Manufacturing · FMCG
A data stack rebuilt to decide, not just report
Stack used
The Intelligence capability
Turn data into decisions your business actually acts on
Intelligence is what your business knows, and how fast it decides. Most analytics stop at the dashboard. We close the last mile, from raw data to the call a leader makes on Monday.
Why it matters
There is no AI strategy without a data strategy
Every AI ambition runs back to the same question: can the business trust what it knows? Frontier models do not fix a broken foundation. They scale it.
Intelligence is not the dashboard. It is the decision the dashboard was supposed to inform.
01
AI runs on what you know
A model is only as good as the data beneath it. Get the foundation wrong and you scale the error.
02
One source of truth
A decision needs a single trusted number, not ten reports that quietly disagree.
03
The last mile is the decision
Most analytics stop at the dashboard. The work is closing the gap to the call itself.
04
Intelligence compounds
Every good decision sharpens the next. The advantage builds quietly, then all at once.
How we work
From raw data to the decision a leader makes
One forward path, not a pile of tools. Each stage earns the next, and the whole thing is built to end in a decision, not a report.
01Sources
Wherever your data lives: the floor, the ledger, the field, the spreadsheet nobody admits to.
02Foundation
Decision-grade data. One source of truth, modelled and trusted across the business.
03Models
Forecasts and scenarios that fit how the business actually moves: demand, supply, risk.
04Decisions
Analytics built into the call a leader makes, not parked in a dashboard they ignore.
05Leverage
Margin, throughput, decision speed. The result on the P&L, compounding over time.
What we do
The Intelligence services, in the order they pay back
Three tiers, mapped to the Aidapt Method. Strategy first, then the foundation, then the capability that keeps it compounding. Not a menu: the diagnostic decides where to enter.
Tier 1
Translate > Decide
Strategy before stack
Data and AI roadmap
A sequenced, prioritised path grounded in where you actually are, written in the language of your operating model. It survives the first board meeting and guides the next three years.
Use-case identification
We map AI use cases across the business: by department, by data readiness, by value on the P&L. You invest in what will work, not what demos well.
The right kind of AI
Assistive, workflow, agentic or generative: each fits a different problem and demands a different data maturity. We match the type to the job before anything is bought.
The output: a sized business case and two to four viable paths. A decision, not a deck.
Tier 2
Design > Build
The foundation, delivered
Data platform and architecture
The governed, scalable foundation everything else runs on. Built for the AI workloads you are planning, not just the reporting you have today.
Engineering and pipelines
Automated, reliable pipelines that connect every source to one source of truth. The plumbing that makes intelligence possible, invisible when it works.
Analytics and visualisation
Governed data turned into decisions. Reporting and self-service analytics for everyone who has to act, not just the data team.
LLM decision support
Frontier models put to work on your data, answering the questions a leader actually asks out loud, with the reasons attached.
The output: one source of truth the business trusts, delivering from the first sprint.
Tier 3
Embed > Evolve
Capability that stays
Fluency and upskilling
Structured programmes that build data fluency at every level, from the boardroom to the floor. Role-based, hands-on, and judged by changed habits.
Senior practitioners, embedded
Capability transferred by doing the work together, not by a training course. Senior people in your team, coaching through every cycle.
Performance that feeds back
We track what the intelligence changes: decision speed, forecast accuracy, time recovered. What the business learns reshapes what gets built next.
The output: an organisation that improves continuously, with or without us in the room.
The right kind of AI
Not all AI is the same
Different problems need different types of AI, and each type demands a different data maturity before it works on a real floor. Pick one to see what it does and what it needs.
Helps your people decide better
AI that sits beside the decision-maker. It reads everything, surfaces what matters, and attaches the reasons. Your planner still makes the call. They just make it with the whole picture.
Proof
Intelligence work, and the instruments behind it
The outcome is ours. The tools are evidence of how we got there, never the headline.
Financial services
Decision-grade data in the bank's operating model
Stack used
Financial services
One source of truth across the bank
Stack used
Representative stacks shown for illustration. Engagement detail and outcome metrics on confirmation.
The instruments
Our instruments, never our product
The warehouse, the modelling layer, the BI surface: each is an instrument we choose for your context. We hold commercial partnerships across this ecosystem, and we are open about it. The protection is the order: the diagnostic picks the stack, the outcome prices the work.
The order is the moat. Outcome first. The stack is the last decision, not the first.
The data foundation
Snowflake
The cloud data platform where one source of truth lives, governed and scalable.
Why this instrument
Decision-grade data needs a home built for the AI workloads you are planning, not just the reporting you have. When the context calls for it, we build here.
The analytics surface
Sigma
Analytics that works like the spreadsheet your operators already trust, on governed data.
Why this instrument
Adoption beats elegance. Your team opens it because it feels familiar, and every number underneath is the same source of truth.
The frontier model
Claude
Anthropic's frontier AI: the reasoning engine behind our assistive and decision-support builds.
Why this instrument
The floor needs answers with the reasons attached. Judgement-heavy work demands a model built for nuance and guardrails.
The wider ecosystem, chosen per engagement
Common questions
Asked by operators, answered plainly
Do we need a data strategy before an AI strategy?
Yes. Every AI ambition runs back to whether the business can trust what it knows. A model on a broken foundation scales the error. The first tier of our work exists precisely so the later tiers hold.
Can you build on the systems we already have?
Usually, yes. Our instruments are chosen for your context. Where what you have is sound, we extend it. Where it is not, the diagnostic will say so plainly, with the business case for the change.
How long before we see something working?
Weeks, not quarters. The diagnostic itself lands in weeks, and delivery runs in sprints from a working foundation, not a big-bang launch at the end. Each cycle ships something the business can use.
What does it cost?
The Impact Analysis is free. The Diagnostic is fixed-fee, agreed before we start. The Engagement is priced to the outcome it is named for. No meter running, no surprise change orders.
Is our business too small for this?
Size is the wrong test. The right test: do you make, move or sell real things, and does an operator own the outcome? If yes, the diagnostic will tell you honestly whether the leverage is worth the spend.
Which tools do you use?
Our instruments, never our product. Snowflake, Databricks, dbt, Sigma, AWS, Microsoft, Anthropic and others, chosen per engagement. The stack is the last decision we make, not the first.
Start here
Start with a translation, not a tool
Every engagement enters through one doorway: a diagnostic, not a tool. Begin with a free Impact Analysis, or go straight to the Diagnostic when you are ready.