AI consulting for game teams that need more than a prototype.

Build

AI-assisted prototyping, agentic coding, design documentation, concept exploration with rights discipline, QA, and internal knowledge systems.

Launch

Market research, store-page messaging, UA creative analysis, community/creator/audience research, soft-launch KPI readouts, publisher/investor/press materials.

Operate

Live-ops planning, post-event retrospectives, offers/events/segmentation, BI copilots, economy/progression monitoring, player-support triage, community intelligence.

Govern

AI roadmap, build-vs-buy, vendor checks, privacy/IP/safety, human review, evaluation criteria, kill/scale decisions.

AI Opportunity Map

Find the highest-value AI opportunities across your studio or business unit and the ones to avoid.

For founders, studio heads, and publishing leads who need a defensible picture of where AI is worth investing in.

Buyer problem: too many possible AI bets, no shared criteria, and no time to evaluate them against real studio constraints.

Format: short, focused engagement combining interviews, workflow review, and a synthesized opportunity register.

• Discovery sessions with leadership and selected operators
• Workflow and stack review across production, live ops, marketing, and BI
• Opportunity inventory scored by impact, feasibility, and risk
• Working session to align on shortlist and sequencing

Deliverables: ranked AI opportunity register, recommended shortlist and sequencing, risks, dependencies, and decision criteria.

AI Readiness & Workflow Audit

Understand where AI fits your actual production, product, live-ops, and operating workflows, not the idealized version on a slide.

For studio operators and product leaders who want to know what their teams could actually adopt.

Buyer problem: existing workflows, tools, data, and team culture are rarely AI-ready in the way vendor pitches assume.

Format: audit engagement focused on real workflows, real data, real tools, and real team capacity.

• Map current workflows in production, live ops, analytics, marketing, or support
• Identify friction, handoffs, and decision bottlenecks
• Assess data, tooling, governance, and team readiness
• Highlight high-leverage AI insertion points with honest caveats

Deliverables: workflow maps, readiness assessment, prioritized recommendations, and known risks.

Live-Ops & Product Intelligence Sprint

Use AI to improve decision loops across events, cohorts, offers, support signals, and KPI movement.

For live game teams and product leaders who want better decision loops without buying yet another platform.

Buyer problem: BI dashboards exist, but the loop from signal to decision is slow, noisy, or owned by no one.

Format: time-boxed sprint focused on a specific decision loop.

• Pick one decision loop and define success criteria
• Review available data, tools, and current rituals
• Prototype AI-supported analysis, summarization, or triage
• Hand off a working method the team can run without me

Deliverables: one improved decision loop, prompts or tooling, and notes on what to expand, drop, or monitor.

Agentic Workflow Prototype Sprint

Build or specify one narrow internal AI workflow with human review and a clear operational handoff.

For teams that want to test one narrow agentic or AI-assisted workflow under real conditions before committing.

Buyer problem: agentic ideas sound powerful on paper and fall apart in real operational settings without scoping and review.

Format: one workflow, one team, one success criterion, one decision at the end.

• Pick a single workflow and define inputs, outputs, and review gates
• Build or specify the prototype with appropriate tools
• Run it against real inputs with human-in-the-loop review
• Evaluate quality, risk, and operational handoff

Deliverables: working or specified prototype, evaluation notes, and a recommendation to build, buy, pilot, stop, or supervise.

Executive AI Briefing

Align leadership on what AI can usefully do in games now and what to avoid with a senior, opinionated working session.

For leadership groups, boards, and exec teams that need a calibrated view of AI in games without vendor spin.

Buyer problem: most AI briefings are either hype or fear. Neither helps a leadership team make better decisions.

Format: working session for an executive group, customized to the studio or company context.

• Pre-briefing call to align on context and questions
• Tailored session covering what is real, what is premature, and what to avoid in games
• Live Q&A around the group's actual roadmap and constraints
• Follow-up notes and recommended next steps

Deliverables: tailored briefing session, summary notes, recommended decisions to consider, and optional follow-up working session.

Fractional AI x Product Advisor

Ongoing senior advisory for teams making repeated AI and product decisions across roadmap, vendors, and rollout.

For studios and game-tech companies making repeated AI and product decisions over months, not weeks.

Buyer problem: one-off advice is not enough when AI choices accumulate across roadmap, vendors, hiring, and rollout.

Format: ongoing fractional advisory with a regular cadence, focused on real decisions in flight.

• Define cadence, scope, and the decisions the engagement should support
• Recurring working sessions with leadership and selected operators
• Asynchronous reviews of roadmaps, vendor proposals, and rollout plans
• Quarterly recalibration

Deliverables: recurring strategy and review sessions, written recommendations, and ongoing context for fewer dead ends and faster calls.

Do you build AI tools or only advise?

Both, within scope. The work ranges from strategy and audits to specifying or building narrow prototypes: Python/API automation, prompt and process libraries, RAG-style knowledge workflows, and agentic experiments.

Is this only for mobile and free-to-play studios?

Mobile and F2P are home turf, but the work applies to PC, console, cross-platform, publishing, and game-tech teams as well.

Can you help with player-facing AI features?

Yes, at the feasibility, evaluation, and risk-assessment level. Player-facing AI requires careful work around quality, safety, IP, store policies, and live-service stability.

Can you review an AI vendor proposal?

Yes. Independent vendor reviews cover fit, technical claims, lock-in, data and IP exposure, integration cost, and operational reality.

Do you train custom models?

Custom model training is rarely the right first step. Most game-studio AI value comes from workflow design, evaluation, prompt and process libraries, RAG-style knowledge systems, and supervised agentic workflows on top of existing models.

What should we bring to a first call?

A real workflow, product problem, vendor proposal, roadmap question, or AI idea you are seriously considering. Specifics make the call useful.

What is the first step?

Book an AI strategy in gaming call. We use it to understand your context, your real constraints, and the decision you are trying to make.