historical loads.

Campaign input
Enterprise data warehouse
Identity & audience partners
Product and event streams

Explore Sources & ingestion

Data foundation

A governed cloud lakehouse: raw landing, validation and enrichment, then a medallion warehouse that earns trust layer by layer.

Cloud storage data lake
Managed orchestration (Airflow)
Bronze / Silver / Gold warehouse
Incremental quality & trust checks

Explore Data foundation

ML & decision intelligence

In-warehouse ML, generative AI, vector search, and custom models trained on the same governed tables the business reports on.

In-warehouse ML
Generative AI copilots
Vector search & retrieval
Custom decision models

Explore ML & decision intelligence

Consumption layer

Decisions get delivered where work happens — BI, experience platforms, campaign tools, and embedded product surfaces.

Executive BI & dashboards
Experience & campaign platforms
Embedded product surfaces
APIs and reverse ETL

Explore Consumption & activation

Proof

Before and after, measured against a baseline

Three engagements, three baselines agreed before the build. The numbers below are the measured delta, not a projection.

Telecom

Churn intercepted before the contract window closed

Problem — Retention teams saw churn only after the disconnect request. Scores lived in a spreadsheet refreshed monthly.

Approach — Daily Gold churn features, an in-warehouse propensity model, and a next-best-offer API embedded in the care agent console.

Monthly churn: 3.4%; from 4.6% (-26%)
Save-offer accept: 31%; from 12% (+158%)
Score latency: 1 days; from 30 days (-97%)

Before

4.6

After

4.6

Before

4.5

After

4.1

Before

4.7

After

3.7

Before

4.6

After

3.4

Discuss a similar problem

Retail

Inventory simulation that freed working capital

Problem — Replenishment ran on a fixed reorder point, so fast lines stocked out while slow lines tied up cash.

Approach — Demand forecasting on Silver sales history plus a constrained allocation optimiser feeding the planning tool nightly.

Forecast accuracy: 84%; from 61% (+38%)
Stock-out rate: 3.6%; from 9.2% (-61%)
Working capital: 76 idx; from 100 idx (-24%)

Before

After

Before

After

Before

After

Before

After

Discuss a similar problem

Pharma

A grounded copilot for field medical teams

Problem — Field teams waited days for evidence summaries; answers arrived without sources and could not be audited.

Approach — Vector search over approved documents with a retrieval-grounded copilot that cites every claim back to a governed source.

Answer turnaround: 2 hrs; from 48 hrs (-96%)
Cited responses: 100%; from 40% (+150%)
Analyst hours / wk: 46 hrs; from 120 hrs (-62%)

Before

After

Before

After

Before

After

Before

After

Discuss a similar problem

Maturity

From reporting to autonomous decisions

Every engagement plots where a function sits today and what the next stage is worth. The jump that matters is not more dashboards — it is moving from describing the past to prescribing the next action.

Impact

Typical movement in the first year

Ranges observed across retail, telecom, and pharma engagements. We baseline before we build, so the delta is measured, not claimed.

Churn reduction18%

Forecast accuracy31%

Margin uplift12%

Inventory freed24%

Cycle-time cut45%

How we deliver

A five-step loop, not a waterfall

Frame

Decompose the business question into decisions, constraints, and the value at stake.

Foundation

Land, validate, and model the data into governed Bronze, Silver, and Gold layers.

Learn

Train, evaluate, and explain models — classical ML, optimisation, and generative AI together.

Deploy

Ship into the tools people already use: BI, CRM, campaign platforms, product surfaces.

Operate

Monitor drift, quality, and adoption. Retrain on a cadence tied to how fast the world moves.

AI point of view

Machines scale. People judge. We engineer the handshake.

Decision sciences

Problem framing before modelling. We decompose a business question into decision variables, constraints, and the number that must move.

Man-machine ecosystem

Models handle scale and consistency; people handle context and judgement. We design the handshake between the two, and instrument it.

Generative AI in the workflow

Retrieval-grounded copilots over governed data, so an analyst asks in language and gets an answer traceable back to a Gold table.

Engineering the last mile

A score in a warehouse changes nothing. We ship the interface, the alert, and the API that put the decision in front of the operator.

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