Data foundations: pipelines that finish, data you can trust, a platform you own.
Build trusted data platforms that power analytics and AI. Data engineering and pipelines, data integration, quality and governance, and data platforms and modernisation, delivered by a pod of three from Pune with a fixed-fee first week and lineage your auditors can follow.
Three sub-services, one discipline underneath everything else
This is the platform discipline the analytics and AI work runs on, not analytics preparation. Each sub-service has its own page with the method and the proof it demands before a cutover is called done.
Data engineering and pipelines
Backend jobs tuned for runtime and cost together, migrations proved field by field with reconciliation, storage laid out for how the data is actually read, and lineage Finance can follow.
Data engineering services →Quality and governanceData integration, quality and governance
Schema contracts at system boundaries, checks that fail a run instead of warning, a named owner per dataset, and reconciliation between the systems that each hold part of the truth.
Data integration and governance →PlatformsData platforms and modernisation
Whether to modernise at all, and we say so when the answer is no. When it is yes: open table formats in your own cloud account, a reconciled parallel run, and a decommissioning date agreed in week one.
Data platform modernisation →ProductAntvia, the lakehouse and BI tool as one product
A bronze, silver and gold lakehouse plus a next-generation BI tool, built by Woodfrog, for teams that want the platform without assembling it.
See Antvia →On topData agents, once the layer beneath them is governed
An agent is only as good as the layer beneath it, so the governed foundation comes first and the model goes on top.
Data agents →The symptoms that start most of these conversations
Each one belongs to a layer, and the audit’s first job is to say which.
Two numbers for one measure, three records for one customer
And no name on either dataset. That is an ownership and contract problem before it is a modelling one.
The old platform is still running, the new one is half built, and reporting is frozen
The second attempt at a modernisation usually starts here. The fix is a reconciled parallel run and a decommissioning date, not a longer freeze.
Jobs that do not finish, and a warehouse bill that doubled
Warehouse spend almost never grows because of storage. A schedule, a cache or a refresh strategy changed quietly and nobody owns it.
A supplier changed a column and the pipeline found out in production
Data contracts assume a producer you can hold to account. When the producer is a vendor, the design needs something else.
What we have actually delivered
No case study is filed under this pillar yet, so these are the vertical-wide figures, plus the one place a customer story shows the foundation work inside a build.
Read the record
StockJarvis: the data layer under a Claude intelligence layer
Phase one was the foundation: secure connectors, normalised backtest outputs and guardrails, before any agent was built on top.
Read the StockJarvis story →Case studiesFour cases, four disciplines, no result used twice
The anonymised cases, each with a number the customer’s own team checked.
See the case studies →How an engagement runs
Fixed scope, fixed price, and a pod of three who stay on it.
- 01
Week 1, fixed-fee audit
Two calls and a written one-pager. Yours to keep whether or not you continue.
- 02
Weeks 2 to 6, build
Something real in front of real users by week three. Median six weeks to the first production-grade artefact.
- 03
Week 7 onward, operate
Quarterly reviews and on-call governance. The people who built it pick up the phone.
What buyers ask about data foundations
Do you build data pipelines and ETL?
Yes. Pipelines and backend jobs tuned for runtime and cost together, with the backfill treated as the real test, because daily runs prove almost nothing.
Data warehouse or lakehouse?
We decide whether you need a new platform at all before choosing one, and we have written down the test to run first. When the answer is yes, the platform uses open table formats in your own cloud account, so you can leave it later.
What does data quality and governance look like in practice?
Checks that fail a run instead of warning, a named owner per dataset, schema contracts at system boundaries, and lineage that Finance and auditors can follow.
Can the data stay in India?
The platform is built in your own cloud account, so residency is decided by where you run it. We have written about what data that cannot leave the country changes in the architecture.
Where are you based?
Pune, India, delivering across India, APAC and the US.
Find out which layer the problem is in
Two calls, a fixed fee, and a one-pager you keep either way: which symptom belongs to which layer, whether a platform move is warranted at all, and what we would build first.