What we help you do

From trusted data to better delivery.

01

Data & Analytics

Ingest data from multiple sources, modernize warehouses, and build a trusted foundation for decisions.

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02

AI & Synthetic Data

Accelerate profiling and transformation with AI, then create privacy-conscious data for testing and learning.

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03

Software & Web

Build useful applications, websites, APIs, hosting, domains, and digital experiences that perform.

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04

Operations & Education

Improve daily delivery, support data operations, and help students build real project portfolios.

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AI, with people in the loop

Make AI useful for the work you actually do.

Start with one workflow: drafting documentation, exploring data mappings, assisting code review, or organizing everyday work. We can help scope custom-agent instructions, evaluation examples, permissions, and a human review process.

Tool selection depends on your accounts, provider terms, data restrictions, and agreed engagement scope. AI output needs verification; it is not a substitute for accountable decisions.

Explore responsible AI planning
Illustrated blue brain with colorful connected nodes representing knowledge, reasoning, and human review

Anthropic Claude Code & Cowork

Claude Code is an agentic coding tool. Cowork supports delegated tasks such as organizing files and preparing documents. Plan context, access boundaries, and review steps before use.

Claude Code overview · Cowork guide

Microsoft Copilot

Explore AI-assisted work with the appropriate Copilot product for your environment. For Microsoft 365 Copilot, agent instructions and knowledge sources can be configured in Agent Builder; availability and controls depend on your plan.

Microsoft agent documentation

Google Gemini

Gemini Gems let you save instructions for a specific task or role. We can help design reusable instructions and evaluation examples, while distinguishing a custom Gem from an independently operating agent.

Google custom Gem guide

These are third-party product references, not claims of partnership, certification, included subscriptions, or guaranteed results.

LLMs & retrieval-augmented generation

Large language models generate responses from context. RAG retrieves relevant material to inform those responses. Plan source permissions, citations, and evaluation; retrieval does not eliminate errors.

Read the RAG overview

Agent orchestration

Coordinate bounded tasks, tools, and handoffs with explicit permissions, logs, stop conditions, and human approval for consequential actions. Start small rather than assume autonomy.

Plan an AI workflow

Consent-based multi-touch attribution

Compare how permitted marketing touchpoints receive credit for an outcome. Respect consent and opt-outs, minimize collected data, and document model assumptions; attribution is not proof of causation.

Explore measurement planning

A practical engagement

Understand. Build. Review. Improve.

A focused starting point, clear ownership, and evidence before expansion.

  1. Understand the problem

    Agree the desired outcome, current systems, data boundaries, and a manageable first scope.

  2. Build a small working example

    Connect the needed information and test a workflow with approved or synthetic examples.

  3. Review the evidence

    Check output quality, access, exceptions, and recovery options with the people who own the work.

  4. Hand over and improve

    Document decisions, train the team, and agree support and the next iteration.

About 70 seconds · A sample scenario told as Situation, Task, Action and Result, with English voice-over, captions, and transcript. Use the controls to play; reduced-motion changes pause playback.

Read the video transcript (English)
  1. Situation: A regional retailer has sales data in three systems, finance spends days reconciling spreadsheets, and leaders see different numbers.
  2. Task: Agree one trusted view of sales, including the outcome, systems, data boundaries, and checks every number must pass.
  3. Action: Connect the sources, map and validate each field with approved or synthetic data, and have people review quality, access, and exceptions.
  4. Result: In this illustrative example, finance works from one shared set of numbers with a documented handoff. It is a sample scenario, not a client result or guarantee.

Start with your priority

Choose a practical next step.

Read the guidance, then discuss a scope that fits your organization.

Connect Your Data

Compare batch, change data capture, streaming, and API integration patterns.

Plan a consulting engagement
Project-based learning

Build skills by building something real.

Students work through guided projects, track progress, receive feedback, and preserve artifacts for their portfolios.

Discover education