Data Science Services
Data science services that turn a company's own history into a decision it can check
ZingZee designs, builds, and operates data science services for companies that hold years of sales, stock, booking, and cost records and decide by hand what those records could decide for them. Each model is trained on the client's own history, tested against past periods before it is used, shown beside the actual figures after go-live, and delivered through the five-phase framework on ZingZee's own hardware in Cyprus or on the client's servers.
What data science services are
Data science services are the work of building a model from a company's own records that answers a question the company currently answers by judgement: how much stock to hold, what price to set, which bid to place, and which customer will not renew.
ZingZee's data science services cover the assembly and cleaning of the history, the choice and training of the model, the test of that model against past periods it has not seen, the deployment of the model into the system where the decision is made, and the monitoring of its forecasts against what then happened. A model is a rule learned from the data where the business has never written the rule down. The output is a number on the screen where the decision is taken, with the actual figure beside it, so a manager can see whether the model improves on the previous method and overrule it when it does not.
Data science services that turn a company's own history into a decision it can check
Industries where ZingZee applies data science
ZingZee has applied data science services in retail, where a stationery retailer's replenishment runs on target levels that move with the sales rate over a live mirror of its ERP; in automotive, where a used-car importer's auction bids are set from a landed-cost model; in energy, where a solar installer's quotes are priced from imagery and a bill; in wholesale distribution, where a demand pool matches held orders to incoming stock; in travel and hospitality, where nightly prices and per-night deals on a villa rental platform are set from occupancy and demand; and in aviation training, where a prediction engine scores a candidate's readiness. The same services are offered to logistics, financial services, insurance, and manufacturing firms, regardless of size.
Data science services ZingZee provides
Demand forecasting and replenishment models
ZingZee builds models that forecast demand per item, per location, and per period from the sales history, seasonality, and promotions, and turn the forecast into a target stock level and a reorder quantity. The output is written to the replenishment screen or the pick list, and the forecast is shown beside the actual sales each week.
Pricing and bid models
ZingZee builds models that set a price or a bid ceiling from the cost history, the demand, the competitor and market data the client holds, and the margin the business requires. Nightly prices for a booking platform, a bid ceiling per auction lot, and a quote from a photograph and a bill are three shapes ZingZee has delivered.
Scoring, classification, and anomaly detection
ZingZee builds models that score a record for the attention it needs: a customer likely to lapse, an invoice that does not match its pattern, a payment that looks like a duplicate, a candidate likely to pass an assessment. Each score carries the features that drove it, so the person reviewing the record can see why it was raised.
Data preparation and feature pipelines
ZingZee assembles the history from the ERP, the booking system, the ledger, and the spreadsheets into one tested dataset, with the cleaning rules written down and re-run on every refresh. The pipeline decides whether the model can be trusted, and it is built and documented as production code.
Model deployment, monitoring, and retraining
ZingZee deploys the model inside the system where the decision is taken, records every forecast beside the actual outcome, and reports the error each period. When the error drifts past the agreed threshold, the model is retrained on the newer history and the new version is tested against the old one before it replaces it.
When data science is
the right choice.
When data science is the right choice
Data science is the right choice when a company holds two or more years of records on the decision in question and the decision is taken often enough for a small improvement to add up: a reorder placed every week, a price set every night, a bid placed on every lot, a renewal quoted for every client. It is the right choice when the current method is a manager's judgement or a spreadsheet formula that no longer matches the business, and when the cost of a wrong decision is visible as stock-outs, discounts, lost bids, or churn. It suits a company that already runs its operations on a database, because the history is then reachable and the model's output can be written back to the screen where the decision is taken. ZingZee trains and serves the models on its own hardware in Cyprus or on the client's servers, and the strategic assessment measures the history and the current decision before any model is proposed.
When data science is the wrong choice
Data science is the wrong choice when the decision is taken a few times a year, because there is no history to learn from and no volume to earn back the build. It is the wrong choice when the rule is already known, since a known rule belongs in ordinary code with tests; a VAT rate, a margin floor, and a contractual price are written as rules, and a model is used only where the rule is unknown. The same applies when the records are wrong, incomplete, or held in a format no system can read, in which case the first piece of work is a database and a pipeline, which ZingZee scopes separately. It is also the wrong choice where the output cannot be checked, because a forecast that is never compared with the actual figure can be neither trusted nor improved. The assessment states which case applies and proposes a report, a rule, or a database where that is the finding.
Use cases
Target stock levels move with the sales rate, a morning pick list follows the aisle layout, and slow lines are surfaced for a decision. For a stationery retailer in Cyprus, stock-outs fell below one percent and around EUR 45,000 of stock was released in the first four months.
A landed-cost model reads the lot, the shipping and duty for its route, and the margin for its class of vehicle, and returns a bid ceiling. For a used-car importer in Cyprus, the ceiling is produced in under eight seconds per lot and lodged automatically on both auction routes.
A quoting model reads the customer's electricity bill and satellite imagery of the roof, sizes the installation, and prices the job. For a solar installer in Cyprus, a quote that began with a site visit is produced in under ten minutes, and site visits are reserved for fewer than one roof in five.
Orders for out-of-stock lines are held in a ranked demand pool and matched against incoming stock as it is received, with the in-stock quantity shipped in the meantime. For a wholesale distributor in Cyprus, a held line releases the moment the goods land and the rep and the customer see the same figure.
A prediction engine reads a candidate's timed practice runs across every stage of an airline assessment and returns a probability of passing, with the weak stage flagged. For an aviation training product, the candidate decides whether to travel to the assessment on a score.
