Custom AI tool and experience development

Reinvent the experience and workflow first.

Don’t just automate the way things work today.

Then build the Human-AI system that makes it real.

What it’s about

A focused, hands-on program that delivers a custom AI tools or experience — but gets there by reinventing the underlying workflow or customer experience before you write a single line of production code.

Rather than “automate what you already do,” we invent the new experience, surface the right Human-AI collaboration patterns, define a data & systems strategy, and then iterate from prototypes to a pilot that proves not only model performance but real human-AI capability augmentation.

  • Invent better experiences and tools that actually change behavior and patterns of work — not just faster versions of legacy products and processes.

  • Deliver working proof-of-concepts that are tested with real users and real data.

  • Measure Human-AI collaboration and capability augmentation (trust, decision quality, speed, error recovery) — not just model outputs.

  • Produce pilot systems with integration, instrumentation and a clear scaling path.

  • Reduce technical and organizational risk with a defined data strategy, vendor/build tradeoffs and a repeatable roadmap.

  • Train practitioners and owners on their new workflow — not just the tool — so the new experience can be run and improved by your teams.

How it works

Stage 1:

Experience invention and technical discovery

Start by inventing the user or employee experience and Human-AI roles (who does what, when, and why). In paralllel we run a technical discovery and data strategy effort: source mapping, quality assessment, privacy & compliance constraints, integration touchpoints, and an initial architecture for iteration.

These engagements are highly collaborative and usually run 8–12+ weeks to produce a robust POC and pilot. Full pilot delivery and scaling planning commonly fall into a 12–24 week engagement depending on scope, integrations and regulatory needs. We blend design sprints, rapid engineering cycles, real-world testing and executive checkpoints so progress is visible and fundable.

Stage 2:

Iterative build and POC prototyping

With the experience defined, we move quickly to functional prototyping, agent building, sandbox integrations and incremental model experiments. Prototypes are evaluated in shadow deployments with real users — with each iteration testing assumptions about the interaction model, handoffs, error states and UI affordances — feeding learning back into product and workflow design.

Stage 3:

Functional pilot and workflow testing

We launch a functional pilot of the experience and workflow, and use it to measure Human-AI collaboration, task outcomes and decision confidence — not just tool or model metrics. This unique approach identifies where AI amplifies human capability (and where it erodes it), and produces the evidence leaders need to decide what to scale.

Stage 4:

Iterative deployment and scaling

We translate pilot learnings into an operational plan for safe, measurable scale: production architecture, MLOps & model governance, data pipelines, monitoring & drift detection, operational playbooks, training & change plans, and phased rollout schedules. We also advise on build vs. buy tradeoffs, vendor management and IP strategy so the scaled product is robust and defensible.

What gets measured differently

Beyond classic product and model KPIs, we track metrics that demonstrate real capability augmentation: human decision quality uplift, time-to-decision, trust and calibration, error detection & recovery, collaborative throughput, and business outcomes attributable to the Human–AI partnership.

Our tests combine quantitative telemetry, structured user observation, A/B experiments, and scenario stress tests to surface failure modes early and design safer, higher-value experiences.

Typical outputs & outcomes

  • Experience blueprint and Human-AI interaction patterns

  • Technical discovery & data strategy (sources, schemas, quality plan)

  • Working prototypes and iterated model artifacts

  • Production-like pilot with instrumentation and Human-AI capability report

  • Full production & scaling plan (architecture, MLOps, governance, training)

  • Playbooks, runbooks and a trained practitioner cohort ready to operate and improve the experience and workflows

Example products and tools

Vanguard Digital Advisor

The world’s lowest cost AI-powered financial advice service — offering complex portfolio and planning guidance for non-expert investors.

Walmart ‘Trend to Product’

Possibly the world’s leading AI-powered product design tool — delivering products to market with significant improvements in time-to-market and quality.

Walmart Everyday Health Signals

An AI-powered platform that analyzes customers’ shopping history to deliver personalized nutrition and wellness guidance based on their retail purchases.

Get in touch.

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