AI inside real systems. Not on top of them.
A lot of “AI integrations” are demo code wrapped around an API. We build the layer underneath — the data flow, the retrieval, the validation, the part that has to stay correct under real load on real customer data.
Now operated through the AI layer we shipped.
Using the pipeline in production today.
Running, monitored, reconciled. That's the bar.
The layers underneath.
Production data prepared for retrieval per customer, on the actual shape of the business. Edge cases and inconsistencies handled in code, not pushed onto the LLM.
Retrieval architectures shaped around the data model — not the shape of a tutorial. Vectorization tuned per data class.
The system doesn't pay for the same thought twice. Cold-start latency stays predictable under load.
Used where data, latency, or cost makes API-only impractical. Self-hosted where it makes the system work, not for ideology.
Outputs that touch numbers — finance, stock, operations — are verified against the underlying data programmatically before they leave the system.
Reconciled against the source of truth before anything reaches a customer. The LLM proposes; the system checks.
Managers ask, the system produces — on real, validated data, per customer, in production. Formatted, reconciled, ready to act on.
We architected and shipped the AI layer inside an ERP serving 70+ companies. $5M+/week of their operations runs through it.
We didn't build the ERP itself. The team that did brought us in because their customers needed AI that could actually run on real production data — and they trusted us to ship it end-to-end.
The system generates custom reports through chat — per company, on real data, in production. The outputs aren't summaries of summaries. They're computed, validated, and reconciled against the source of truth before they reach anyone.
It's been running. Stable. That's the bar.
The bar that keeps us out of certain projects.
- ×Bolt-on chatbots whose only job is to look modern.
- ×AI features with no production path.
- ×Pipelines that work on the sample data and quietly fall apart on the real thing.
- ×Anything where we'll figure out hallucinations later.
If AI is going inside a system that matters, it has to behave like everything else in that system: predictable, monitored, debuggable, correct.
Four conditions that usually have to be true.
You have a real ERP, platform, or product, and you want AI built into it — not next to it.
The outputs have to be right — financially, operationally, contractually.
You'd rather have one team owning the data layer, the model layer, and the integration layer than three vendors who disagree on whose fault it is.
You can show us actual production data, actual customers, and actual decisions the AI needs to support. We don't ship pipelines on hypotheticals.
Have a real product that needs a real AI layer?