Data engineeringMost teams have the model. They don’t have the data to run it in production.
The platform was chosen by someone who’s no longer here. The bill is 2–4x the original estimate. The engineers working with it every day know a simpler stack would do the job — but migration is always someone else’s quarter.
We migrated one team off Databricks entirely — 5 production pipelines, zero downtime. We pick the tool that fits the actual workload, not the one that looks impressive in a procurement meeting.
Relational databases, document stores, object storage, legacy warehouses — all holding different pieces of the same truth. Getting that data into shape for RAG or an agent means solving five integration problems before you write a single LLM prompt.
Data lakes promised to solve this. They became swamps. Raw data poured in, nobody governed it, nobody documented it. Nobody owned the data quality underneath.
The model is ready. The dashboard is designed. Then the team discovers inconsistent schemas, duplicate records, and missing fields upstream. Every AI project has this moment.
The teams that move fastest are the ones who solved the data layer before it became the blocker.
We don’t have a preferred vendor. We pick what fits:
Spark when you need it, Python and Polars when you don’t. No Datadog if you don’t need Datadog. No Databricks when a few gigabytes of data don’t justify the bill.
Ingestion, transformation, loading — built to survive schema changes upstream.
Grouping rules, merging rules, source records → master record → golden record; daily batching with full audit trail.
Stored procedures, partition-based quality checks by country/department, deduplication, validation at ingestion.
Pinecone, Weaviate, pgvector — whichever fits your stack.
Structured data → vector store, relational → RAG-ready, documents → LLM-consumable.
Kafka topics, Protobuf schemas, streaming pipelines with classification logic and fault-tolerant delivery.
Databricks → GCP Dataproc, legacy ETL to modern stack, zero-downtime delivery with 100% IaC (Terraform + GitLab CI/CD).
Regex-based scanning to surface hidden PII (names, emails, phones, currencies) in uncontrolled data regions, compliance exposure mapping.
You know where every record came from and what touched it.
MongoDB + Snowflake merge dashboards, partition-level analytics for complex enterprise environments.
Consumer-to-producer latency, consumer lag, throughput metrics; Grafana + OpenTelemetry; persistent history servers.
4M records Master Data Management platform. Unified distributed enterprise infrastructure across multiple source systems. Grouping + merging rules, full audit trail, daily batching — all on Snowflake Scripting. Golden Record workflow with approval and publishing flow.
4M records MDM
Snowflake Scripting
12-month delivery
Sanctions monitoring latency architecture for payments. New Kafka topic connecting Seeker Indexer and Inverse Seeker, classification logic, Protobuf schema updates. Observability: consumer lag, latency, throughput in Grafana. Sub-minute latency budget achieved.
< 1 min latency
Event-driven Kafka
OpenTelemetry
5 critical pipelines migrated from Databricks to GCP Dataproc. 100% Infrastructure as Code. Zero downtime. 1-week stability run before go-live.
Zero client-side changes
Platform-agnostic
Production-ready
80+ data source integrations across 35 unique systems. Metadata Store migration from Postgres to Snowflake Scripting (more effective storage, better client metrics). Data quality rules via stored procedures with dynamic partitioning. Custom dashboard merging MongoDB and Snowflake data for partition-level analytics.
80+ data sources
35 unique systems
Multi-vendor stackWe bridge data engineering and AI implementation. When the data layer is done, the AI work can start.
Final price confirmed after 1–3 scoping sessions.
We write the spec — no homework on your side.
For teams where data exists but can’t be trusted or acted on fast enough. Reporting is manual, sources don’t agree, and there’s no single picture of the business.
What’s includedFor teams where data infrastructure has become the bottleneck. Pipelines break silently. Compliance requirements are growing. AI initiatives are blocked by data quality upstream.
Everything in Data Foundation, plus:For organisations with real data volume, regulated environments, or complex migration and AI-readiness programmes.
Typical scenarios:Audit data sources, schemas, quality issues, downstream AI dependencies
Define data contracts, transformation rules, quality thresholds, AI output requirements
Pipelines, transformations, validation layers, vector store injection if needed
Data quality checks, reconciliation against source, AI output spot-checks
Production cutover, monitoring dashboards delivered, handover docs
No vendor bias. Spark when justified. Python when sufficient. Snowflake Scripting when the whole product lives in the warehouse. We’ve seen the bill when the wrong tool gets picked — and we won’t let that happen to you.
The engineers on the scoping call are the engineers who build. No bait and switch. No junior bench on a platform migration.
Data projects scope-creep silently. We write the spec with you before build starts. Scope changes go through a change request — they don’t happen quietly.
We know where the data ends up — RAG pipelines, LLM context, agent memory. We build with that destination in mind from day one, not as an afterthought.
Our tech director owns the data architecture. Not a consultant who recommends and leaves. An engineer who commits and stays accountable through handover.
Data contracts, pipeline runbooks, lineage docs — yours at handover. No ongoing dependency on us to keep it running.
Most data projects stall in requirements — not because the engineering is hard, but because nobody defined what “good data” actually means for your specific downstream use case. We define that with you.
Start with a scoping sessionNo SoW from your side. No pitch deck. No follow-up sequence.
30 minutes. We’ll tell you exactly how we’d approach your data challenge — stack, scope, timeline.
No SoW required. No pitch deck. No follow-up sequence.
No SoW required. No pitch deck. No follow-up sequence.