Our Work & capability statements

See what MGV has built.

Explore DNA Health Insights, our interactive prototypes, and the Gateway we use to manage AI-assisted engineering.

Each example identifies what was built, what remains illustrative, and where you can examine the work further.

Built application / Consumer genomics

DNA Health Insights.

Helping people make sense of their genetic information.

Problem. Genetic results can be unfamiliar and difficult to use without clear context.
Design decision. Raw DNA processing stays in the browser, while structured findings and reports can be saved to the signed-in account. This separates personal source data from the information people revisit.
Built. An educational application that brings genetic findings, plain-language explanations, and reports together in one experience.
Checked. The current application source confirms browser-local raw-DNA processing and account-scoped saved findings and report access.
Current result and limit. People can explore results, learn the context, and return to saved information; the application provides education, not diagnosis or treatment.

Created for a family member's business; shown as an MGV work sample, not an independent client endorsement. Account access may be required for application features.

Visit DNA Health Insights See more of the engineering

DNA Health Insights homepage introduction with product navigation and educational purpose — **Understanding your biology.** Explore genetic findings through plain-language explanations and educational reports. Enlarge screenshot

DNA Health Insights Command Center with findings organized along a helix and a color-coded category legend — **From data to understanding.** Genetic findings are paired with plain-language explanations and context, helping people explore their results and prepare informed questions. Select either image to enlarge it.

Saved Performance Suite report introducing movement, recovery, and mental load, followed by a section explaining how five findings relate — **From data to understanding.** Genetic findings are paired with plain-language explanations and context, helping people explore their results and prepare informed questions. Select either image to enlarge it.

Authentic product-interface screenshots. Account names and identifying details are not shown.

Interactive Prototypes

A good prototype helps expose requirements that documents alone don't reveal.

Try a proposed system before committing to the full build. A focused prototype gives your team a practical way to explore everyday tasks, compare options, and discover what needs to change. Hands-on experience helps you choose the right direction for your organization. These demonstrations are separate from the DNA Health Insights application.

01 / Local-government prototype

Local government planning.

A clear way to see the plan, collect updates, and understand progress.

MGV built this visual proof of concept for municipal strategic-plan tracking, using Chesapeake's published needs as the example. It lets a visitor move between staff updates, annual review, goal details, reports, and illustrative AI-assisted insights.

Separate review from results.

The design separates an update being reviewed from a goal reaching its target. Human review comes before an update is accepted. Staff contributions, reviewer responsibilities, and the year's progress have distinct places in the interface.

An illustrative browser-based prototype with hypothetical records—not a City deployment. The AI and Microsoft examples are demonstrations, not live integrations.

Try the prototype Read the illustrated guide

The current Chesapeake prototype homepage prominently identifies Mastermind Group Ventures LLC as its creator — **Try it. Shape it.** Walk through a proposed system from start to finish, see what fits your team's needs, and refine the plan before investing in full development.

The current yearly goal view separates reviewed updates, targets met, and items needing attention — **Follow the year's progress.** Distinct cards show reviewed updates, targets met, and information that needs attention. Enlarge screenshot

02 / Community-assessment prototype

See the needs.
Check the information.

A community-needs assessment concept for people planning local services.

This MGV discussion prototype lets staff review sources, examine community findings, and prepare reports while keeping missing information and final approval visible.

An independently developed discussion concept using hypothetical information, not commissioned or adopted Shine Early Learning software. Data connections and access controls are illustrative.

Explore the community prototype

Community-needs assessment interface with information sources, findings, report previews, and final review — **Information people can check.** The overview connects community findings to their sources and the review still needed. Enlarge screenshot

03 / Retail chatbot prototype

Stride shopping assistant.

Try the conversation before committing to an integration.

MGV built an interactive assistant inside an original retail concept. Visitors can choose a suggested question or type their own, see a response, and explore how help could fit into the shopping experience.

An original proposal prototype with scripted responses and a generic brand. No live AI, customer accounts, retailer affiliation, or inventory connection is represented.

Try the shopping assistant

Stride retail interface with an open shopping-assistant panel, suggested questions, and a message field — **A working conversation flow.** The assistant opens within the retail interface, with sample questions and a free-form message field. Enlarge screenshot

The engineering behind the work

Governed AI Dispatch Gateway.

A defined assignment. Limits on what an agent may change. A result people can inspect.

Problem. AI-assisted delivery needs clear limits, recoverable records, and a distinct release decision.
Design decision. Separate review from limited code changes, and separate technical completion from approval or release. The tradeoff is deliberate: wider work needs additional authority.
Built. MGV's internal Gateway uses LangGraph to guide supported workflow transitions and Git to identify the code version. It sets limits on what an agent may change and records the result.
Checked. One demonstrated coding workflow completed a limited edit, a specifically authorized local commit, acceptance testing, and independent review.
Current result and limit. These workflows have been tested in MGV’s macOS environment. Use in a customer environment requires separate integration and testing.

A person decides whether the work is ready to release. The Gateway is not presented as a client deployment, certification, or ready-to-install enterprise product.

Explore the engineering