Most AI programmes are limited by the data beneath them. We build the platforms, pipelines and controls that make data trustworthy: lakehouses and warehouses, streaming ingestion, quality and lineage, retrieval infrastructure for agents, and the observability to know when any of it drifts.
Data engineeringEach capability is delivered as infrastructure-as-code with tests, documentation and dashboards, and deployed in your own cloud tenancy or data centre. Nothing depends on a Silverline-hosted service unless you choose it.
Design and implementation of governed analytical platforms on open table formats or managed warehouses: medallion-style layering, domain-oriented modelling, access control by classification and cost management from the first day.
Event ingestion and processing for transactions, telemetry and operational events, with exactly-once semantics where the business requires it, schema evolution handled explicitly and replay from retained history.
Declarative quality rules on every critical dataset, column-level lineage from source to report, and ownership recorded in a catalogue. When a regulatory return or an agent's answer is questioned, the path back to source is already documented.
Document ingestion, chunking, embedding and indexing for agents that must answer from your own material. We treat retrieval as a governed data product with access control, freshness guarantees and evaluation of retrieval quality, not as a side effect of a chatbot.
Pipeline health, data freshness, volume anomalies, schema drift and cost, on dashboards your operations team owns, with alerts routed to named owners and runbooks for the common failures.
Classification-driven access control, encryption at rest and in transit, tokenisation of personal data where analytics do not need identities, and deployment inside India where residency or the DPDP Act requires it.
Our platforms are organised around a single flow. Each stage has a defined owner, tests and monitoring, so that a new data source or a new consumer is added by following the pattern rather than by inventing one.
Batch extracts, change-data-capture and event streams land in a raw zone with schema registered, source and load time recorded, and nothing altered.
Data is cleaned, conformed and modelled into domain entities and metrics, with quality rules applied and transformations under version control.
Curated datasets, feature tables and retrieval indexes are published as data products for analysts, applications and agents, with contracts and access policies.
Ownership, classification, lineage and retention are recorded in the catalogue. Access requests, approvals and policy exceptions leave an audit trail.
Freshness, volume, quality and cost are monitored continuously. Alerts go to owners; incidents feed back into rules and runbooks.
When a client engages us for an agentic pipeline or an AI transformation programme, the discovery sprint almost always identifies data gaps that must be closed before a model can be trusted: missing history, inconsistent identifiers, undocumented transformations or data that exists only in a legacy system nobody wishes to touch.
Our data engineers join those engagements from the architecture stage. They build the retrieval index the agent reads from, the feature tables the decision engine scores on and the audit store the pipeline writes to. Because the same team owns the evaluation suite's data, the numbers in the eval report and the numbers in the platform reconcile.
The practice also works independently, for organisations that need a governed analytical platform or a modern reporting foundation whether or not AI follows.
Hand-over. Every platform is delivered with infrastructure-as-code, a runbook, on-call procedures and training for the team that will operate it. Managed operation is available under a separate service agreement.
A two-week data readiness review tells you which use-cases are feasible now, which need remediation and what that remediation costs.