Your AI Platform Needs a Lakehouse That Can Keep Up.
Your data and AI platform shouldn't hold you back. We architect, build, and optimise Databricks lakehouses — with Unity Catalog, MLflow, and Delta Lake — so your teams ship models, not tickets.
The Reality
Fragmented Data Infrastructure Is Why AI Projects Fail
Your teams aren't failing because they lack talent. They're failing because the platform underneath them was never designed for AI-scale workloads. These problems compound — and they're costing you millions in wasted compute and lost opportunity.
Siloed data across warehouses and lakes
Your data lives in 5 different systems — Snowflake, S3, on-prem databases, legacy warehouses — and nobody trusts a single number.
ML models that never reach production
Data scientists build models in notebooks. Engineering can't deploy them. Most ML projects never make it past the prototype stage.
Governance is an afterthought
No unified catalog. No lineage. No access controls that work across data and AI assets. Compliance audits are a scramble every quarter.
Costs spiralling without visibility
Clusters running 24/7, no autoscaling, no job-level cost attribution. Your cloud bill grows meaningfully year-over-year and nobody knows why.
Pipelines break silently
ETL jobs fail overnight. Nobody notices until a dashboard shows stale data on Monday morning. There's no alerting, no SLA monitoring.
No real-time capability
Batch pipelines run once a day. Your fraud detection, pricing engine, and recommendation system are always 24 hours behind.
BI tools disconnected from the platform
Tableau and Power BI query raw tables instead of a governed semantic layer. Every analyst writes their own SQL — and gets different answers.
AI governance doesn't exist
Models are deployed with no versioning, no lineage, and no audit trail. You can't explain to a regulator what your model does or why.
Our Expertise
Four Ways We Engineer Your Databricks Platform
Lakehouse Architecture & Modernisation
Design and deploy a unified lakehouse that replaces fragmented data warehouses and data lakes with a single, governed platform built on Delta Lake.
- Lakehouse architecture design & roadmap
- Delta Lake implementation & optimisation
- Legacy warehouse migration (on-prem or cloud)
- Multi-cloud & hybrid deployment strategy
MLOps & AI Platform Engineering
NEWBuild production-grade ML pipelines with MLflow — from experiment tracking and model registry to automated retraining and monitoring.
- MLflow setup & experiment tracking
- Model registry & versioning workflows
- Automated retraining & drift detection
- Feature store implementation
Data Engineering & Pipeline Operations
Build reliable, scalable data pipelines with Spark, Delta Live Tables, and Databricks Workflows — from ingestion to transformation.
- Delta Live Tables & Structured Streaming
- Databricks Workflows orchestration
- Data quality checks & SLA monitoring
- Cost optimisation & cluster tuning
LLMOps & Generative AI Engineering
Deploy and manage large language models on Databricks — fine-tuning, RAG pipelines, vector search, and production serving with governance.
- LLM fine-tuning on Databricks
- RAG pipeline & vector search setup
- Model serving & inference endpoints
- AI governance & Unity Catalog for AI
Databricks + AI
Databricks + AI: The Platform Every AI Team Needs
Databricks isn't just a data warehouse — it's an AI platform. From feature engineering and model training to LLM fine-tuning and real-time inference, we build the infrastructure that turns your data into production AI.
Explore AI CapabilitiesData Intelligence & BYOM
Go beyond SQL and dashboards. Bring your own models into Databricks — or use built-in AI functions for classification, summarisation, and entity extraction.
MLflow & Experiment Management
Track every experiment, compare model runs, and promote the best performers to production — all within a governed, reproducible workflow.
Unity Catalog & AI Governance
Govern data, models, and AI assets from a single control plane. Fine-grained access, lineage tracking, and compliance — built in, not bolted on.
Real-time Serving & Inference
Deploy models to production endpoints with autoscaling, A/B testing, and real-time monitoring — so your AI delivers value, not just predictions.
Databricks Ecosystem
The Full Stack That Surrounds Your Databricks Platform
Databricks doesn't operate in isolation. We integrate it with your entire data ecosystem — ingestion, transformation, governance, BI, and AI serving — so every layer works together.
+ native connectors to data sources via Databricks Partner Connect
Our Process
From Fragmented Data to AI-Ready Lakehouse in Weeks
Assessment & Discovery
We audit your current data infrastructure — sources, pipelines, governance gaps, and ML readiness — and deliver a prioritised lakehouse roadmap.
Architecture Design
We design your lakehouse architecture: medallion layers, Delta Lake schemas, Unity Catalog policies, and compute strategy — tailored to your workloads.
Build & Migrate
Our engineers build the pipelines, migrate data from legacy systems, implement Delta Live Tables, and deploy MLflow for model tracking.
Deploy & Integrate
We go live — connecting Databricks to your BI layer (Tableau, Power BI), downstream applications, and alerting infrastructure.
Optimise & Scale
Post-launch, we tune cluster configurations, optimise query performance, reduce cloud costs, and expand the platform as your AI ambitions grow.
Case Study
Pharma Company Consolidates Data Sources and Ships ML Models Notably Faster on Databricks
A global pharmaceutical company was running 12 disconnected data sources across 3 cloud providers. ML model deployment took 6 months. We built a unified lakehouse on Databricks, implemented Unity Catalog for GDPR compliance, and deployed MLflow — cutting model deployment time by 75%.
4×
Faster ML deployment
90%
Less data duplication
12→1
Unified platform
GDPR
Compliant from day one
Faster ML model deployment
Unified · Governed · AI-Ready
Real Results
The Business Impact of an AI-Ready Lakehouse
1000+
Projects
600+
Customers
20+
Years of Enterprise Expertise
4.5
Customer Satisfaction Score
How We Work
Engagement Options
Pick the model that fits where you are. All engagements include a dedicated Databricks lead and a clear outcome definition.
Databricks Health Check
Ideal for: Teams already on Databricks who need an expert audit
A 2-week deep dive into your Databricks environment — cluster config, pipeline health, Unity Catalog setup, and cost efficiency — with a prioritised improvement plan.
- Cluster & compute cost analysis
- Pipeline reliability & SLA review
- Unity Catalog governance audit
- Delta Lake optimisation assessment
- Prioritised improvement roadmap
Lakehouse Migration & Build
Ideal for: Organisations migrating to or building on Databricks
A full lakehouse build — from architecture and migration through to MLflow, governance, and BI integration — delivered in 8–12 weeks with a dedicated engineering team.
- Everything in Health Check
- Medallion architecture implementation
- Data migration from legacy warehouses
- Delta Live Tables & pipeline orchestration
- MLflow & model registry setup
- BI tool integration (Tableau / Power BI)
AI Platform Engineering & Managed Service
Ideal for: Teams that want expert-managed Databricks operations
We manage your Databricks platform end-to-end — monitoring, optimisation, ML pipeline operations, and a dedicated engineering partner on call.
- Platform administration & monitoring
- Cluster & cost optimisation
- ML pipeline operations (MLOps)
- Dedicated Databricks engineer
- Priority SLA support
Connected Ecosystem
Databricks Powers the Intelligence Layer. Here's What It Feeds.
Your lakehouse isn't the destination — it's the engine. We connect Databricks to every downstream system so your data drives decisions, not just dashboards.
Tableau Analytics
Data Intelligence
AI Strategy
Salesforce Data Cloud
ML Engineering
dbt Transformations
Power BI Reporting
Consulting & Advisory