Agentic Data Development

Proměňte AI v měřitelnou hodnotu s ROI. Začněte AI strategií.

Získejte jasný plán, jak využít AI napříč celou organizací – od kratkodobých cílů po dlouhodobé investice, které se vám skutečně vrací.

Důvěřují nám přední firmy z globálního trhu
Pain points

Why data development doesn't scale

Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Sed vel lectus. Donec odio tempus molestie, porttitor ut, iaculis quis, sem.

Developers are the bottleneck
BI analysts know what they need, but every request waits in a dev queue. Engineers spend most of their time translating requirements into SQL — work that's structured, repetitive, and ripe for automation.
Context gets lost in handoffs
Requirement → ticket → spec → code. By the time it's built, key nuances are lost. The final output often doesn't match original intent, triggering rework cycles.
Copilots help, but don't own the task
Tools like Claude Code accelerate individual developers, but they still require constant human steering. The developer remains responsible for the full workflow.
Platform knowledge is tribal
Naming conventions, transformation patterns, platform quirks — rarely documented. Long ramp-up for every new developer, risk with every departure.
How it works

How agentic data development works

From business requirement to deployable pull request — with the developer entering the flow only at the review stage.

Business requirement

A BI analyst or business user submits a detailed, structured specification — what data is needed, from which sources, with what logic and granularity.

Context assembly

The orchestrator pulls in your platform documentation, data catalog, naming conventions, existing models, and architectural patterns — assembling full context for code generation.

Autonomous code generation

The agentic platform generates the code: SQL scripts, dbt models, pipeline definitions, tests, and documentation. This step runs entirely autonomously — no developer in the loop.

Pull request + developer review

Generated code is packaged as a PR. A data engineer reviews it alongside the original spec, gives feedback or makes adjustments. The agent incorporates feedback automatically.

Merge & deploy

Approved code merges and automatically propagates into the target environment — CI/CD, orchestration, monitoring included.

Náš přístup

What changes for your team

Enterprise AI projekty uspějí tehdy, když strategie, governance a exekuce fungují společně. Náš framework propojuje priority vedení s reálným byznysovým dopadem – zajišťuje přehlednost, compliance a škálovatelnost od prvního dne.

80%+ runs without a developer

From requirement to PR, the process is autonomous. Developers focus on review and architecture.

Business gets results faster

A well-defined requirement can produce a reviewable PR within hours, not sprints.

Knowledge is encoded, not tribal

The system ingests your documentation, conventions, and patterns. Standards are applied automatically.

Full traceability: requirement → code

Every generated artifact is linked to its source requirement.

Developer time shifts to high-value work

Less time on routine SQL, more on architecture, optimization, and complex problems.

Works with your existing stack

Databricks, Snowflake, dbt, Airflow, Azure, GCP — the platform adapts to your environment.

Traditional vs. agentic development

BigHub-founders-story-Karel-Simanek-Tomas-Hubinek
Differentiators

What sets this apart

Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Sed vel lectus. Donec odio tempus molestie, porttitor ut, iaculis quis, sem.

Autonomous, not assisted
Not a copilot. The agent system runs the full migration cycle — discovery, translation, validation — with minimal human intervention.
Built-in validation loop
When the validation agent detects inconsistencies, it autonomously adjusts the migrated solution and re-validates until the target matches the source.
Full lineage & traceability
Every migrated object is linked to its source equivalent. Complete audit trail — critical for compliance and governance.
Proven in enterprise
BigHub designs and deploys the agent system tailored to your specific source-target combination, data volumes, and operational constraints.
Jak pracujeme

Implementace

Náš přístup zajišťuje plynulou a měřitelnou cestu od nápadu k realizaci. Každá fáze je strukturována tak, aby ověřila dopad, minimalizovala rizika a urychlila doručení hodnoty.

Plán ověření konceptu

Nejprve identifikujeme jasný obchodní use case a definujeme metriky úspěchu. Společně ověříme technickou proveditelnost a měřitelnou návratnost investic prostřednictvím malého prototypu přizpůsobeného vašim datům a systémům.

Pilot

Jakmile se koncept osvědčí, rozšíříme řešení do řízeného produkčního prostředí. Zaměřujeme se na výkon, uživatelskou adopci a integraci se stávajícími pracovními postupy — abychom zajistili, že přinese skutečné obchodní výsledky.

Plná produkce

Po úspěšné validaci nasazujeme plnou produkční verzi. Náš tým zajišťuje škálovatelnost, monitoring a průběžnou optimalizaci, aby vaše AI řešení i nadále přinášelo měřitelné výsledky dlouhodobě.

Case studies

Real strategies. Real results.

See how organisations turn AI strategy into measurable outcomes. Real projects, real impact, delivered in real enterprise environments.

Ready to move off your legacy platform?

Want to discuss the details with us? Fill out the short form below. We’ll get in touch shortly to schedule your free, no-obligation consultation.

Ověřeno 100 + firmami
Thank you! Your submission has been received.
Oops! Something went wrong.