Model layers commoditise. Data layers accrue. We assess legacy data estates, design AI-native architecture, build production pipelines, and operate in regulated environments, whether cloud-native, on-premise, or air-gapped. The teams pulling ahead are the ones with embedding pipelines that produce reproducible RAG, feature stores that eliminate training-serving skew, and vector storage purpose-built for AI workloads. That infrastructure is engineered, not inherited.
Data engineering for AI works in both directions. We build the infrastructure AI workloads require, and we deploy AI to modernise the data engineering function itself.
Infrastructure → AI
The pipeline layer that makes AI reliable, reproducible, and compliant in production.
We design, build, and operate the data infrastructure AI workloads actually require - feature stores, embedding pipelines, vector stores, real-time serving, lineage, and governance. Built for the specific cadences, failure modes, and audit requirements that distinguish AI systems from analytics workloads.
AI → Infrastructure
The data engineering function that improves continuously rather than scaling linearly with headcount.
We deploy AI to modernise the data estate itself, translating legacy pipelines and unmaintained code, automating quality detection and root cause traversal, and instrumenting the stack with self-monitoring capabilities that were previously too expensive to operate at scale.
Every AI team we work with reaches the same conclusion, usually after their second production deployment. The model was never the hard part. The data was. The assessment reveals the same gaps at comparable points of the journey, along with the engineering path to close them.
Frontier model capabilities improve every six months as access costs fall. None of that translates to durable advantage unless the data layer captures it. We focus the engagement on the assets that endure - the proprietary embedding pipelines, curated training corpora, and entity-resolved feature stores that accrete value as the model layer commoditises. The model is rented. The data stack is owned.
Embeddings at scale. Multimodal data in petabytes. Feature serving in single-digit milliseconds. End-to-end lineage from prediction back to source. Legacy warehouses were built for rows of transactions, not vectors of meaning. We assess the gap between the analytics warehouse in place and the AI-native storage primitives the workload needs, before the team discovers it mid-project.
A retailer ships on hyperscaler cloud. A defence contractor ships on air-gapped infrastructure. A regulated bank ships on sovereign on-premise. A manufacturer ships at the edge. The same data lifecycle looks different in each environment. We have built in all four contexts. Architecture conversations that ignore deployment environment produce designs the business cannot ship.
The lifecycle is the same in every engagement - ingestion, transformation, storage, features, serving, deployment. The implementation changes with the workload, the environment, and the regulatory context. We deliver across all six layers.
We assess your current ingestion estate, design multimodal pipelines for the data types AI workloads actually require, and build streaming and batch infrastructure that keeps features current.
Multi-Modal Ingestion: Foundation models consume every modality. We rebuild ingestion stacks to treat PDFs, images, video, audio, sensor telemetry, and structured tables as first-class data types in a unified namespace: object stores, streaming buses, and metadata catalogs wired together so the AI layer sees one coherent estate. The most valuable AI capability was unlocked by treating those modalities as first-class for the first time.
Streaming & Change Data Capture: Online inference needs features that reflect what just happened, not what was true at 2am. We instrument Kafka, Pub/Sub, or Kinesis for streaming aggregations and wire Change Data Capture (Debezium, Fivetran HVR, Estuary) so every source change replicates without dual-write hacks. Schema-registry contracts, Avro evolution rules, and automated incompatibility alerts ensure the AI stack discovers source changes before production does.
Batch at AI Scale: Training corpus assembly, fine-tuning data curation, and reproducible model rebuilds are batch workloads of a different character than the nightly warehouse refresh. We orchestrate pipelines that resume cleanly on failure, deduplicate at petabyte scale, prove provenance from output back to source row, and integrate with experiment tracking so every training run is anchored to a versioned dataset. The boring reliability work such as handling late-arriving data, retrying on rate-limited APIs, detecting structural drift, determines whether the legal defence of a model holds up.
We redesign transformation layers to ELT patterns, build embedding pipelines with the same engineering discipline as any other pipeline, and implement domain feature engineering that enriches every model that follows.
