Three layers between a question and a decision
When one layer is weak, meetings turn into arguments over whose number is right, and budget goes to the wrong place. We fix the layer holding you back, or build all three.
Logizly · Data & AI consultancy
We help you avoid waiting two weeks to answer the board.
Most teams spend more of the week finding where the data lives, checking it and arguing about which number is right than they spend on the analysis. We fix the tracking and build a data model AI can search for you, so every question starts from figures the whole team accepts. Your people spend their time on insight and decisions, and the first results land in the first sprint.
Free 30 minute call with the engineer who would do the work. You keep the first fix whether or not you hire us.
Marketing spend measured against real finance applications, including offline ones, for the first time
US lease-to-own lender
Teams we have built data for
Royal CaninCigna HKEukanubaCircle LineGinmoniQuantiGrippiEnclave
What we do
01 · Digital analytics 02 · Data platform 03 · AI on your data
Collect the data that decides where money goes
Tracking that records every sign-up, sale and lead against the campaign that paid for it, so budget moves to what works.
GA4 and Adobe, server-side tagging, event design, attribution, consent and platform migrations.
Model your business so answers come fast
We write down how your business works in code: customers, orders, refunds, revenue and how they connect. People and AI query that model, so nobody hunts for the right table and every figure shows its working.
BigQuery, Snowflake or Databricks, tested dbt models, a semantic layer (dbt, Cube or LookML), orchestration and BI.
Ask a question and get an answer you can check
AI fetches, drafts and summarises in seconds. A person who knows the business checks the figure and makes the call.
Agents grounded in your metric definitions, with evaluation sets, access controls and a query log for every answer.
Give AI a map of your business
AI does the searching, so your team works on what the numbers mean
An AI tool pointed at raw tables has to guess which one holds revenue, whether refunds come off and what counts as an active customer. It guesses quickly and often gets it wrong. A data model answers those questions in advance. With one in place, AI writes the right query in seconds, shows the definition it used, and your team checks the figure and moves straight on to what it means.
Website & app trackingAd platforms & CRMProduct eventsFinance & billing
Warehouse + dbt modelsSemantic layer: metrics & entitiesTests & access controls
For peopleDashboardsBoard packsAnalyst sign-off
For AIPlain-English questionsDrafted analysisAutomations
AI for speed. People for judgement.
What changes for your team
Most of the time spent on analysis goes on reaching the data: finding the right table, exporting it, checking it matches last month. AI takes over that retrieval and does it in seconds, reading from a data model that defines what each number means. Your people start from figures they can rely on and spend their time on the decision.
What this means for you
- Routine figures fetched by AI without a ticket or a wait for next week’s report
- Every figure comes with the query and definition behind it, so anyone can check it
- Analysts spend their week on analysis and recommendations
- A person who knows the business signs off anything that moves money
Results
Selected client work
What changed for lenders, insurers, retailers and global brands once they could rely on their data.
Explore all 24 case studies US lease-to-own lender FinTech · Consumer lending Royal Canin FMCG · Pet nutrition Forex backtesting platform FinTech · Trading SaaS
Ad spend matched to real applications, including the offline ones.
We rebuilt tracking on a server-side GA4 setup, brought every source into one warehouse and built attribution that ties spend to completed finance applications.
OfflineApplications matched
Server-sideData collection
OneMarketing warehouse
A global GA4 migration with no break in measurement for any market.
We designed a global analytics schema for Royal Canin, migrated a very large multi-market site to GA4 and piped the raw data to its Azure and Databricks teams.
ContinuousMeasurement
AutomatedData pipelines
GlobalShared standards
Spend tied to signups and revenue during a fast growth push.
We built the acquisition dashboard and product analytics for a fast-growing trading replay tool, so the team could see cost per acquisition by channel and how traders use the product.
ClearCost per acquisition
TrackedTrader behaviour
Data-ledRoadmap
All services
Every service, by layer
01 Digital analytics
02 Data platform
Data Platform Build
A tested data model of your business that people and AI tools can query.
Warehouse Migration
Move warehouses on a fixed date, with proof the numbers match.
Data Team Modernisation
Free your data team from the ticket queue to work on what moves revenue.
Embedded Analytics
Dashboards inside your product that customers will pay for.
03 AI on your data
Across all three
Industries we work in Ecommerce & DTC B2B SaaS Fintech & lending Insurance Healthcare & telehealth Consumer brands Travel & attractions Agencies
Compare
The smallest fix that solves the problem
Most consultancies bill by the hour, which rewards complexity. We earn repeat work by solving the problem you called about in the simplest way that holds up.
Traditional consultancy
- Months of discovery before anything ships
- A partner sells the work and juniors deliver it
- The biggest solution gets recommended because it bills the most
- AI pilots that make up numbers
- A retainer designed to be hard to leave
Logizly
- Something working in the first sprint
- The engineer on your first call does the work
- The smallest fix that solves the problem gets recommended first
- AI that shows its working, checked by a person
- Everything in your accounts and documented, so you can stop any time
The modern stack we build on
BigQuerySnowflakedbtDagsterCubeLookerFivetranClaude
How we work
How an engagement starts
The work runs in three steps. You see working output within weeks and can stop after any stage.
01
Discovery call
A 30 minute call with the engineer who would do the work. We look at your setup, tell you what we would fix first, and whether you need us for it.
Day 1 02
Blueprint sprint
We map your sources, stakeholders and goals, then give you an architecture and a prioritised roadmap. You keep it whether or not we do the build.
2 to 4 weeks 03
Build in sprints
We ship working tracking, models, dashboards and agents every two weeks. You can stop after any sprint, and everything built stays yours.
Every 2 weeksWhat happens on the call
Book your call- 30 minutes with the engineer who would do the work
- No slides or sales pitch
- A clear answer on whether we can help, and a referral if we can’t
- The first fix we would make, which you can use either way
Questions?
What does Logizly do?
We cut the time your team spends finding data and debating which figure is right, so its time goes on insight and decisions. The work sits in three layers: tracking that records what customers do and what it earned you, a data model that defines what every number in your business means, and AI that uses that model to fetch figures and draft analysis while your people check it and decide. We also fix single problems in existing setups.
Who is it for?
Any company where money decisions depend on data: marketing budgets, pricing, retention, stock. We have worked in lending, insurance, credit unions and SaaS. Whatever the industry, the questions run through the same three layers.
How quickly will we see results?
The blueprint lands in two to four weeks. After that we ship in two-week sprints, so something usable reaches stakeholders early and often.
Do we get locked in?
No. Everything lives in your cloud and your repositories, documented and tested, and your team is trained to run it. Many projects end at handover. Clients who keep working with us month to month do so by choice and can stop with a month’s notice.
Which tools do you use?
Whatever fits what you already have. For tracking, usually GA4 or Adobe with server-side tagging. For the platform, BigQuery, Snowflake or Databricks with dbt. For AI, Claude, Gemini or OpenAI running in your own cloud.