AI/ML & Data Analytics Accelerators
Our accelerator toolkit cuts delivery time in half across AI/ML and Data Analytics engagements. We publish our tools on GitHub so you can see exactly how we work before you hire us.
AI/ML Accelerators
A natural-language interface over your semantic layer. Connects via XMLA and pairs an LLM with your model metadata so business users can ask questions in plain English and get back working DAX, not just a guess. We roll this into every AI/ML discovery workshop.
View on GitHub open_in_newOn-prem generative AI for database work. Wires Ollama, LM Studio, or any OpenAI-compatible model directly into SQL Server Management Studio, so query generation and schema exploration happen without a byte of data leaving your network. Standard on every healthcare and financial services AI/ML engagement.
View on GitHub open_in_newAn AI/ML coding copilot that never phones home. Fully local inference for DAX, Python notebooks, feature-engineering scripts, and T-SQL, so our ML engineers can iterate on client models without routing proprietary data through a third-party API.
View on GitHub open_in_newBrings predictive and generative AI into the ERP itself. An Odoo 18 module that layers local LLMs over operational data, turning transactional records into an AI/ML- and analytics-ready dataset without a separate export pipeline.
View on GitHub open_in_newData Analytics Accelerators
A pre-wired medallion architecture — bronze, silver, gold — with domain schemas, sample datasets, and Power BI dashboards ready on day one. We fork it per engagement and layer in vertical-specific data models for manufacturing, healthcare, and financial services analytics.
Metadata-driven ELT that automates the repetitive 80% of bronze and silver layer pipeline work. Config-driven Spark notebooks replace hand-written ingestion code, which is why a 3-person analytics team can deliver what typically needs 8.
A 120+ function Python library for Fabric notebooks that automates Direct Lake migration, runs Best Practice Analyzer checks, surfaces VertiPaq engine statistics, and scripts the Tabular Object Model — the analytics equivalent of an ML training loop, but for semantic models.
Turns a single notebook cell into a full Power BI QA environment: automated model scanning, one-click BPA fixes, VertiPaq memory analysis, and auto-generated model diagrams for documentation.
Query authoring and performance profiling for semantic models — server timings, query plans, and trace capture our analysts rely on daily to keep dashboards fast at scale.
Headless semantic model development: batch metadata edits, C# scripting, and automated Best Practice Analyzer runs, without ever opening Power BI Desktop.
Proprietary
Production-ready forecasting, anomaly-detection, and classification pipelines, pre-tuned so client AI/ML use cases start from a working baseline instead of a blank notebook.
Star schemas and dbt models purpose-built for manufacturing, financial services, and healthcare analytics, mapped to the metrics each industry actually reports on.
Power BI .pbit files engineered to light up with live client data the moment the connection is made — no rebuild from a blank canvas.
Facilitator guides, sample datasets, and follow-up sequences purpose-built to walk a client team through their first AI/ML or analytics use case in a day.
Standardized scoring frameworks that grade AI/ML and data-analytics maturity and roll straight into a client-ready PDF report.