Data science engagement scope
Deliverables
The trained model, the data pipeline that feeds it, the deployment inside the client's system, the monitoring dashboard with forecast against actual, the back-test report, and the documentation, in daily use for the decision in question at the end of the engagement.
Deliverables
The trained model, the data pipeline that feeds it, the deployment inside the client's system, the monitoring dashboard with forecast against actual, the back-test report, and the documentation, in daily use for the decision in question at the end of the engagement.
Included as standard
A strategic assessment with a baseline of the current decision's error and cost, a back-test against at least one full year the model has not seen, the cleaning rules under version control, alerts on data freshness and model drift, an operations runbook, and a recorded handover.
Environments and hosting
ZingZee provisions a training environment and a production deployment on its own GPU servers in Cyprus, on a dedicated cloud server, or on the client's hardware, and runs both until handover. Training data stays on the same infrastructure, and any cloud contract is held in the client's name.
Priced separately
Additional models after the agreed first one, the database or pipeline work the assessment identifies as a precondition, changes to the client's operational system needed to show the model's output, external data feeds, and any platform the model sits inside are scoped and quoted as their own items.
What the client provides
Read access to the history in its current form, the person who takes the decision today and the method they use, the cost of a wrong decision in each direction, credentials for the systems the model reads and writes, and a person who approves each release.
Outside the engagement
Licences for external data sources, changes to supplier or customer contracts that a new pricing or ordering method requires, and legal advice on the use of personal data in a model are the client's to arrange, with ZingZee supplying the technical inputs each one needs.
Data science tooling
ZingZee builds every data science project on the same set of tools, so a client who reads about one model can expect the same on the next. The tooling covers:
- Python with pandas and scikit-learn for data preparation, feature engineering, and classical models
- Gradient-boosted trees and time-series models as the default, with neural networks only where the back-test justifies them
- Postgres as the store for the history, the features, the forecasts, and the actuals they are scored against
- Scheduled pipelines that refresh the data, retrain on drift, and log every run
- Dashboards built in React and Next.js that show the forecast beside the actual figure and the error per period
- GPU servers in ZingZee's own facility in Cyprus for training, or the client's hardware where the data must stay on site
How a data science project with ZingZee runs
A data science project with ZingZee runs through the five-phase delivery framework. The strategic assessment profiles the history, measures the error and the cost of the current decision, and states whether the data can support a model and at what accuracy. The AI roadmap fixes which decision is modelled first, the accuracy target, the data preparation needed, and where the output will be shown, in order of return. Integration and deployment builds the pipeline, trains and back-tests the model, deploys it beside the current method on live data, and switches the decision over once the model has beaten the baseline for an agreed period. Adoption and enablement trains the people who act on the model's output, with written guidance on what the number means and when to overrule it. Governance, optimisation and scale monitors the forecast against the actual figure, retrains on drift, and adds the next decision. Each phase opens with a scoping workshop and closes with a hardening workshop, where the model is tested against held-out periods and the failure modes, and a delivery workshop, where the client's staff use the release and sign it off.
Strategic assessment
We assess how the business operates today: its processes, its data and the systems it runs on. From that we identify the use cases with the highest return and confirm the organisation is ready to adopt them, so the programme starts from a defined baseline.
AI roadmap
Findings become a phased roadmap that balances early wins with the longer build. ZingZee sets the milestones, the resourcing and the governance that keep delivery on schedule and aligned to business objectives.
Integration and deployment
Our engineers develop, validate and deploy the solution into your production environment, integrated with the enterprise systems you already run and sized for the workloads it will carry.
Adoption and enablement
Enablement programmes prepare business users and technical teams to work with the new capability, and structured change management ensures the organisation captures the full value of what has been deployed.
Governance, optimisation and scale
Ongoing governance, monitoring and optimisation keep the solution accurate, compliant and performing. Proven solutions are then scaled across departments and regions under the same data governance standards.
Data science engineering practices
Every model is back-tested on periods it has not seen before it is proposed, and the back-test report states the error against the current method on the same periods. The simplest model that beats the baseline is chosen, and a more complex one is adopted only when the back-test shows a gain the business would notice. Every forecast is stored with the version that produced it and scored against the actual figure when it arrives, so drift is measured, never assumed. Cleaning rules and feature definitions are code under version control, re-run on every refresh, and reviewed when the source system changes. Each score or forecast carries the features that drove it, an overrule on the screen is logged, and no model output is written to a price, an order, or a payment without a rule or a person between them until the client removes that step in writing. Training data stays on hardware the client controls, and personal data is minimised or excluded from the features.
Cost and time for a data science project
The cost of a data science project depends on the state of the history, the number of systems it must be assembled from, the accuracy target, the frequency of the decision, and where the output must be shown. A forecasting or scoring model over one clean database, shown on an existing screen, is a matter of weeks including the back-test. A model that needs the history assembled from several systems, a new screen, and a period of running beside the current method is a matter of months. Where the assessment finds the data cannot yet support a model, the pipeline and database work is quoted first. ZingZee provides a written estimate after the strategic assessment and phases the budget so the first model's return funds the next.
What happens next?
You send a description of the decision you want to improve, the systems that hold the history behind it, and how the decision is taken today.
An engineer reads it and replies within two working days with the shape of a strategic assessment.
You sign a non-disclosure agreement if you need one, and you receive a proposal with the phases, the estimate, and the team.
Frequently asked questions
Straight answers on Data science work with ZingZee.