Lakehouse on Databricks, warehouse and pipelines on AWS, ML that trains and serves on schedule, and analytics that answer in minutes instead of tomorrow morning. AWS Partner (Select Tier) and Databricks Consulting Partner.
Data & AI Platforms is the Erbis service for building the data foundation AI runs on: Databricks lakehouse, AWS data stacks on Redshift, Glue and SageMaker, near-real-time ETL, ML pipelines and analytics. It is for CTOs and heads of data whose operational data sits in databases, SaaS tools and spreadsheets and who need one governed platform before scaling AI.
Stanford’s 2026 AI Index reports that 88% of organizations use AI in at least one function, but fewer than 10% have fully scaled it in any single function. Gartner names poor data quality first among the reasons 30% of GenAI projects are abandoned after proof of concept. In healthcare, KLAS finds data readiness is the number one barrier to AI. The pattern repeats across industries: the model is ready, the data is not.
Most operational data estates grew by accretion. Orders in one database, transit updates in another, finance in the ERP, customer data in a SaaS tool, and a BI layer refreshed overnight. Analysts reconcile by hand. Data engineers patch pipelines nobody documented. An LLM pointed at that estate answers confidently and wrongly. An ML model trained on it learns the reconciliation errors.
Erbis builds data platforms in the client’s own AWS or Databricks account, so security, cost and compliance stay under your control. We have modelled 304K shipments for ETA prediction on SageMaker, brought data latency down from overnight to minutes with Glue, and delivered generative-AI analytics on Redshift that returned insights 60% faster. We do not run a hosted data product. We build yours, in your account, and hand it over.
One governed platform for BI, ML and agents, on the lakehouse you already pay for or one we stand up.
Warehouse, pipelines and ML on AWS-native services, in your account, under your IAM.
Operational data in the warehouse in minutes, not the next morning. Structured and unstructured, from streams, databases and third-party APIs.
Feature engineering, training, evaluation, deployment and retraining as a pipeline, not a notebook.
Dashboards people open and questions people can ask in plain language, embedded where the work happens.
The retrieval layer, vector index and access rules that let an agent answer from your data without leaking it.
For platforms that already exist: cost, performance, governance and readiness for AI.
A data platform is judged in month twelve, not at the demo. Four things are in place before handover.
Catalog, lineage and access control from day one: Unity Catalog on Databricks, Glue Data Catalog on AWS. Row- and column-level security for tenants and roles. PII and PHI classified, masked and retained under written rules. Everything runs in your account under your keys; GDPR, HIPAA and ISO 27001 controls are documented, not assumed.
Data quality tests on every pipeline: schema, null rates, referential checks, freshness. ML models carry a held-out test set and a written acceptance threshold; for shipment-ETA prediction that was an RMSE of 21.4 h. Reports and dashboards are reconciled against source systems before release.
Freshness, volume, failure and cost dashboards per pipeline and per table. Warehouse and cluster spend tracked per team with budgets and alerts. Endpoints shut down when idle. You see cost per query and cost per prediction, not one monthly bill.
The platform feeds your BI, your applications and your agents through documented interfaces. Infrastructure is Terraform, pipelines are in your repository, and runbooks cover on-call. Your engineers pair with ours during the build and take over at a date we agree, with support under SLA if you want it.
60%faster insights with AI-driven visualizations and automated reporting
Embedded dashboards and natural-language Q&A on Redshift and QuickSight Q for a multi-tenant SaaS, with Keycloak SSO and row-level security per tenant.
21.4 hRMSE of the linear regression model on 80,889 test shipments
Linear regression and XGBoost on Amazon SageMaker predict delivery times from 304K shipments, with account-based Lambda routing and no GPU CapEx.
$10M+annual savings across the companies using the solution
AWS Textract plus SageMaker Ground Truth human-in-the-loop review digitizes ~95% of invoices unattended, saving 10+ companies $10M+ a year.
6 monthsto complete the migration to AWS
A marketing SaaS serving 3,500+ brands in 70+ countries moved from IBM to AWS in six months, with weekend cut-over and one-minute replication.
Two weeks. We inventory sources, volumes, latency needs, consumers and compliance constraints, and decide whether the problem needs a warehouse, a lakehouse, a pipeline or a model. Output: target architecture, cost estimate and a first increment with a metric.
Ingestion, cleaning and modelling of the datasets the first use case needs. Catalog and access rules set up before data lands. Model architecture or analytics design chosen for the requirement, not the fashion.
A working pipeline, dashboard or model on real data within 2 weeks of build start. Iterated with the people who will use it. Acceptance thresholds tested against a held-out set before anyone calls it done.
Production rollout with monitoring, cost controls and runbooks. Retraining schedules, freshness SLAs and quarterly cost reviews. The platform is extended use case by use case rather than rebuilt.
A warehouse (Redshift, Snowflake) serves structured, SQL-shaped analytics well and is the simpler choice when that is the whole requirement. A lakehouse (Databricks on Delta Lake) adds unstructured data, streaming and ML on the same governed storage, which matters once agents and models are consumers, not just dashboards. Many clients run Redshift for BI and Databricks for ML. We decide in discovery from your sources, consumers and latency needs, not from a preference.
The platform runs in your AWS or Databricks account under your IAM and your keys; Erbis engineers work through scoped roles you can revoke. PII and PHI are classified on ingestion, masked or tokenized, and covered by row- and column-level security. Access and lineage are recorded in the catalog. Erbis is ISO 27001 certified and works under DPAs and, for healthcare, BAAs. Nothing leaves your account to be processed elsewhere.
Discovery takes about two weeks and produces a target architecture, a cost estimate and a fixed-price first increment. The first working pipeline, dashboard or model runs on real data within 2 weeks of build start. A first production increment, such as a near-real-time ETL feed into Redshift or an embedded analytics layer, typically takes two to three months. The platform then grows one use case at a time rather than as a multi-year program.
Every model ships with a held-out test set, an acceptance threshold and a monitoring job that tracks prediction quality and input drift. Retraining runs on a schedule or on a drift trigger, and a new version is promoted only if it passes the same threshold. Endpoint cost is controlled with auto-shutdown and routing. We operate this with your team under an SLA or hand it over with runbooks; the pipelines are in your repository either way.
Two costs matter: the build, which we quote as a fixed price per increment after discovery, and the run cost of the cloud services, which we estimate in discovery and track per team once live. Run cost is where platforms go wrong; we design for it with serverless services, idle shutdown, storage tiering and per-query cost visibility. A typical first increment is a two- to three-month engagement. We do not charge a platform license.
Yes, and we prefer it. Erbis is a Databricks Consulting Partner and an AWS Partner at Select Tier, and most engagements extend a platform the client already runs rather than replacing it. We start with an audit of cost, reliability, governance and AI readiness, then build the next increment inside the same account, catalog and CI/CD. Where migration is the right call, such as Hadoop or an on-premises warehouse to the lakehouse, we plan it around a zero-downtime cutover.
A source list, a rough volume and the question nobody can answer today. You get a target architecture, a first increment and a fixed price for it.
AWS partner · Databricks partner · ISO 27001 · You own the code from day one
