Teceze designs and builds the data pipelines, embedded analytics and AI embedded features that turn raw, scattered data into decisions made inside the applications your teams already use. From ingestion through to a prediction rendered on screen, every layer is engineered, secured and owned by one team, so insight stops living in a report nobody reads.
Book a Data and AI Assessment Most organizations already collect enough data. What is usually missing is the engineering that connects it to a screen a user actually looks at. These are the layers Teceze builds and owns for you, end to end.
We ingest, cleanse and unify data from your applications, databases and third party systems into a single reliable pipeline, built on modern ELT and ETL patterns rather than manual exports and spreadsheets.
Relational, NoSQL, vector and cache layers are combined into a polyglot persistence model, so each workload runs on the storage engine best suited to it instead of one database doing everything.
Interactive dashboards, reports and visualizations are built directly into your product interface, so users see insight in context instead of switching to a separate business intelligence tool.
Predictive models, recommendation engines and generative AI features are embedded directly into application workflows, surfacing an answer or a next best action rather than a static, backward looking report.
Streaming pipelines and event driven architecture keep dashboards and models current to the minute, with automated alerts raised the moment a metric moves outside its expected range.
Role based, least privilege access, data lineage and audit logging are built in from day one, with support for frameworks such as GDPR and sector specific data residency requirements.
These are the ranges Teceze clients typically see within the first 12 months of a data and AI embedded application build. We baseline your current reporting and data estate during the assessment, then commit to targets in the contract.
Achieved through right sized, dynamic data platforms rather than fixed, over provisioned capacity.
Release velocity roughly doubles once pipelines move from manual, quarterly cycles to automated, continuous delivery.
Delivered by replacing single point of failure data stores with isolated, self healing services.
Automation and embedded dashboards remove spreadsheet based reporting and the technical debt behind it.
Delivery runs across four phases with no disruption to existing reporting: discovery and assessment, target design and pilot, core build and migration, then hyper scale and handover.
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A traditional business intelligence project delivers a dashboard in a separate tool that users have to remember to open. Data, Analytics and AI Embedded Applications puts that same insight, and the AI features built on top of it, directly inside the product your teams already use every day. Practically, that means a maintained pipeline, a governed data model and an interface component, delivered together as one working feature rather than a report handed off at the end of a project.
No. We build around your existing estate, whether that is SQL Server, PostgreSQL, Snowflake, Power BI, Tableau or a similar platform, so your data, workflows and reporting history stay where they are. Where a genuine gap exists, such as a missing real time layer or a vector store for AI features, we will recommend introducing one during the assessment, not as a precondition for starting.
A focused pilot, such as one embedded dashboard or a single predictive feature, is typically delivered in a small number of weeks. A full data platform with several embedded AI features spans a number of months, released in stages rather than as one large launch. Every engagement starts with a scoped pilot so you can validate the approach on real data before committing to the wider roadmap.
We select the model that fits the use case, drawing on established providers alongside our own trained models where a specific or regulated task calls for it. The choice is documented and agreed with you before build starts. Your data is processed within agreed boundaries to power your application and is not used to train third party foundation models unless you explicitly agree to it in writing.
Access is role based and least privilege, with every action logged and auditable, and full data lineage tracked from source to dashboard. We align to relevant regulatory frameworks, including GDPR, and to sector specific data residency requirements. AI components operate on your data within agreed boundaries; we document exactly what is processed, where it is stored and what is excluded before anything goes live.
Pipeline and dashboard builds are typically priced as a fixed scope project. AI embedded features that need ongoing retraining, monitoring and tuning are usually run as a managed service, priced monthly against agreed service levels. We model both options against your actual data volumes and use cases during the assessment, so you see the total cost of each path rather than comparing headline rates alone.
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Schedule a 45 minute assessment with our data and AI specialists. You will leave with a clear view of where your data currently lives, what it costs to report on today, and where embedded AI can remove that work.