We run our own AI products, and we build custom systems for other companies. Either way the starting point is usually the same: more data than anyone has time to read, and a decision riding on it.
Different industries need different engines, but the work underneath doesn’t change much. Get the data right first, then use AI where judgment actually helps.
A command center for brands that sell and advertise on Amazon. Search performance, real profit after fees, PPC, inventory and the Buy Box all sit in one view that stays current, so you can see what needs attention without digging through five reports first. Built on the official Selling Partner and Ads APIs.
Visit stackcore.appAgents that sit inside the workflow instead of next to it. They watch the data, draft the change, and wait for a person when the call isn’t theirs to make.
AI for game development, from production through live service. Game Chain plugs into the studio pipeline: it generates and iterates content, gives NPCs behavior that reacts instead of following a script, runs automated playtests so QA is not tied up with the routine passes, and keeps an eye on the economy once players are in.
Talk to us about Game ChainDemand, spend and price models trained on your own history, not on the category average. Your catalog stopped behaving like the average a long time ago. What you get are forecasts and alerts your team can plan around.
Pipelines that pull from scattered, inconsistent sources and give back one joined dataset you can actually query. AI does the matching, extraction and enrichment, because hand-written rules never keep up with messy data.
When none of our products fit, we build it. Every build is different; the groundwork rarely is. Ingestion, a store that keeps history, a reasoning layer, and an interface your team will actually use. You own what we build.
A bid optimizer and an NPC behavior system share almost no code. What they share is everything around the model, and that’s usually what decides whether either one is still running a year later.
Official APIs, engine telemetry, streams and feeds, with backfill, retries and rate limits handled properly. An outage upstream shouldn’t leave a hole in your history.
The same product, player or account shows up under five different names. We resolve it to one entity before anything downstream has to make sense of it.
Metrics and features are versioned over time, so a model can answer what changed and when. The first time you have to reconstruct what you knew last quarter, you’ll be glad it works this way.
We pick the model for the task and keep it swappable. Where a plain rule does the job, we write the rule. AI goes where judgment actually helps.
Every model-backed feature ships with an eval set and a fallback path. When quality slips, it fails over to something predictable instead of failing silently.
Least-privilege credentials, encryption in transit and at rest, scoped authorizations you can revoke, and an audit trail covering anything an agent does.
Three stages, and you see something running on your own data inside the first six weeks.
We start from the decision you’re trying to get right, not from a list of features. That keeps the scope small and cuts the work that was never going to change anything.
Week oneA working system on your own data inside the first six weeks, deliberately narrow. You use it, we see where it gets things wrong, and the roadmap comes from that.
Weeks two to sixWe run what we build: monitoring, evals, upstream API changes, new sources when they matter. You get the improvements without carrying the maintenance.
OngoingThe demo is the easy part. The hard part is the year after, when it has to keep working while the data shifts underneath it.
We build applied AI systems in a few areas. Two of them are our own products: StackCore, for brands that sell and advertise on Amazon, and Game Chain, for game development. The rest is data aggregation, forecasting, agent systems and custom builds for other companies. The common thread is messy, high-volume data that has to turn into a decision or a working feature.
It covers the life of a title. During production it generates and iterates content (assets, levels, dialogue and narrative) and gives NPCs and companions behavior that holds up in real play. Before launch it runs automated playtests and catches broken builds and difficulty spikes. Once players are in, it watches retention, the in-game economy and balance. Studios take the parts they need; it isn’t all or nothing.
Both. The products are where we prove the work on our own data and our own money, and custom work points the same engineering at whatever is specific to your business. If something we already run would solve your problem, we’ll tell you that and save you the money.
Fluxify is owned and operated by QBIT Commerce Inc., a Canadian company based in Toronto. The team that builds the products also handles the engagements, so there’s no account layer between you and the people writing the code.
Official platform APIs, under an authorization you grant and can revoke whenever you like. Engine and game telemetry you connect. Licensed market data and public sources, plus your own systems where you hook them up. We don’t resell your data, and nothing you give us is used to train a model that anyone else can use.
Whichever one fits the task, and we keep it swappable so a better model can drop in later. Most systems mix a frontier model for the judgment-heavy steps with smaller models and ordinary deterministic code for everything else. That keeps the cost reasonable and the behavior predictable.
Data is encrypted in transit with TLS 1.2+ and at rest. Access is limited to the people who operate the service. Credentials are least-privilege and scoped to what a feature actually needs, and any third-party authorization can be revoked at the source whenever you want. The detail is in our Privacy Policy.
It starts with a short call about the decision you’re trying to get right, or the thing you want built. Pricing depends on data volume, how many sources are involved, and whether you want us running the system afterwards. We quote once the scope is real; a generic price list wouldn’t fit this work.
Tell us what you’re trying to decide or ship, and what you have to work with. We’ll tell you whether AI is the right tool for it, what it would take and what it would cost. If it isn’t, we’ll say that too.
Every message is read by the people who build the software. You’ll hear back from us, usually within one business day.