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Rosera Rosera Technologies, home

Data and AI consultancy

Modern data platforms and production AI

Rosera Technologies migrates enterprise warehouses onto Snowflake, Databricks and Microsoft Fabric, then engineers the pipelines, governance and AI systems that run on the new platform. We are a US consultancy with delivery teams in the United States and India.

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Technologies we work with daily

  • Snowflake
  • dbt
  • Fivetran
  • AWS
  • Azure
  • Databricks
  • Power BI
  • Tableau
What we do

Six practices, one delivery team

Each is scoped and priced on its own, because few organizations need all six at once. The engineers move between them, which is why an access rule agreed during a migration is already in place when the reporting is built on top of it.

Artificial intelligence

Past the demo, into production

We build retrieval systems, agents and models with an evaluation harness in place from the first week. Every release is scored against a fixed set of questions with known answers, so a change that helps one case and breaks others is caught before it ships.

Once it is running we track cost, latency and drift in answer quality. The rollback procedure is tested before go-live, so your team can switch the system off without waiting for us.

AI and machine learning
A retrieval pipeline with an evaluation harnessData flows from your documents and tables through retrieval and an agent to a cited answer. An evaluation harness sits beneath the pipeline and scores every release for cost, latency and drift.Your datadocuments and tablesRetrievalindexed and rankedAgentgrounded promptsAnswerwith citationsEvaluation harnessquality measured on every releaseCostLatencyDrift
The evaluation harness sits under the pipeline and scores every release before it ships.
Method

Verification decides the date

Most migration schedules slip during verification. Converting schemas and rewriting jobs is well-understood work, and tooling covers a good part of it. The time goes on proving that the new platform returns the same numbers as the old one, report by report.

Both platforms run the same workloads over the same period, and we compare results at row, aggregate and column level. Every difference is traced to a cause. Some of them turn out to be defects in the legacy platform that nobody had noticed, and we agree the treatment with your team before closing them.

Parallel run and reconciliation during a migrationTwo horizontal tracks run the same period of data. The upper track shows the legacy warehouse running its existing workloads to produce reference output. The lower track shows Snowflake running the converted models to produce candidate output. Both outputs feed a reconciliation step, which compares them and returns any documented difference to the converted models for correction. Cutover follows once differences are accounted for.EXISTING PLATFORMTARGET PLATFORMLegacy warehouseExisting workloadsReference outputSnowflakeConverted modelsCandidate outputReconcilerow, aggregate, columndocumented difference
The dashed path is the reconciliation loop, which runs until every difference is resolved or accepted in writing.
How we work

We build it to hand it back

01

Parallel run before cutover

Whatever we build runs beside the system it replaces, on the same inputs and the same reporting periods, until the two agree. Differences are normal early on. We trace each one to a cause, record it and get it signed off before the old platform is turned off.

02

Built to be handed over

Code, tests and infrastructure definitions sit in your repository, under your version control, from the first week. Nothing is kept on our laptops. We write the runbooks during the build and walk your engineers through them before handover.

03

Honest about what is unresolved

We write down what didn't go to plan. In the closing document we set out the constraints we ran into and the questions still open at handover, including any difference between the old platform and the new one that we could not fully explain. Your team receives it in the same pack as the runbooks.

Industries

Same engineering, different rules

Regulation, retention rules and what counts as a correct answer change from one sector to the next, and those answers decide more of the design than the platform choice does. Partitioning is set against the retention periods, and lineage depth against what an auditor will ask for.

  • Healthcare and Life Sciences

    Clinical, claims and operational records have to be joined together, and every access rule attached to any one of them still applies to the joined result.

  • Financial Services

    Regulators and auditors ask for a published figure to be reproduced months later, down to the individual transactions behind it.

  • Insurance

    Policy, claims and actuarial data sits across separate administration systems, some of which arrived through acquisition and were never consolidated.

  • Retail and Consumer

    Transaction volumes are high enough that modeling decisions show up on the monthly bill, and the business wants yesterday's numbers before the stores open.

  • Manufacturing and Supply Chain

    Sensor readings and ERP records describe the same plant at grains that do not match.

  • Software and Technology

    Product telemetry, billing and customer records feed the same platform, and some of it usually has to be exposed back to your own customers through the product itself.

All industries

Send us the part you are stuck on

Most engagements start with a short assessment. You get a written plan setting out the sequence of work and what it costs, and that plan is yours whatever you decide next. If you run the build with your own team, we will scope the assessment on that basis.

Contact uspv@roseratech.com