Generative AI & Data Analytics
Turn your data into working products
Most AI projects stall between demo and production. Cloudvora starts from a specific use case with a measurable outcome, then builds it on managed AWS services so you are not maintaining bespoke infrastructure to run a model.
That typically means Amazon Bedrock for foundation models, a retrieval layer over your own documents and data, and the surrounding controls — evaluation, guardrails, cost management and monitoring — that make the result safe to place in front of users.
Use cases first, models second
We scope AI work the same way we scope infrastructure: what decision or workflow improves, how we will measure it, and what it costs to run per request. Model selection follows from that, not the other way around.
What's included
- Use-case discovery, feasibility and success metrics
- Amazon Bedrock integration with foundation models
- Retrieval-augmented generation over your own data
- SageMaker training and inference pipelines
- Data platform work: warehousing, ETL and BI dashboards
- Evaluation harness, guardrails and cost monitoring
What you can expect
A production-ready capability, not a stalled prototype
Predictable per-request cost and latency you can budget for
Guardrails and evaluation so quality is measured, not assumed
How We DeliverOur GenAI & Analytics Process
Use-case discovery
We identify where AI creates measurable value, and agree how success will be evaluated before building.
Proof of concept
We build a working prototype on Bedrock or SageMaker against your real data to validate quality and cost.
Productionize
The prototype gains what production requires: retrieval, guardrails, monitoring, security and scale.
Measure & iterate
We track quality, adoption and cost per request, then tune the system against those numbers.
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Discuss your genai & analytics objectives with our AWS certified team and agree a clear first step.