Generative customization for SaaS platforms
Every enterprise SaaS product is customized for each customer, and that customization is what limits how many you can take on. On2Go models your product as a knowledge graph, however complex, and turns each customer's customization into a governed program over it, so onboarding that runs for months becomes repeatable and stays maintainable as you grow.
The bottleneck
Each new customer brings data to migrate, often millions of records, checked for integrity and validated against your system before it can load. Alongside it comes customization across four surfaces: the data model, workflows, reports and integrations. The data goes back and forth for weeks as errors are found and corrected, and each customization is specified informally in documents and spreadsheets, then re-encoded by hand. Multiply that by every customer you onboard. And every time your product moves to a new release that breaks backward compatibility, the same migration runs again for every existing customer.
What you get
Because each customer becomes a program over your product graph, the work that gates onboarding becomes something your team can author, review and ship quickly.
Each customer's customization is a program over your product graph, so onboarding stops taking an engineering cycle for every client. Your team serves more customers with the capacity it already has.
A customer's rules are authored and reviewed as a program you can read, so a new customization reaches production in days and every change is easy to trace.
The customization lives in one machine-readable language tied to your product graph. It replaces the spreadsheets and documents that hold this today with a specification your systems can query, test and maintain across hundreds of customers.
Every customization is reviewable and runs deterministically, and the same engine runs inside your own cloud, so customer data stays with you.
How it works
On2Go is compiled for your product. Three moving parts, one path from a plain-language requirement to a live result.
Extract the code graph and functional graph from what you already run: the data model, workflows, reports, integrations, approvers and events. The specification stage that used to be informal becomes formal and queryable.
A language whose vocabulary is the graph's own primitives. A plain-language requirement becomes a program, however large the customization, and the grammar and the graph keep every line coupled to what your product supports.
The program becomes the specification and replaces the hand-built implementation. It compiles to the running screens, flows and records, or to a clean validated data load. Same input, same result.
Composition
Most of what a customer runs comes straight from the graph, shared with every other customer and unchanged. What is specific to them is the set of rules that are theirs, named once at the composition and written where anyone can read them.
Governed by the graph
The language core is generated from the knowledge graph. Its words are your entities, its codes are your codes. So a program can only express what your platform supports, however large the customization grows, and anything outside the graph is refused at author time. Every element traces back to a graph source, so a reviewer reads the customization the way they would read a spec.
Deterministic by design
The intelligence lives at design time, reading a customer's requirement and authoring the program with you. At run time there is no model in the loop, so a customization is repeatable, reviewable, and safe to regenerate at any scale.
Plain-language intent, a new field, an eligibility branch, a customer threshold, captured once and written as a program you can read and edit.
One command syncs it. The definition is never hand-authored, so the language cannot drift from the product.
The same program gives the same output, whether a screen, a flow, or a million-row load.
Edit the program on the left, and the compiled screen, flow and configuration update on the right. What you author is what ships.
Open any generated element, or any bypassed, mapped or rejected data row, and see the exact line and rule that produced it.
One engine, in practice
Whether the output is a customized application or a customer's data landed cleanly in your product, it is the same move: compile each customer into a governed program over the product's graph.
Covers the surfaces that carry the customization work: the data model, workflows, reports and integrations. The standard is inherited from the graph, and what is specific to the customer is made explicit, coupled to the product's primitives, and checkable.
Compiles to a clean, validated load: rows checked for integrity, mapped, and validated against your system's master data. Errors come back by row with the reason, so a customer can correct their own data before it loads, and every row traces back to the line that produced it. The same move carries a customer's data forward when a new release breaks backward compatibility.
Scale and ownership
The exact same compiled program runs in the browser for instant previews and on a container service for large, confidential jobs in parallel, held in lockstep by a conformance suite, so the preview and the production run agree.
Author and compile on every keystroke. Samples and small jobs, fully offline, nothing uploaded.
The same package executed on a scalable container service for million-row loads and combined, cross-file runs.
Portable by design. The same image self-hosts inside your cloud, so customer data never leaves it.
Bring one real customization requirement, or a messy customer file. We will model a slice of your product, compile a program for it, and run it in front of you.