Search results
35 resultsHow we run a one-week research spike
The exact process we use to de-risk a technical bet in five days.
CI/CD Pipeline Design — From Commit to Production
Stages, gates, deployment strategies, and keeping pipelines fast.
Applied AI & ML — Service Overview
Everything included in our Applied AI engagements: RAG, agents, fine-tuning, evals, and guardrails.
What is Retrieval-Augmented Generation (RAG)?
A plain-English explanation of RAG: why it beats pure LLM memory for production knowledge systems.
Database Schema Migration Strategies
Expand-contract pattern, zero-downtime migrations, and tooling.
Choosing a vector database: pgvector vs Pinecone vs Weaviate
A practical comparison across dimensions that matter for production RAG systems.
Building a Data Quality Framework
Dimensions of data quality, validation layers, and monitoring in production pipelines.
Progressive Delivery — Feature Flags, Canary, and Dark Launching
Techniques for releasing software confidently at any scale.
Privacy-First Data Design — PII Handling Patterns
Tokenisation, pseudonymisation, encryption at rest, and right-to-deletion workflows.
DORA Metrics — Measuring Engineering Delivery Performance
Deployment frequency, lead time, MTTR, and change failure rate in practice.
Schema Registry and Avro for Kafka Data Contracts
Why schema management matters for streaming pipelines and how to implement it.
How long does a typical project take?
Timeline expectations from kick-off to launch.
Elasticsearch Indexing Strategy and Performance
Mapping, sharding, bulk indexing, and query optimization for Elasticsearch.
gRPC Service Design — Protocol Buffers and Production Patterns
Proto file design, streaming, deadlines, interceptors, and error handling.
LLM Guardrails: keeping AI outputs safe in production
Techniques for input/output filtering, content policies, and hallucination mitigation.
Can you work with our existing codebase?
Yes — we regularly parachute into production systems.
Container Registry Management and Image Lifecycle
Tagging conventions, vulnerability scanning, retention policies, and registry options.
Airflow Best Practices for Production Pipelines
Idempotency, backfilling, SLA misses, and common pitfalls to avoid.
Fine-tuning LLMs: when, why, and how
A practical guide to LoRA, QLoRA, and full fine-tuning for production use cases.
Infrastructure as Code for Data Platforms with Terraform
Managing cloud data infrastructure reproducibly with Terraform.
Kubernetes Deployment Patterns for Production Services
Deployments, Services, Ingress, HPA, and resource management.
The Twelve-Factor App — Principles for Modern Services
How the twelve factors apply to real production services today.
Orchestrating Pipelines with Apache Airflow
DAGs, operators, scheduling, and production best practices for Airflow.
Semantic Versioning — MAJOR.MINOR.PATCH in Practice
When to bump each version number and how to communicate breaking changes.
Load Testing with k6
Script a realistic load test, interpret results, and find bottlenecks before they find users.
Testing Strategy for Data Pipelines
Unit tests, integration tests, data contract tests, and regression testing for pipelines.
Building a Data Catalog with DataHub
Ingestion, metadata, search, and making your catalog actually useful.
OpenAPI Spec-First API Development
Write the contract before writing code — benefits, tooling, and workflow.
Stream Processing with Apache Flink
Event time vs processing time, windows, stateful operators, and production deployment.
Our observability stack for production services
Logs, metrics, traces — how we instrument every service we ship.
Logging Best Practices for Production Services
Structured logging, log levels, correlation IDs, and log aggregation.
Redis Caching Patterns for Production Applications
Cache-aside, write-through, TTL strategy, and cache invalidation approaches.
Feature Flags — Safe Deployment and Gradual Rollout
Types of flags, implementation patterns, and avoiding flag sprawl.
Docker Containerisation Best Practices
Writing efficient Dockerfiles, multi-stage builds, security hardening, and image size reduction.
Dependency Management and Supply Chain Security
Lock files, vulnerability scanning, SBOM, and keeping dependencies up to date.