Services
Five fronts, one team
We cover the full chain: from Lakehouse architecture to AI agents running in production, with governance and cost under control.
01
Data Platform on Databricks
Your data unified in a governed Lakehouse, replacing legacy DWs and silos.
For whom
Companies with data fragmented across Redshift, Synapse, legacy Snowflake or ungoverned lakes.
What we deliver
- Medallion architecture (Bronze/Silver/Gold) on Delta Lake.
- Unity Catalog for governance, lineage and fine-grained access control.
- Delta Live Tables for declarative pipelines.
- Migration from Hive, Redshift, Synapse or Snowflake.
- Cost optimization: cluster policies, Photon, auto-scaling, spot.
02
Data Engineering
Reliable ingestion, clear modeling, measured data quality.
For whom
Teams that need production batch and streaming pipelines, not POCs.
What we deliver
- Batch and streaming ingestion: Kafka, Kinesis, Event Hubs, Pub/Sub, Auto Loader.
- Orchestration with Databricks Workflows, Airflow, Dagster or Azure Data Factory.
- Modeling with dbt or Delta Live Tables.
- Data quality with Great Expectations and DLT expectations.
- CDC with Debezium, Fivetran or native Databricks ingestion.
03
Machine Learning and MLOps
Models that make it to production, get monitored and retrained.
For whom
Data science teams that want to close the gap between notebook and production.
What we deliver
- Training with MLflow, AutoML when applicable, feature stores.
- Serverless deployment: Databricks Model Serving, SageMaker, Azure ML, Vertex AI Endpoints.
- Drift monitoring, observability and scheduled retraining.
- CI/CD with GitHub Actions and Azure DevOps for dev → stg → prod promotion.
04
Generative AI and LLMs
Production RAG, agents live in production, fine-tuning with frontier foundation models.
For whom
Companies that want to take LLMs from POC into real use cases.
What we deliver
- Production RAG: chunking, embeddings, vector DBs (Databricks Vector Search, Pinecone, Weaviate, Qdrant, pgvector).
- Agents with LangGraph, CrewAI, Bedrock Agents, Azure AI Agents, Vertex AI Agent Builder.
- Fine-tuning and adaptation: LoRA, QLoRA, Mosaic AI Model Training.
- Multi-provider LLM gateway: OpenAI, Anthropic (Claude), Google (Gemini), Meta (Llama), Mistral, DBRX.
- Evaluation and observability: Langfuse, LangSmith, TruLens, Ragas, guardrails.
- LLMOps: prompt versioning, A/B testing, cost tracking, PII redaction, rate limiting.
- AI security: prompt injection, data leakage, compliance (GDPR, HIPAA, ISO 42001).
05
Cloud and DevOps
Reproducible infra as code, security by default, cost under control.
For whom
Teams that need the foundation on which data and models actually run.
What we deliver
- IaC with Terraform or CDK on AWS, Azure and GCP.
- CI/CD with GitHub Actions, Azure DevOps or GitLab.
- Observability with CloudWatch, Azure Monitor, Datadog or Grafana.
- FinOps: budgets, anomaly detection and rightsizing.
- Security: least-privilege IAM, secrets in Vault or Secrets Manager, hardening.
Which of these fronts is yours?
Tell us the context and we reply with a concrete plan, not a generic proposal.
Let's talk →