Services

A modern data stack and AI-ready data, so analytics and AI work from information you can trust.

Data pipelines (ETL/ELT)

Reliable ELT pipelines on a modern stack, so data arrives clean, tested, and on time.

  • IngestionFivetran, Airbyte, and custom connectors from any source.
  • Transformationdbt models with tests and documentation.
  • OrchestrationAirflow, Dagster, or Prefect scheduling with retries.
  • Pipeline monitoringAlerts on failures, delays, and anomalies.
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Data platforms

Cloud warehouses and lakehouses on Snowflake, Databricks, BigQuery, or Microsoft Fabric, built on open table formats to avoid lock-in.

  • Platform selection and setupThe right platform for your workloads and budget.
  • Lakehouse architectureApache Iceberg and Delta Lake for open, flexible storage.
  • Data modelingLayered models that analysts and AI can both use.
  • Cost and performance tuningQueries and compute sized to what you actually need.
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AI-ready data

Most enterprise knowledge sits in documents, tickets, and email. We turn it into clean, current, permission-aware data that agents and assistants can rely on.

  • Unstructured data processingPDFs, slides, emails, and scans parsed into clean text and tables.
  • Chunking and embeddingsContent split and embedded for accurate retrieval.
  • Vector storespgvector, Pinecone, Weaviate, or your platform’s native vector search.
  • Freshness and access syncIndexes kept current, with source permissions carried through.
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Data modernization and migration

Move off legacy databases and warehouses to modern cloud platforms, with every record validated.

  • Legacy database migrationOracle, SQL Server, Teradata, and on-premise systems to the cloud.
  • AI-assisted SQL conversionStored procedures and queries translated with AI, then tested.
  • Validation and reconciliationRow-level checks that prove nothing was lost.
  • Cutover planningPhased switch-over with rollback options.
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Real-time and streaming

Event-driven pipelines that deliver data in seconds for live operations and real-time AI.

  • Event streamingKafka, Kinesis, and Pub/Sub architectures.
  • Change data captureDatabase changes streamed with Debezium and similar tools.
  • Stream processingFlink and Spark Structured Streaming for real-time transforms.
  • Real-time context for AIFresh data delivered to models and agents as it changes.
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Data quality, governance and lineage

Trustworthy data with clear ownership, tracked lineage, and controls ready for audit.

  • Data quality and contractsAutomated tests and data contracts on critical tables.
  • Catalog and ownershipUnity Catalog, Purview, or open-source catalogs.
  • LineageEvery metric traced back to its source.
  • Access control and privacyPolicies for sensitive and regulated data.
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Analytics engineering and BI

A semantic layer and dashboards that give everyone the same numbers, plus plain-English querying for non-technical teams.

  • Semantic layerShared metric definitions in dbt or your BI tool.
  • Dashboards and reportingPower BI, Tableau, Looker, and more.
  • Self-service analyticsGoverned access for teams to explore data.
  • Conversational analyticsAsk questions of your data in plain English.
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MLOps and DataOps

Models, agents, and pipelines run in production with versioning, monitoring, and automated retraining.

  • Model deploymentServing on SageMaker, Vertex AI, Databricks, or Kubernetes.
  • LLMOpsPrompt versioning, eval tracking, and model gateways.
  • Monitoring and retrainingDrift detection and automated retraining pipelines.
  • VersioningData, models, and code versioned together.
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Enter where your team needs help.

Some projects begin with an opportunity to evaluate. Others start with a defined product build or an AI system that needs to work more reliably. We scope the work around the problem, the people who will use the result, and the systems it needs to fit.

  1. Understand the work

    Align on the workflow, users, constraints, available data, and what success would look like for this engagement.

  2. Design and build

    Bring the appropriate product, AI, data, and engineering specialists together to make and test the solution.

  3. Deploy and improve

    Prepare the system for use, document how it works, observe its performance, and plan the next improvements with your team.

Bring us the workflow or system you are working on.

Tell us what you want to build, what already exists, and where your team needs specialist help. We will start with the problem and identify a useful scope together.

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