Easy to recognise. Hard to quantify without the right data.
Two disciplines, one goal — turning the problems you recognise into systems that fix them.
Before any model can forecast demand or predict customer value, your data needs a foundation it can rely on. That's what Data Engineering builds.
● Data pipelines & integration
So your POS, ERP, and logistics data stops living in silos and starts feeding one unified view.
● Data platforms
So you have a single source of truth built for retail, not a patchwork of exports and spreadsheets.
● Automated reporting
So the weekly report that took three days arrives every morning before standup.
● Data quality & monitoring
So you can trust the numbers your models and dashboards are built on.
Retail generates more data than anyone can analyse manually. Data Science builds the models that find the patterns — in demand, in customer behaviour, and in the margin you're losing.
● Demand forecasting
So inventory follows real predicted demand, not last year’s averages or a buyer’s instinct.
● CLV prediction & segmentation
So retention spend goes to the customers actually worth keeping.
● Personalisation & recommendations
So your best customers see what’s relevant to them, not the same catalogue as everyone else.
● ML in production
So your models leave the notebook and reach the people who act on them.
What we've delivered — anonymised for confidentiality, measured for accountability.
We built a demand forecasting system to replace gut-feel inventory decisions. The model runs on existing ERP data, produces weekly SKU-level forecasts and cut excess inventory by 18% within one quarter.
We built a CLV prediction system to answer one question: which customers matter most? The system identifies the 12% generating 80% of revenue and spots the 45% quietly walking away.
The operations team spent 3 days every week compiling reports from 5 disconnected systems. We built a unified data platform that delivers automated reports every morning – reporting time cut from 3 days to 4 hours.
Candid conversations about the operational decisions, data challenges, and trade-offs that shape retail today.
Data Engineering and Data Science technical stack we master
Leave your details and a short description of the problem.
We'll reach out within one business day to schedule a call.
Founder
Most retailers are sitting on the answers to their hardest problems. The data is there. The systems to turn it into decisions aren't.
Leave your details and a short description of the problem.
We'll reach out within one business day to schedule a call.
That's exactly where most of our engagements start. The data exists: in your ERP, POS, e-commerce platform or spreadsheets, but nobody has built the systems to connect it, clean it and turn it into something your team can act on. We assess what you have, identify where the biggest leverage is, and build from there.
It depends on your current baseline, but most mid-market retailers using historical averages or buyer intuition see meaningful improvement within the first iteration. Our case study with a specialty retailer showed 18% reduction in excess inventory within one quarter. The first iteration is scoped specifically to prove whether the model outperforms your current approach — on your actual data, not hypothetical benchmarks.
Not at all — most of our clients don't. We operate as your external data engineering and data science team. Everything we build is designed to run on your existing infrastructure with minimal internal maintenance. Where ongoing involvement is needed, we document everything and can train your team or continue supporting it on T&M terms.
Large firms send generalist teams who built models for banks last month and telecom the month before. They spend weeks learning your domain before delivering anything useful — and you pay for that learning curve.
We work exclusively with retail. Our team already understands POS data, seasonal demand patterns, inventory dynamics, and retail-specific customer behaviour. That means faster time to value, fewer wasted hours, and models built by people who know what a retail operations team actually needs.
No bench warmers, no project managers who don't touch data. You work directly with senior data engineering and data science practitioners who build and deliver. Our T&M model means you pay for actual work, and the free first iteration means you see real output before any commercial commitment.
The first iteration is scoped to deliver a working output — not a report or a roadmap, but something tangible: a forecasting model, a data pipeline, or an analysis with specific recommendations, depending on whether the problem calls for data engineering, data science, or both. Typical timeline is 1-3 weeks. The output is designed to be immediately useful and to demonstrate whether a longer engagement would deliver ROI.
The 'Free Iteration Work Model' highlights how Dastellar stands out from the competition. We start our collaboration with a comprehensive planning phase, breaking down the execution into clear, manageable steps called iterations.
What is an Iteration?
The first iteration is entirely free, providing an opportunity to evaluate our mutual satisfaction with the process and results. If you're pleased with the outcomes, we can proceed to a long-term partnership, ensuring alignment and value from the outset.
