Accelerators are the templates, code, tests, and documents we reuse from one engagement to the next. We adapt them to your data, your systems, and your controls, so the work starts on your problem instead of on setup. What we adapt for you is yours to keep.
Each accelerator is fitted to your data, platforms, and controls. Your environment stays as it is.
The code, configuration, and documents we adapt for you are handed over at the end of the engagement.
Accelerators run on the major clouds and data platforms. We recommend tools based on fit.
Access controls, tests, logging, and documentation are part of each accelerator from the first increment.
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.
Tell us the business decision you want to improve. We will tell you which accelerators apply and what the first step would be.
