Studios and publishers hold more player and production data than they can use

Live operations, cross-platform data, studio knowledge and player support all depend on information spread across teams, titles and tools. We help games businesses join it up and bring AI into development and product operations where it genuinely helps.

Start a conversation The problems we solve

The problems

Where games businesses lose time and insight

  • Live ops decisions on partial data

    Events, offers and balance changes are decided before the data has caught up.

  • Player insight split by platform

    Each platform, store and title reports players differently.

  • Cross-platform data

    Joining player behaviour across PC, console and mobile is a project in itself.

  • Studio knowledge in people’s heads

    How the build works, why a system was designed that way and what broke last time.

  • Publishing and development models

    Publishers and studios need different views of the same data and the same release.

  • Product analytics

    Telemetry is collected but not turned into design and commercial decisions.

  • Player support knowledge

    Support teams search patch notes, known issues and past tickets for each answer.

  • QA and testing

    Test knowledge, bug history and reproduction steps are hard to reuse across titles.

  • Release workflows

    Certification, patching and release checklists depend on a few people.

  • AI adoption in development

    Teams experiment with AI tools without shared guidance on what is safe and useful.

Forcing events

When it usually comes to a head

  • A live-service launch or a new platform
  • Player support volumes rising after a release
  • A publishing deal or acquisition joining studios
  • Pressure on development budgets
  • Teams adopting AI tools faster than the studio has policy for

What you already hold

The knowledge and data involved

  • Game telemetry and player behaviour data
  • Platform and store data
  • Patch notes, known issues and support tickets
  • Design documents and technical documentation
  • Bug databases and QA records
  • Build and release pipelines

Workflows

Workflows we change

Specific pieces of recurring work, each with an owner, a baseline and a measure of what changed.

  1. Live ops analysis

    Event and offer decisions informed by joined-up player data.

  2. Player support answers

    Support answers from patch notes, known issues and resolved tickets, cited.

  3. Studio knowledge

    Build, pipeline and design knowledge answerable for the team.

  4. QA and bug history

    Previous bugs, fixes and reproduction steps found in seconds.

  5. Release readiness

    Release and certification checklists backed by the studio’s own history.

Where AI earns its place

AI opportunities

  • Player support from the studio’s own knowledge

    Consistent, cited answers that reflect the current patch.

  • Studio knowledge on hand

    Engineers, designers and QA find what the studio already knows.

  • Analytics people use

    Player and product questions answered from curated data.

  • Guided AI adoption in development

    Shared guidance and controlled tools for the teams already experimenting.

What usually sits underneath

When AI disappoints, the cause is usually the data, systems and ownership beneath it, so we look there too.

  • Telemetry schemas that change from title to title
  • Platform data held in separate stores
  • Knowledge in wikis, chat and tickets with no single home
  • Analytics pipelines built quickly for a launch and never revisited

Broader technology and data work

  • Player data platforms and analytics
  • Architecture review for live-service platforms
  • Technology due diligence on a studio acquisition
  • Senior data and technology leadership

What we could build or change

Examples of the work

Illustrations of what an engagement could produce, scoped to your own information and measured against a baseline.

  • A player support assistant grounded in patch notes and resolved tickets
  • A cross-platform player data model
  • A studio knowledge assistant over design and technical documentation
  • A QA knowledge base that finds previous bugs and fixes

Answerable

Answerable Assist and Systems

Assist is set up for support teams: knowledge base, tickets, reviews and policies. Systems covers technical documentation, runbooks and incident logs for engineers. Both are shown on real screens from demonstration environments.

See every configuration

Answerable Assist: CX & Customer Support

Set up for customer service: knowledge base, support tickets, product reviews, sizing guides and return policies.

Answerable Assist screen with an answer on exchanging an incorrectly sized item, an exchange table and its cited guide (opens the screen full size in a new tab)
Answerable Assist answering how to handle an exchange for an incorrect size, with the steps and the guide behind them. Real product screens from demonstration environments. Organisation and author names in them are fictional, and the figures show how the product answers, not findings.

Proof

What we have built and done

Each item says where it comes from: TechGuidr’s own products, the founder’s earlier roles or demonstration configurations.

  • Founder’s earlier role

    Studio analytics in the early days of Azure

    At Microsoft, Dan centralised analytics for AAA games studios on a young Azure, and dogfooded the internal tooling that later became Power BI. This is experience from an earlier role, not TechGuidr client work.

    See earlier work
  • Demonstration configuration

    Answerable Assist and Systems

    Configurations for support answers and engineering knowledge, shown on illustrative content.

    See the configurations

How we engage

Where to start

Every engagement starts from the decision or outcome you need. Prices are starting points and exclude VAT.

Industries

Related industries

All industries

Bring the player, studio or support problem

We will tell you what sits underneath it and where data or AI would genuinely help.

Start a conversation See the AI Work Review