Templates, code, tests, and documents we reuse across engagements and adapt to your data, systems, and controls, so the work starts on your problem instead of on setup.
We start from a copy in your environment. From day one it is your code.
Example names and values are replaced with yours, and the code is fitted to your cloud, tools, and controls.
Access controls, tests, logging, and documentation come with each one from the first increment.
At the end of the engagement, your team keeps the adapted code, configuration, and documents.
Check whether your data can support a decision before you fund the work.
We take one business decision you want to improve and check the data behind it. We profile the actual source systems for completeness, consistency, ownership, and access. We list the security and compliance requirements that apply. You find out what is ready, what is missing, and what it would take to close the gaps.
A starting data platform, set up as code on your cloud.
A reference architecture and infrastructure-as-code templates for a lakehouse or cloud data warehouse. It covers ingestion, storage layers, access policies, and deployment pipelines. We fit it to your cloud and the tools you already use, so your team can spend its time building data products.
Starting models and matching rules for customer, product, and supplier data.
Data models, match and merge rules, and stewardship workflows for the master data domains most organizations need. We tune the rules against a sample of your records in the first cycles, so you see real match results early. It works with the major MDM platforms and with custom builds.
Define each business metric once and use it in every report.
Templates for building a shared metrics layer: how each metric is defined, who owns it, how it is tested, and how it is approved for use. We start with the metrics behind the decisions your teams make most often. Then we connect your reporting and self-service tools to those definitions.
Pipelines to deploy, monitor, and retrain machine learning models.
Pipeline templates for training, validating, deploying, monitoring, and retraining machine learning models. Drift monitoring, model versioning, and rollback are included from the start. We use them for new models and to move existing models from notebooks into production.
Tests that measure assistant and agent quality before and after launch.
A test framework for assistants, copilots, and agents. We build a set of questions and expected answers from your own content with your subject-matter experts. We add edge cases and adversarial prompts. Every change to a prompt, model, or retrieval setting is scored for accuracy, use of sources, safety, and cost, and releases wait on the results.
Connect generative AI to your documents and keep existing access rules.
A reference implementation of retrieval-augmented generation (RAG) for enterprise content. Document permissions carry through to the search index, so people only get answers from content they can already see. Every answer cites its sources and every request is logged. We adapt it to your document stores, identity provider, and chosen models.
Scope, permissions, logging, and approvals for AI agents.
The same controls we apply to the AI agents on our own team. Each agent gets a written scope, minimum necessary permissions, a log of every action, and human approval for high-impact actions. Agents cannot change their own permissions. Any agent can be paused or switched off at once.
Policies, templates, and workflows to govern how your organization uses AI.
A starting set of policies and workflows for governing AI. It covers acceptable use, risk assessment, an inventory of models and agents, and ongoing monitoring. We fit it to your risk tolerance and connect it to how your teams already release work.
Each one stands alone. Together, the output of one feeds the next.
The Data Readiness Assessment runs in the Ground step. The gaps it finds feed the Master Data Starter (duplicates and join coverage) and the Metrics Layer Starter (definitions that need agreement).
The Data Platform Foundation is where pipelines, master data, and metrics run. The MLOps Templates train and monitor models on it and write an Evaluation Card for each version.
The Secure RAG Starter builds the assistant. GenAI Evaluation Tests check it before every release, including one permission test for each restricted area.
AI Agent Controls limit what an agent can do. The AI Governance Starter records every system, its risk tier, and the controls that tier requires. Test results, audit logs, and scope files become the evidence.
Tell us the decision you want to improve. We will tell you which accelerators apply.
