Technology · Proof of work

You've run the pilots. We automate the work in production

Tested and documented · Model-agnostic · Runs on-prem

The demo worked. The production didn't. Most AI pilots die in the gap between a playground and a workflow your team can trust and run every day.

We start from the work. We map a live workflow step by step, connect it to the systems your team already uses and automate it end to end. Code and rules run the routine steps, and agents take the ones that need judgment. It ships as a tested platform inside your infrastructure and keeps running after we leave.

Most AI agencies stop at advice or a demo. Below are six systems we shipped, and the engineering standards we hold them to. They run on whichever model your procurement team approved, on your own keys behind your firewall, and compliance tests prove nothing leaks.

The argument

Automation = Harness + Workflow

A model is a gifted hire on day one. It has no tools and no memory, nobody has told it the rules, and nobody manages it. Left in a chat window, that's all it ever is.

  • The harness is how you know the model did its job: a gate before anything ships, an eval against last week's version, a source on every number.
  • The workflow is the job you give it: the steps, the systems it reads and writes, and who signs off.

Every stalled pilot we've been brought into failed the same way. The model was fine. Nothing gated bad output before it shipped, nobody had an eval to tell whether this week's system beat last week's, and nobody could explain why it did what it did. None of that is a model problem.

Building the workflow also shows which steps never needed a model. At one studio a language model was estimating player strength, and it was off by 30 to 100 times. The fix wasn't a better model. That step now reads measured player data. Most steps end up like that, as plain code and rules. Agents keep the steps that need judgment, and an agent stays in only if it beats real past results.

Then someone has to use it. A workflow your team routes around is a demo with a longer invoice, so we scope adoption like a feature: champions, training, usage targets and runbooks.

The standards

What enterprise-grade actually means

I

Workflow and harness

What you build around the work so you can depend on it.

  • Every workflow mapped step by step: routine steps as code, agents only where a step needs judgment
  • Deterministic gates run before anything ships or spends, with coverage reporting on what was checked
  • Every number carries provenance: sourced, estimated, modeled, or derived. A surface that can't say where its numbers came from isn't done
  • Evals and backtests run against shipped history
  • Every run leaves a trace: what each step did, what it cost, why
II

Model-agnostic, on-prem

It runs on your infrastructure and your keys, with whatever model you pick. You're not tied to a vendor.

  • Bring-your-own-token: everything runs on your API keys, inside your VPC or data center, with smoke tests proving tokens never leak
  • Tiered model routing (strong, standard, cheap) decided by evals. Copy generation never pays frontier prices
  • Swapping models is one environment variable
  • Anthropic, OpenAI, open-weight. The architecture doesn't care
III

Built to be owned

Automation your team runs after we leave.

  • Workflows live in versioned YAML and go through code review
  • Multi-tenant from day one: config delivery, segmentation, and experimentation are platform features
  • Hundreds of automated tests per system, enforced in CI
  • We scope adoption like a feature, with training, internal champions, usage targets and runbooks. It only lasts if your team actually uses it
What we've built

Six workflows, automated for live games

Each one is work a studio does every week, automated end to end and wired into its stack. The studios stay unnamed. Every figure below comes from the systems' own code, tests and reports.

01 · Live ops

Live-ops tooling

Live-ops tooling for a top-20 mobile games studio, on a game played by millions. It automates the workflow behind events, sales, patch notes and post-launch readouts. The design layer decides what to ship. It simulates the game's economy, calibrates difficulty and values each event on net lifetime value. The ship layer versions the config, targets player segments and runs the A/B test. Governance gates sit between the two, and the results of each event go into planning the next one.

It optimizes for net lifetime value, meaning retention plus spend players can sustain, and never for one event's revenue. The gates reject any design that buys a revenue spike by burning through the player base.

Their creative team spends its quarters on design instead of scheduling.

It runs on their keys in their own stack, and their team operates it without us. Reference on request.

490automated tests, and an adversarial review whose confirmed critical and high findings were all fixed
  1. Design
  2. Approve
  3. Version
  4. Promote
  5. Target
  6. Measure
  • An event designer on about 20 deterministic skills in four groups: authoring, simulation, monetization and governance
  • Simulation runs against 25 player baselines, five stages of the game by five spend tiers, from free players to top-spending whales. Every surface uses the same player population
  • Five event archetypes share one simulation spine. Adding a surface is one function
  • Backtested against shipped events and real history, with per-field provenance on every number. Its value model ranks events in close to the order their revenue lift came in, a rank correlation of 0.77
  • Governance gates for standing rules, economy balance and design review, through a propose, predict, gate, approve and apply control plane. Economy gates report their coverage: what ran, and what lacked inputs to run
  • A multi-tenant ship layer: config bundles that draft, stage, promote, and roll back or kill at any time, with per-player segment targeting and sticky A/B variants
  • Workflows in versioned YAML for event design, launch, operations, postmortems and patch notes
  • Replaced a language model's guess at player strength, which was off by 30 to 100 times, with measured player data
  • Wired into the studio's stack: Notion, Slack, Firebase Remote Config, BigQuery. Bring-your-own-token with a compliance smoke test, and tiered model routing across OpenAI and Anthropic
02 · Production hubs

