Flaperon AI

Databricks data and AI, flown end to end.

Take-off · flaperon 20° down · lift builds

Small surface. Substantial impact.

AssessmentCloud & platform setupUnity Catalog governance

Roll · left further down, right up · onto heading

One surface. Many roles.

Data migrationData modellingMedallion architecture

Climb · flaperon 20° down · through the layers

Everyone has the engine. We build the control surfaces.

Proof of conceptEvaluation gatesGo / no-go

Level at 40,000 ft · flaperon closed · build

Height locked. Build what people use.

Lakeflow pipelinesDatabricks SQL & AI/BIML, GenAI & agents

Descent · flaperon 35° · drag from 40,000 ft · operationalise

Governed from the first table. Operated from the first model.

MLOps & Asset BundlesQuality & cost monitoringSupport

AI that lands.

Your data platform, flown from assessment to production.

Flaperon AI designs, migrates and operates Databricks lakehouses on Azure and AWS. Unity Catalog governance from the first table, MLOps from the first model, costs visible from the first cluster.

Start with an assessment See the flight plan

Databricks Consulting Partner. Built on the Databricks Data + AI Platform.

The flight plan

Four phases, each with defined deliverables, flown by one engineer.

Assessment through production runs on one platform: Unity Catalog governing it, Lakeflow moving it, MLflow and Model Serving putting it in front of the business. The same engineer who scopes it architects it, and is still there when it is running in production at two in the morning.

Phase 1 · Take-offBrakes off to 5,000 ft

Take-off. Assess and set up.

The assessment comes first, so we know exactly where the estate stands. Then the cloud and the Databricks account are built to security best practice: private networking, Entra ID or IAM identity, least privilege, and secrets and keys managed properly. The Unity Catalog metastore, spend attribution and source control are all switched on before the first table lands.

Current platform and tech stack assessment

Inventory of sources, pipelines, tools and costs. What stays, what goes, what it will cost to run.

AI readiness assessment and roadmap

Use case scoring, data maturity, a sequenced 12 month plan with budgets and owners.

Cloud and platform setup on Azure or AWS

Landing zone, private networking, Entra ID or IAM identity with least privilege, secrets and key management, and the Databricks account and workspace topology. Security best practice from the first workspace, not retrofitted.

Governance with Unity Catalog

Catalog design, lineage, row filters and column masks, and attribute based access control driven by governed tags, administered from day one.

Cost and code control from day one

System tables, budgets and tags so finance sees spend per workload from the first cluster, and Git with Databricks Asset Bundles versioning every notebook, pipeline and job from the first commit.

Serverless and the optimisation roadmap

Serverless compute, Photon, liquid clustering, predictive optimisation and right sizing. A quarterly list of what to tune next.

Proof of concept in weeks, not quarters

Scoped POCs with evaluation gates and a go or no-go you can defend to finance.

Phase 2 · RollBank to heading

Roll. Migrate and model.

Data comes off the legacy estate into Delta under Unity Catalog, ingested with Lakeflow Connect and shaped into a bronze, silver and gold model built for the questions the business will ask.

Migration from legacy warehouses and Hadoop

Warehouses, on-prem Hadoop, SQL estates and files, moved to Delta with lineage intact.

Ingestion with Lakeflow Connect

Managed connectors for enterprise applications, databases, cloud storage and message buses. Zerobus for application events.

Data model and medallion architecture

Bronze, silver and gold Delta tables designed for the questions the business will ask.

Pipelines with Lakeflow

Spark Declarative Pipelines with expectations built in, orchestrated by Lakeflow Jobs, streaming or batch.

Phase 3 · LevelHeight locked at 40,000 ft

Level. Build.

This is where the platform turns into products people actually use: Databricks SQL and AI/BI over governed gold tables, and models and agents tracked in MLflow, registered in Unity Catalog and served through Model Serving.

AI/BI Dashboards on Databricks SQL

Databricks dashboards built over governed gold tables and shared with the people who need them, with row filters and column masks doing the access control.

Genie Agents for the business

Domain Genie Agents configured with trusted tables, metrics and business rules, so the question gets asked in plain language and the answer can be relied on.

ML and GenAI on MLflow

Propensity, forecasting, recommendation and RAG. Tracked and registered in MLflow, served through Model Serving.

Agents with Agent Bricks

Knowledge assistants and supervisor agents over governed data, with evaluation gates before anything talks to a customer.

Real time AI applications

Lakebase and Databricks Apps when a use case needs an application, not a dashboard.

Phase 4 · DescentTop of descent to runway

Descent. Operate.

Production is where most AI programmes fail, so it is where we do the most work: Databricks Asset Bundles promoting dev to test to prod, data quality monitoring and alerts running ahead of the dashboards, and spend visible per workload in system tables.

MLOps, LLMOps and AIOps

Databricks Asset Bundles with Git, Azure DevOps or GitHub. Every pipeline, model and agent is code, reviewed and promoted dev to test to prod.

Data quality monitoring in Unity Catalog

Anomaly detection and data profiling on every gold table and inference table, alerting before dashboards go wrong.

Support and maintenance

On-call runbooks, upgrade cadence and a named engineer who already knows the platform.

Before every landing

Run the landing checklist.

This is the checklist a Databricks platform has to pass before we call it production. Press and hold to run it.

Press and hold the button. Release early and the checklist eases back down.

Unity Catalog lineage complete across every gold table
Row filters and column masks tested against the roles that will use them
Every pipeline, model and agent deployed from a reviewed Databricks Asset Bundle
Models and agents registered in Unity Catalog and served through Model Serving
Anomaly detection and data profiling live, with alerts, ahead of the first dashboard
Usage tagged per workload, budgets set, spend visible to finance in system tables
Runbook written and a named engineer on call

Databricks Expertise

The Databricks stack, wired the way we run it.

Ingestion through to agents, with Unity Catalog governing every layer and everything deployed as code.

Lakeflow Connect

ingest · SaaS · databases

Lakeflow Jobs + Declarative Pipelines

orchestrate · expectations

Delta Lake

bronze · silver · gold

Databricks SQL + AI/BI

dashboards · Genie

MLflow + Model Serving

track · register · serve

Agent Bricks + AI Search

build · retrieve · evaluate

Unity Catalog one governance layer under all of it: lineage, row filters and column masks, audit, data quality monitoring, cost attribution

Databricks Asset Bundles + Git everything above ships as code through Azure DevOps or GitHub, promoted dev to test to prod

Serverless compute + Photon the engine underneath, tagged and budgeted so finance can read the bill in system tables

Certifications

Certified where it counts.

Six Databricks credentials held by the practice lead, including the partner programme's Solutions Architect Champion.

About

One practice, one accountable engineer.

Flaperon AI is a specialist Databricks consultancy for organisations that need a lakehouse designed, migrated and run properly. On Azure or AWS, with Unity Catalog governance from the first table and MLOps from the first model.

The practice is led by Aditya Bhatraju, whose work spans the full width of the platform: enterprise Databricks setup and account administration, Unity Catalog governance, data engineering, the data science process, and production machine learning on Azure and AWS. Feature pipelines in PySpark, models tracked and registered in MLflow, promoted through Azure DevOps. He holds six Databricks certifications and a master's degree in data science, and he has built the platforms other teams then built their work on.

Every engagement is scoped, architected and delivered by the same person who will stand behind it in production. That is the whole point of the firm.

Contact

Talk to the engineer who will build it.

Tell us where the platform is today. You will get a written reply within one working day, or book a 30 minute assessment call directly. No deck, no pitch.

Book a 30 minute call Email