ELT, Not ETL: We have helped teams cut transformation latency by an order of magnitude by inverting the order - load raw data into the lakehouse first, transform with lakehouse compute (dbt, Snowflake, Spark, Delta Live Tables) where storage and compute are decoupled. Ingestion velocity stops being capped by transformation complexity, reprocessing becomes affordable, and the same compute that runs analytics powers AI workloads.
Embedding Pipelines as First-Class Transforms: Chunking strategy, tokenisation, embedding model versioning, deduplication, metadata enrichment. We treat these with the same engineering discipline as any other pipeline: tested, versioned, reproducible. When the embedding model upgrades, the entire corpus regenerates predictably, not as a science project.
Domain Feature Engineering That Unlocks Insights and Actions: The most wasteful pattern in enterprise ML is five teams computing the same customer features five different ways. We design entity-centric transformation layers (customer-360, product-360, account-360) where every new feature enriches every model touching that entity. The twentieth use case takes twenty percent of the effort of the first.
Semantic Layer for AI Consumption: Cube, dbt semantic layer, LookML - single metric definitions consumed identically by LLM agents, BI dashboards, and downstream models. We implement the semantic layer that prevents AI from amplifying metric inconsistency at conversational speed.
We architect the storage layer the workloads above it actually require. Open lakehouses, vector stores, object storage, and knowledge graphs required by AI, in the right combination for the scale, query pattern, and compliance context.
Lakehouse + Open Table Formats: Delta Lake, Apache Iceberg, Apache Hudi. ACID transactions, time-travel, and schema evolution at the storage layer rather than retrofitted on top of files. These are the foundation that makes governance tractable, reprocessing affordable, and reproducibility possible. We have seen storage-layer redesigns unlock more AI capability than any algorithm change.
Vector Stores for Embeddings: The new storage primitives are Pinecone, Weaviate, Qdrant, Milvus, pgvector, Lance. They are purpose-built for semantic retrieval. We select based on scale, hybrid search requirements (vector plus keyword plus structured filters), and deployment context. The right choice for a 10M-document SaaS RAG and a 50B-document regulated archive is not the same product.
Object Storage for Multimodal: Petabytes of unstructured content with metadata-rich catalogs, lifecycle policies that keep cold training data cheap, and format choices (Parquet, Lance, Iceberg) the analytical layer can read without copies. Object storage is the substrate of the AI stack, not the place files go to be forgotten.
Knowledge Graphs as AI Context: Neo4j, RDF stores, GraphRAG patterns. Structured relationships that ground LLM responses in domain truth where pure vector search hallucinates with confidence. The fastest path from a generic chatbot to a domain expert that knows the organisation's products, customers, contracts, and supply chain is a knowledge graph behind retrieval, and it's increasingly table-stakes for agentic systems that need to reason across entities.
We design and implement feature stores that eliminate training/serving skew, enable sub-10ms feature serving for real-time inference, and make feature reuse the default, so each new model starts from a shared foundation instead of rebuilding from scratch.
Training/Serving Parity: The same feature, computed identically online and offline. We implement the architectural discipline that eliminates the most common production-ML failure mode — the model that works in development and quietly underperforms in production because features were computed slightly differently in serving than in training. The skew is silent, the impact is significant, and a feature store is the architectural answer.
Online and Offline Serving: Sub-10ms feature lookups for real-time inference; batch reads for training and large-scale analysis. Tecton, Feast, Hopsworks, Databricks Feature Store, Vertex AI Feature Store are choices driven by latency budget, scale, and whether streaming features are required. The serving discipline ensures the feature store does not become the bottleneck between a fast model and a slow customer experience.
Streaming Features: Windowed aggregations computed on streams: rolling spend, recent session activity, current basket composition, last-N-events. Table-stakes for fraud, real-time personalisation, dynamic pricing, and any workload where a feature computed an hour ago is too stale. The hardest engineering on the data platform, and the most differentiating when it is built well.
