Adaptive Cloud Gaming PC

For

PC gamers who upgrade hardware every two years

Solves

Gamers buy expensive video cards that age quickly and sit idle most of the day.

Delivers

Continuously optimised cloud gaming session with per-scene graphics tuning and predictive adjustments

Built in

about 3 weeks of creation time, MVP in 4 days

Investment

$7,000 for the MVP, $26,000 for the full product

Run it

Inside your business, or as part of your offer to clients

For PC gamers who upgrade hardware every two years, turn gameplay telemetry, scene data and user preferences into a continuously optimised cloud gaming session with per-scene graphics tuning and predictive adjustments.

1. Build a player profile from the first hour of play.
2. Adjust graphics settings per scene in real time.
3. Predict next moves and pre-load assets.
4. Generate upscaled detail for lower-end hardware.
5. Auto-assign newer GPU architecture for new titles.
6. Voice-command settings changes without menus.

What goes in, what comes out

The workflow

1. In
   Start with

   Gameplay telemetry, scene data, user preferences, hardware specifications

2. 1

   Install thin client

3. 2

   Play an initial profiling session

4. 3

   Review the generated profile

5. 4

   Accept or adjust auto-tuning rules

6. 5

   Start a game session

7. 6

   Monitor live adjustments

8. 7

   Review performance summaries

9. Out
   Finish with

   Continuously optimised cloud gaming session with per-scene graphics tuning and predictive adjustments

AI does the heavy lifting, people stay in charge

Use reinforcement learning to tune graphics settings per scene and computer vision to analyse on-screen content. Predict player actions from input patterns. A human reviewer checks profile accuracy and tuning behaviour before any settings are applied automatically.

What your team sees

Key screens: Session dashboard, graphics tuning panel, performance overlay. Use a central gameplay view with a side panel showing live settings adjustments, frame rate and latency. Let users toggle auto-tuning on or off and view a history of changes. Display predicted next actions and suggested voice prompts. In this product, the first view is session dashboard, followed by graphics tuning panel and performance overlay.

Accounts and administration

User accounts, session history, tuning profiles, permission levels for manual overrides, versioned settings, audit trail of all adjustments and a record of hardware assignments.

Integrations and data access

Game launchers, graphics APIs, streaming codecs, and user authentication systems.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   4 days

   One buyer segment, one recurring use case; first modules: basic auto-preset selection and a performance overlay. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   5 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   8 days

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will PC gamers who upgrade hardware every two years use it to solve "gamers buy expensive video cards that age quickly and sit idle most of the day"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. A paid pilot with 50 sim racers over 30 days proves value by comparing average frame rate variance and session length against their previous hardware.
4. Measure, then decide. Track average frame rate variance per session and percentage of testers who renew after the pilot. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. One buyer: sim racers. One use case: consistent frame rates in racing titles. First two modules: basic auto-preset selection and a performance overlay. Manual review of tuning decisions before they go live.

After the MVP. After the paid pilot, automate per-scene tuning, add predictive asset loading, and introduce voice commands for settings changes.

What the build depends on. Low-latency streaming infrastructure, GPU cloud capacity, and reliable inference for scene analysis.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test pricing at $29 per month for 1080p, $49 for 1440p, and $79 for 4K at 120fps.

Message to test

Play on a top-tier GPU that learns you, never ages, and costs less than a new card every two years.

Where to find buyers

Discord gaming communities, sim racing forums, Twitch streams, and PC gaming subreddits.

Lead magnet

A free one-hour trial session with a performance comparison against your current hardware.

The first 30 days

1. Week 1: Set up cloud VM with single GPU tier and thin client.
2. Week 2: Build basic auto-preset agent based on detected hardware and title.
3. Week 3: Recruit 50 beta testers from Discord and ship the client.
4. Week 4: Collect feedback, fix critical issues, and measure frame rate consistency.

Paid pilot

A paid pilot with 50 sim racers over 30 days proves value by comparing average frame rate variance and session length against their previous hardware. The decision to proceed is based on at least 70% of testers reporting a local feel and renewing for a second month.

Success metrics

Average frame rate variance per session and percentage of testers who renew after the pilot.

Retention and expansion

The subscription continues monthly, with the AI improving tuning accuracy and adding new game support, so users stay because the service keeps getting better without extra cost.

Why clients would pick it

Player profiles and tuning models improve with usage, making the service more accurate and personalised over time, while the cloud infrastructure scales with demand.

Alternatives and positioning

People buy new GPUs or use static cloud gaming services; this differs by adapting to individual play styles and scene content rather than offering fixed settings.

Main delivery costs

Cloud GPU rental, streaming bandwidth, model training and inference, and support staff.

Limits on data collection to gameplay telemetry only, permission for manual overrides, no sharing of personal data, and a safety check that prevents the AI from making changes that could cause motion sickness or discomfort.