AI and Data Services
AI that works from your own numbers, on our hardware
ZingZee's AI and data work runs on private models, reads the client's own documents and databases, and connects to the systems the business already pays for. Every answer carries its source, every figure drills down to its row, and no client data is sent to a public model API.
What AI and data services are
ZingZee's AI and data services cover four kinds of work: private language models that run on ZingZee's own hardware or the client's servers, retrieval and search that lets a model answer from the client's own documents and records, analytics and BI that show live business figures with a drill-down to the source, and integrations that connect the systems a company already pays for. A language model is a program that reads and writes text; on its own it knows nothing about a business. The value comes from connecting it to the right documents and the right database, keeping the data private, and tying every feature to a measured outcome such as hours saved or errors removed.
AI and data technologies.
Answers from your own numbers, on our own hardware.
When AI and data work pays off
AI and data work pays off where staff read documents to extract a few facts, where questions about the business are answered by building a spreadsheet, or where two systems hold the same record and disagree. A broker reading policy schedules, an accounts team keying invoices, a manager waiting for the monthly stock report, and a sales team phoning the warehouse to check availability are the shapes ZingZee sees most. In each case the first step is to make the data reachable and correct, and the second is to put a model or a dashboard on top of it. ZingZee's assessment measures the current cost of the task before proposing any model, so the outcome can be checked after go-live.
When AI is the wrong answer
AI is the wrong answer where the data is wrong, where the rule is already known and belongs in ordinary code, and where a mistake cannot be checked by a person before it acts. A pricing rule, a VAT calculation, and a stock allocation are written as deterministic code with tests, and a model is used only for the reading and drafting steps around them. ZingZee's assessment says so in writing where a client's request would be better served by a database view, a scheduled job, or a form. A model that guesses is never placed in front of a payment, a legal document, or a customer without a person or a rule between them.
AI that works from your own numbers, on our hardware
Industries served
ZingZee's AI and data services run the policy-reading tool of an insurance broker, where a document is checked in minutes; the invoice inbox and live profit and loss of an accounting system for a business with hundreds of properties; the warehouse dashboard and analysis tools over an SAP mirror for a retailer; the owner dashboard of a distributor's ordering platform; and the transcription and grading pipeline of an aviation assessment product. The same practice serves travel, energy, and automotive clients. Each is described by shape on the industry pages.
AI and data services ZingZee provides
Private language model deployment
ZingZee runs open-weight language models on its own hardware in Cyprus or on the client's servers, with key-only access and no training on the client's data. Prompts and answers stay inside the client's estate.
Retrieval and search over business documents
Contracts, policies, invoices, manuals, and emails are indexed so a model answers from the client's own text and cites the page it used. Extraction of fields such as dates, amounts, and names is deterministic where the rule is known.
Analytics and BI on live data
Dashboards read the operational database, refresh within seconds, and let a manager drill from a total to the record behind it. Every figure agrees with the system it came from, because it is the same figure.
Integration of the systems a client already runs
ERP, accounting, payment, channel, and messaging systems are connected through typed services with logging and retry. The result is one record of truth for stock, orders, bookings, and cash.
Forecasting and decision support
Demand, occupancy, and reorder forecasts are built from the client's history, tested against past periods before use, and shown beside the actuals so staff can judge them.
AI and data scope
- Document reading and extraction
- Invoices, policies, contracts, and forms read into structured records with a source reference
- Assistants over internal knowledge
- Answers from manuals, procedures, and past cases, with the page cited
- Live dashboards and reports
- Sales, stock, margin, occupancy, and cash, with drill-down and role-based access
- Forecasting
- Demand and reorder models tested against history
- System integration
- ERP, accounting, payments, channels, and messaging connected with audit logs
- Private hosting and data governance
- Models and data on hardware the client controls, with access scoped by role
Engagement model
AI and data work follows ZingZee's five-phase delivery framework, and this is the section of the practice the framework was named for. The strategic assessment measures the tasks, the documents, and the data sources, and states the current cost of each task. The AI roadmap fixes which tasks get a model, which get a dashboard, and which get a plain integration, in order of payback. Integration and deployment connects the systems, indexes the documents, and puts the first feature into daily use. Adoption and enablement trains staff to use and check it. Governance, optimisation and scale reviews accuracy, cost, and access after go-live. Each phase opens with a scoping workshop and closes with a hardening workshop, where outputs are checked against known answers, and a delivery workshop where the client signs off.
- Strategic assessment
- AI roadmap
- Integration and deployment
- Adoption and enablement
- Governance, optimisation and scale
AI and data tooling
ZingZee's AI and data practice uses one set of tools, chosen so that every output can be traced to its source. The tooling covers:
- Open-weight language models served on ZingZee's own GPU hardware or the client's servers
- Python services for extraction, indexing, and orchestration
- Postgres with vector search for document retrieval
- Typed integration services with logging, retry, and audit trails
- Dashboards built in React and Next.js over the operational database
- Evaluation sets of known answers run before every model or prompt change
AI and data engineering practices
Every model output carries its source, so a person can check it. Every figure on a dashboard is the figure in the operating system, because the dashboard reads the same table. Extraction of a field with a known rule is written as code, and a model is used only where reading or drafting is required. Prompts, models, and evaluation sets are versioned, and a change ships only when it scores at least as well as the last on the known-answer set. Data stays on hardware the client controls, access is scoped by role, and no client data is used to train a model. At handover the client receives the services, the indexes, the evaluation sets, the dashboards, and a written runbook.
Cost and time
The cost of AI and data work depends on the number and condition of the data sources, the volume and variety of the documents, the accuracy the task demands, the systems to connect, and the hosting choice. A dashboard over one clean database is a matter of weeks. A document-reading pipeline with a private model, evaluation sets, and a review screen is a matter of months and goes live one document type at a time. Integrations add a fixed effort per system, driven by the quality of its API. Private hosting carries a hardware or server cost that ZingZee sets out beside the per-request cost of a public model, so the client can compare. A written estimate follows the strategic assessment.
What happens next?
You send the tasks that take your staff time, the documents and systems involved, and the outcome you want to measure.
An engineer reads it and replies within two working days with the shape of a strategic assessment and a first view on where a model, a dashboard, or an integration fits.
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 AI and data work with ZingZee.