Feature Reuse as Organisational Discipline: Entity-centric features computed once, used by every model that touches that entity. We have seen feature-store adoption shorten model-build time from quarters to weeks, not because models got easier but because the data work stopped being repeated.
We build the serving layer that turns warehouse capability into operational reality: real-time inference APIs, batch scoring with reverse ETL into operational systems, and vector search endpoints powering RAG and agentic workflows.
Real-Time Inference APIs: Optimised serving runtimes (Triton, BentoML, vLLM, TorchServe), feature-store-backed input assembly, response caching for repeated queries, and graceful degradation when the model is unavailable. We have deployed real-time inference systems handling tens of thousands of predictions per second. The most consequential engineering is rarely the throughput pipeline; it is the fallback: when the model times out, when the feature store is degraded, when the GPU pool is saturated.
Batch Scoring & Reverse ETL: The churn score exists in the warehouse, but the account manager does not see it in Salesforce. The demand forecast exists in the ML platform, but the supply planner does not see it in their planning tool. We close that gap — predictions flow back into Salesforce, SAP, marketing platforms, and ERPs via Hightouch, Census, Polytomic, or native platform shares as native fields teams consume in existing workflows. Distribution is the multiplier on ML adoption.
Vector Search & RAG Endpoints: Production-grade RAG is hybrid retrieval (vector plus BM25 plus filters), reranked by a cross-encoder, with retrieved chunks rewritten for the generation step and citations preserved for auditability. We have built these endpoints for workloads where every retrieved chunk must be traceable to source.
We architect for the environment the business actually operates in (cloud-native, on-premise, air-gapped sovereign, or hybrid edge) without assuming the workload can be moved to a preferred vendor's cloud.
Cloud-Native: Databricks, Snowflake, BigQuery, Synapse, Vertex AI, SageMaker. Hyperscaler-managed services deliver the fastest time-to-value where data residency and sovereignty constraints do not bind. The vast majority of greenfield AI workloads start here, and should, unless there is a specific reason not to.
On-Premise: Cloudera Data Platform, Red Hat OpenShift AI, NVIDIA AI Enterprise, Kubernetes-native data platforms. Required when sensitive intellectual property, regulated data, or latency-bound workloads cannot reasonably go to public cloud. Every layer is the team's to operate, and the engineering payoff is full control of the stack.
Air-Gapped & Sovereign: Defence, classified government, sensitive financial workloads. The full AI stack (model serving, vector stores, feature stores, lineage tooling) operating without internet egress, often without inbound updates for extended windows. Open-weight models, locally-hosted embedding generation, and air-gap-friendly observability are the building blocks. We have built in this context; the constraint set is uncompromising.
Hybrid and Edge: Edge inference brings low latency intelligence to the manufacturing floor, retail store, telecom base station, autonomous vehicle, point-of-sale. Leveraging features computed centrally and shipped to the edge, retraining triggered when edge drift is detected. The architecture is harder than either pure-cloud or pure-on-prem; the deployment surface area is the reason it is increasingly common.
Traditional DataOps optimises for query freshness. AI DataOps optimises for something harder: reproducible training runs, feature consistency at inference, drift detection before model degradation, and data architecture that AI agents can navigate without hallucinating structure. The discipline carries the same name, but the failure modes and the requirements are categorically different.
Autonomous operations, data monetisation, zero information friction: the new data capabilities within reach of modern data teams who wield AI.
The organisations that pull ahead in AI do not start with the most ambitious use case. They build three foundational capabilities that make every use case faster, cheaper, and more defensible, and then the portfolio accelerates because each new model inherits the infrastructure the previous ones built.
An AI delivery company that understands data. We know what AI needs from data, and we bring AI itself to modernise the data stack: self-monitoring pipelines, semantic resolution, unstructured extraction, legacy modernisation. The conversation starts from the binding constraint in your data architecture, not a generic maturity exercise.
