AI development & product design · Richmond, VA

Built to leave theprototype behind.

AI Property Solutions designs and engineers useful AI products, from the first hard question to the systems that keep working after launch.

LLM ApplicationsAI AgentsComputer VisionProduct DesignData EngineeringMLOpsVoice InterfacesSearch & Retrieval

A product studio, end to end

The model is one part of the system.

Useful AI work connects a real decision to the right context, interface, data, safeguards, and operating model. If those pieces are designed separately, the gaps become the product.

We keep product judgment next to the engineering so a promising idea can become a system people understand—and an existing system can become more reliable without being rebuilt for theater.

How we work

Two starting points

New idea or existing system. Same standard.

The work begins where the uncertainty is—not where a standard agency process says it should begin.

01

Starting something new

Turn an opportunity into a product direction, prove the hard assumptions, and build the smallest complete system that can earn a wider investment.

  • Opportunity and workflow definition
  • Feasibility, data, and model risk
  • Prototype through production delivery
02

Strengthening what already exists

Find the product, architecture, evaluation, or operating gap that is holding back a working system, then take ownership of the highest-leverage repair.

  • Product and technical audit
  • Workflow, interface, and reliability repair
  • Release and operating-system hardening

01 / What we do

One team, from question to operating system.

Strategy, product design, and engineering stay in the same room. The result is a system people can understand, use, and keep improving.

01

AI Strategy & Discovery

Focused discovery to identify where AI creates leverage, test the hard assumptions, and shape a buildable roadmap.

  • Audits
  • Roadmaps
  • Feasibility
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02

LLM Applications & Agents

Assistants, copilots, and generation tools built around the right model, retrieval system, and evaluation plan.

  • RAG
  • Evals
  • Guardrails
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03

Product & Interface Design

Clear product experiences that make model behavior legible, controllable, and useful to the people doing the work.

  • UX research
  • Design systems
  • Prototypes
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04

Data, ML & Production Delivery

The pipelines, catalog systems, vector stores, and model-facing infrastructure that turn prototypes into products.

  • Pipelines
  • MLOps
  • Observability
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Evidence, not theater

Confidence should match what was proven.

A passing build, a deployed route, a configured provider, and a real customer outcome are different facts. Our case studies say what is working and preserve the boundary around what still needs outside approval or real-world acceptance.

Review the case studies
10
Published product case studies
04
Connected service disciplines
Web + mobile
Product surfaces across customer and operating workflows
Explicit gates
Code, deployment, provider, and real-world proof kept distinct

More proof, outside the case-study index

Three other products. Three different jobs.

These are separate from the ten formal case studies: additional product work that shows range without duplicating the work page.

Real Rotation mobile product screens showing the dating journal home, roster, and text decoder experience.

Dating journal · Mobile product

Rotation

A private dating journal that turns rosters, moments, text decoding, and personal patterns into one voice-led mobile system.

  • Mobile UX
  • AI Decoder
  • Privacy
Evidence boundary: Current product and interface work is represented here; public store release remains a separate gate.
Real Vice of Verse mobile screens showing the lyric feed, song discovery, and community publishing experience.

Music community · Web + mobile

Vice of Verse

A lyric-interpretation community spanning discovery, song rooms, posting, profiles, moderation, and owner operations.

  • Community UX
  • Moderation
  • Release Systems
Evidence boundary: The implemented product surfaces and release preparation are shown without presenting provider or store approval as complete.
Real Until mobile interface showing its shared matches and asynchronous games experience.

Relationships · Experiential mobile

Until

A long-distance relationship product built around two cities, one countdown, shared moments, asynchronous play, and the complete reunion cycle.

  • Product Systems
  • Motion
  • iOS + Android
Evidence boundary: The production-quality front end and complete mocked narrative are proven; backend services and live subscriptions are not claimed.

02 / How we work

Make the risk visible. Then make the product real.

Weekly working demonstrations keep decisions concrete. Acceptance gates keep confidence proportional to evidence.

01

Discover

Understand the people, systems, data, and hard constraints before committing to a direction.

1–2 weeks
02

Prototype

Put a focused working slice in front of real users and test the assumptions that matter.

2–4 weeks
03

Build

Design and engineering move together through weekly demonstrations and measurable acceptance gates.

6–12 weeks
04

Operate

Launch deliberately, observe the system, improve it, and make ownership clear.

Ongoing or handoff

How an engagement can start

Begin with the ownership the problem needs.

Scope follows the decision or operating problem. Pricing stays private until the actual constraints and responsibility are clear.

03 / Questions

The useful answers, including the one about the name.

What does AI Property Solutions actually do?

Despite the name, we are not a real-estate company. AI Property Solutions is an AI development and product design studio. We build AI applications, agents, data systems, and the interfaces that make them useful.

Where are you based?

Richmond, Virginia. We work with clients across the United States through a mix of remote collaboration and focused working sessions.

Can you take a product from idea to production?

Yes. Engagements can cover discovery, product design, application engineering, model integration, deployment, and the operating systems needed after launch.

Do you work with an existing product team?

Yes. We can lead a focused build, extend an existing team, or take ownership of a difficult product and engineering workstream.

Which models and platforms do you use?

We choose models and infrastructure around the product, data requirements, quality targets, cost, and deployment constraints instead of forcing every project onto one stack.

How do we start?

Send a short note about the problem, the people affected, and where the project stands today. We will respond with an honest read on fit and the most useful next step.

04 / Start a conversation

Bring us the problem that keeps surviving the meeting.

Tell us what you are trying to change, who it affects, and where the work stands today. We will give you an honest read on fit and the most useful next step.

adrian.hillman@aipropertysolutions.org(804) 291-8176