Relevant Experience

The discipline behind Gridmark.

Gridmark is the public-sector practice of This Waay. The qualifications below are selected commercial engagements, re-framed for public systems work, that demonstrate the research, design, and modernization capability we bring to civic work. As we complete public-sector engagements, we'll add them here as references.

Selected qualifications

Engagements that show how we work.

Each engagement below links to a full case study — the challenge, the evidence, what we built, and why it matters for public systems.

Modernizing the driver experience for paratransit operations

An aging driver application forced transit operators to buy dedicated hardware and retrain every driver. A standards-aligned redesign removed both burdens — and made service more reliable for the riders who depend on it.

Workflow analysis
Experience design
Accessibility
Modernization

Unifying a fragmented multi-portal student journey

Students navigated four portals and two apps to answer basic questions. We defined a validated end-state and a sequenced path toward one experience — no risky replatform required.

Journey mapping
Service design
Modernization planning

Modernizing a mission-critical operations platform without a rebuild

A platform trusted with mission-critical operations had grown in power faster than clarity. Research across three staff roles turned a planned refresh into a phased, evidence-based modernization.

User research
Workflow analysis
Delivery planning

Making a system's most critical capability its most accessible

Safety-critical video evidence was buried across dashboards. Discovery, needs modeling, and a unified design made the system's most important capability its most accessible.

User research
Service design
Delivery leadership

Decision-grade research before a major product investment

When adoption lagged, leadership couldn't tell whether the problem was value or usability. A two-touchpoint mixed-methods study delivered decision-grade answers in weeks.

User research
Survey research
Evidence-based prioritization

Responsible AI in practice

Our AI principles, practiced.

These engagements are our responsible-AI principles applied on real systems: evidence over hype, structure before scale, human needs first. Each one deepened an engagement above — research first, then modernization, then responsible AI.

Structure before scale: rebuilding a workflow so AI could help

AI can't fix a fragmented workflow — it amplifies it. We rebuilt a twelve-screen process into a clear foundation, then introduced AI safely within it.

Responsible AI
Workflow modernization
Service design

Embedding intelligence in a system people already depend on

Interpreting failures depended on experience, not clarity. An AI interpretation layer accelerated decisions while preserving operator control and transparency.

Responsible AI
AI opportunity assessment
Experience design

AI as a structured accelerator within a trusted workflow

A trusted-but-complex configuration workflow slowed staff. AI as a structured accelerator improved speed while keeping every suggestion visible, validated, and confirmed.

Responsible AI
Workflow analysis
Experience design

Our approach to responsible AI →

Across this work, the team has repeatedly separated signal from noise through research · mapped journeys and designed human-centered services · modernized complex workflows without disruptive rebuilds · applied AI responsibly, with structure before scale · set direction with sequenced roadmaps and evidence-based prioritization · led delivery to durable outcomes.

Let's find the signal.

Tell us about the program you're trying to improve. We'll propose a right-sized way to begin.

Start a conversation →