Studio operating hub

The app a live game's production team runs its week from. It holds the roadmap, three backlogs that publish straight to the code repository, research questions, design review, the events console and the event designer, next to the acquisition, retention and monetization views.

23views in one hub, from the roadmap to economy health
  1. Roadmap
  2. Backlog
  3. Review
  4. Events
  5. Readout
  • 23 views in four groups: overview, acquisition, retention and monetization
  • The roadmap as a ledger, a dependency graph or a timeline
  • An analysis index catalogs every warehouse query with its result, so a question gets answered once
  • Freshness gates on the data views, so a stale number shows as stale
  • For a second studio, a client-facing operating view: the roadmap, a spec library, an issue register and the decisions waiting on the studio
  • For a third, a team workspace with a figure register, graded findings and an AI terminal that answers questions over the whole document set. 40 build checks, 37 of them on every build
03 · BI and analytics

Warehouse to decision

Pipelines from a game's raw events to the dashboard its team reads every morning, plus the diagnosis that tells them what to do when a number moves.

81 of 131A/B tests filed as no result in which a metric had moved
  1. Events
  2. Warehouse
  3. Checks
  4. Dashboard
  5. Decide
  • A daily pull from the event warehouse through Looker to a 17-view operating dashboard, refreshed every morning since July 2026. Row-cap and freshness checks run before anything publishes
  • Re-read 158 finished A/B tests straight from the experiment console's own results. In 81 of the 131 filed as no result, a metric had moved
  • Traced a 6.3x collapse in a game's first-purchase conversion to a single release week, and showed acquisition mix explained only 13 to 15% of it
  • Traced a revenue drop to one app build running at about 1% of the previous build's purchase conversion, and showed a third of the loss was still live after the partial fix
  • 12,909 figures read off a studio's dashboards and design documents, each traced to its source
04 · Growth and UA · Marketing OS

Paid growth loop

We built Marketing OS as our own growth platform for mobile-game studios. Its paid side runs the loop from soft launch to scale: plan the launch, buy across the major ad networks, pace the spend, attribute the installs and measure what the spend caused.

6ad networks from one flow
  1. Plan
  2. Buy
  3. Pace
  4. Attribute
  5. Measure
  • Campaigns on Meta, TikTok, Google, Unity, AppLovin and ironSource from one flow, with spend pacing and an automatic pause
  • Soft-launch plans with KPI gates
  • Mobile attribution built for the post-ATT world, alongside the major MMPs
  • Lift tests and media-mix modeling for incrementality
  • Retention, creator and community modules on the same platform
05 · Content creation · Marketing OS

Content pipeline

The organic side of the same platform. Posts, images, video and playable ads from a brief, graded against a rubric and approved by a person before they publish.

15publishing destinations
  1. Brief
  2. Generate
  3. Grade
  4. Approve
  5. Publish
  • Brand, audience and platform knowledge loaded from a skill graph for each task
  • Drafts graded against a rubric and rewritten with the grader's notes
  • A person approves before anything from the pipeline publishes
  • Image, video, carousel and playable-ad generation
  • Publishing to 15 destinations, from X, LinkedIn and Instagram to Discord, Reddit and both app stores
06 · Review · Alexandria

Specialist review

Some steps are all judgment, like the reviews a studio would otherwise send out. That's where Alexandria's specialist agents come in. Load-bearing claims carry their source, and separate agents check that the source says it.

87%citation precision on 240 sampled claims
  1. Question
  2. Agents
  3. Verify
  4. Cite
  5. Report
  • An 11-agent game teardown in eleven phases with a fact-check gate: 87% citation precision on 240 sampled claims
  • Five agents read 76.6 minutes of gameplay recordings, about 900 frames, and tagged each moment against the game's analytics events
  • Three adversarial verifiers on each load-bearing claim of a monetization finding: 24 agents
  • A redesigned first session reviewed through twelve discipline lenses
Next step

Watch the systems run

Thirty minutes with Patrick. We run the systems live in front of you, then tell you straight which of your workflows are worth automating, if any.

or email: patrick@higheragency.solutions

Further reading