The Technology
Ecosystem

The entry point of every data system. We design ingestion architectures that handle structured, semi-structured, and unstructured data from dozens of sources simultaneously — with sub-second latency for event-driven systems.

A

Apache Kafka

High-throughput event streaming. Our default for real-time ingestion at scale.

Core

A

AWS Kinesis

Managed streaming on AWS-native deployments.

Cloud

D

Debezium

CDC (Change Data Capture) from relational databases into streaming pipelines.

Core

F

Fivetran

Managed connectors for 300+ SaaS and database sources.

Select

A

Airbyte

Open-source EL(T) when full connector control is needed.

Select

The lakehouse pattern combines the scale of a data lake with the reliability and performance of a data warehouse. It's the architectural standard for organisations that need both analytics and AI training on the same data layer.

D

Delta Lake

ACID transactions on object storage. Default for Databricks-centred architectures.

Core

A

Apache Iceberg

Open table format. Preferred for multi-engine environments (Spark + Flink + Trino).

Core

A

Apache Hudi

Upserts and incremental processing on data lakes.

Select

D

Databricks Unity Catalog

Unified governance across lakehouse assets — tables, models, dashboards.

Platform

The analytical query layer where business logic lives. We select warehouse technology based on the client's existing cloud estate, query patterns, and AI workload requirements — not vendor preference.

S

Snowflake

Our most commonly deployed warehouse. Separation of compute/storage, strong governance.

Core

G

Google BigQuery

Serverless warehouse for GCP-native clients. Exceptional for large-scale analytics.

Cloud

D

Databricks SQL

When warehouse and ML platform need to be unified on one compute layer.

Platform

D

dbt

Transformation layer that turns raw data into trusted analytical models. Used on every engagement.

Core

The layer that makes AI systems intelligent at runtime. Feature stores serve pre-computed features to models at inference time. Vector databases power semantic search, RAG architectures, and long-term AI memory.

P

Pinecone

Managed vector database. Default for production RAG and semantic search systems.

Core

W

Weaviate

Open-source vector DB when self-hosted control is required.

Select

P

pgvector

Vector search inside PostgreSQL. Zero additional infrastructure when PG is already the operational DB.

Select

F

Feast

Feature store for serving computed features to ML models at low latency.

Core

The layer where models are trained, evaluated, versioned, and registered. We select platforms based on the model type, compute requirements, and the cloud estate — never a one-size-fits-all approach.

M

MLflow

Experiment tracking, model registry, and deployment. Platform-agnostic and our default.

Core

H

Hugging Face

Open-source model hub and inference for NLP, vision, and multimodal workloads.

Core

L

LangChain

Orchestration framework for LLM-powered applications and RAG pipelines.

Core

A

Anthropic Claude

Frontier LLM for enterprise applications requiring safe, interpretable AI outputs.

LLM

A

AWS SageMaker

End-to-end ML platform for AWS-native clients. Strong for custom model training at scale.

Cloud

Where trained models meet production traffic. The serving layer must handle latency SLAs, versioned deployments, A/B testing, and graceful degradation. We design serving infrastructure for the traffic patterns and SLAs of each use case.

F

FastAPI

Default for building model serving APIs. High performance, async, auto-documentation.

Core

B

BentoML

Model packaging and serving with built-in batching and adaptive scaling.

Select

T

Triton Inference Server

NVIDIA's high-performance inference server for GPU-accelerated models.

Select

K

Kubernetes + Istio

Container orchestration and service mesh for scalable, observable model deployments.

Core

The layer where data becomes decisions. We build analytics infrastructure that serves everyone from data engineers to board members — from technical SQL interfaces to self-serve dashboards to AI-embedded insights.

T

Tableau

Enterprise BI standard. Most commonly deployed for executive-facing dashboards.

Core

L

Looker

LookML semantic layer for governed self-serve analytics in GCP estates.

Cloud

P

Power BI

Microsoft-native BI for Azure and Office 365-integrated deployments.

Cloud

G

Grafana

Operational metrics, model performance dashboards, and system observability.

Core

A

Apache Superset

Open-source BI when vendor independence is a priority.

Select

The conductor of the data and ML pipeline. Orchestration determines when jobs run, in what order, with what dependencies, and what happens when they fail. The right orchestrator is determined by the complexity of the pipeline and team preference.

A

Apache Airflow

Industry standard for complex DAG-based data pipelines. Our default for mature engineering teams.

Core

P

Prefect

Modern Python-native orchestration. Faster to adopt than Airflow for smaller teams.

Select

D

Dagster

Asset-centric orchestration. Strong for teams who want lineage built into the pipeline definition.

Select

T

Temporal

Durable execution for long-running workflows and distributed transactions.

Select

The layer most organisations underinvest in until it becomes a crisis. AI systems are only as trustworthy as the data they were trained on. Governance and quality frameworks ensure that data moving through the system meets the standards required for AI inference.

C

Collibra

Enterprise data governance platform. Data catalogue, lineage, and policy management.

Enterprise

D

DataHub

Open-source metadata platform. Our default for teams that need cataloguing without the Collibra cost.

Core

G

Great Expectations

Data quality testing framework integrated into ingestion and transformation pipelines.

Core

M

Monte Carlo

Data observability — detecting anomalies in data before they reach models.

Select

Deploying a model is not the end. Monitoring model performance, detecting drift, tracking feature importance over time, and triggering retraining pipelines is the operational discipline that keeps AI systems working in production beyond launch day.

E

Evidently AI

Model monitoring and drift detection. Open-source, integrated into CI/CD pipelines.

Core

A

Arize

ML observability platform for real-time model performance monitoring.

Select

P

Prometheus + Grafana

Infrastructure and application metrics. Every deployment gets a Grafana dashboard on day one.

Core

D

Datadog

Full-stack observability for production AI systems. APM, logs, traces in one platform.

Enterprise

We are cloud-agnostic by design. We architect for the client's existing estate — not for our preferred vendor. Every major cloud platform has been deployed in production. Multi-cloud and hybrid architectures are fully supported.

A

Amazon Web Services

Most commonly deployed. SageMaker, Redshift, Kinesis, Glue, S3, EKS — all production-certified.

Core

M

Microsoft Azure

Primary for clients with Microsoft estates. Azure ML, Synapse, Event Hubs, AKS.

Core

G

Google Cloud Platform

Preferred for BigQuery-centred analytics and Vertex AI workloads.

Core

T

Terraform

Infrastructure as code across all three clouds. Every environment is code-defined and reproducible.

Core

Security is not a layer — it runs through every other layer. AI systems have a distinct threat landscape from traditional software: model poisoning, prompt injection, data extraction, and adversarial inputs require architecture-level responses, not perimeter controls.

H

HashiCorp Vault

Secrets management and dynamic credentials across all pipeline components.

Core

O

Okta

Identity and access management. Zero-trust access to data and model serving endpoints.

Enterprise

C

Cloudflare

Edge security, DDoS protection, and WAF for AI-facing APIs.

Enterprise

S

Splunk / SIEM

Security information and event management for AI system audit trails.

Select