AI-grown business systems

Describe your operations.
Watch the system grow.

Sabi Seeds turns a plain description of how your organization runs into a live BPM front-end and full operating backbone — approvals, assets, finance, HR — customized and assembled by AI, not hand-built over quarters.

Daysfrom a plain-language brief to a working system
1 stackcovering BPM, EAM, finance, HR and procurement
Yoursto keep customizing as the org changes
one seed request → a connected operating system
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How it grows

Four stages, the same way a seed becomes a tree.

No blank IDE, no six-month discovery phase. You describe the operation; the AI cultivates the rest — and you're always the one holding the shears.

Stage 01 · Plant

Describe the process

Tell it what happens today — who requests, who approves, what triggers an escalation. Plain language, not a spec document.

Stage 02 · Sprout

AI drafts the system

Data tables, approval stages, routing rules and the review forms your team will actually use are generated together, already wired to each other.

Stage 03 · Grow

Review and shape it

Adjust thresholds, roles and routing in plain settings — no redeploy, no ticket to a dev team. The system bends to how you actually work.

Stage 04 · Harvest

Run it, live

Every request, approval and exception is logged, auditable and reportable from day one — across every module you've grown.

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Coverage

One backbone for the operations any organization runs.

Not a single-purpose app — a shared frame that grows a new module for every process your organization actually has, on the same audit trail and the same rules engine.

BPM

Approvals & workflow

Multi-stage requests with role-based routing, thresholds and escalation, built for the way your teams actually sign off.

EAM

Assets & equipment

Requests, allocation and lifecycle tracking for physical and IT assets across departments and sites.

Finance

Expense & cash advance

Tiered approval by amount and role, budget initiatives, and adjustments — with the routing logic held in settings, not code.

HR

Leave & people ops

Leave requests, manager approval and escalation, onboarding checklists — the everyday people processes, covered by default.

Procurement

Vendor & purchase orders

Vendor selection panels and purchase orders that share data with the same approval backbone as every other flow.

Compliance

Risk & assessment

Structured assessment flows — like data-privacy impact review — with the same rigor as a financial approval.

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Why grow it, not build it

The case for an AI-grown system, plainly put.

01

Weeks, not quarters

Skip the requirements-to-code lag. What used to be a scoping deck becomes a running system in the same week.

02

Priced like software, not a project

No bespoke build fee per department. New modules grow from the same backbone instead of starting from zero.

03

Shaped to you, not the other way round

Every organization routes approvals a little differently. The rules engine adapts to your thresholds — you don't adapt to a template.

04

One record of truth

Every stage, decision and exception lands in the same audit trail, so reporting and compliance aren't a separate project.

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Under the hood

How a flow actually gets built, and what it runs on.

Not a black box. Here is the real build pipeline and the real stack — the same one running this page's own BPM flows.

How AI builds your business flow

One model call to draft the spec, then deterministic code generation the rest of the way.

How AI builds a business flow Five stages: Describe, AI drafts the spec, Generate code, Deploy, Live flow. Only stage two calls a language model; stages three through five are deterministic. 1 · Describe One plain-language brief. No spec document, no ticket. AI 2 · AI Drafts the Spec The ONLY LLM call in the whole pipeline. Returns a FlowSpec — stages, roles, fields, thresholds — which you review and edit before continuing. 3 · Generate Code Deterministic templates — no LLM. Same spec always produces the same schema-eform-engine columns, stage- routing rows, and UI form source. 4 · Deploy 5 scripted calls: create table → patch config → seed flow defs + workflows + features → write form files → patch routes. 5 · Live Flow The interface reloads instantly. The new form runs live on schema-eform-engine + the shared workflow-engine webhook. Steps 3–5 run without ever calling the model again.

Solid stack architecture

One reverse proxy, one app server, three purpose-built services underneath.

Solid stack architecture Browser talks to an edge proxy, which talks to the bpm-app server, which talks to schema-eform-engine backed by a relational store, workflow-engine, and the ml-advisor service. bpm-app also calls an external LLM provider server-side only, once per new flow. Browser — Web Client edge-proxy — HTTPS Reverse Proxy bpm-app — App Server hosts FlowBuilder's one LLM call (server-side) + the ml-advisor proxy schema-eform-engine workflow-engine ML ml-advisor rule_sets: routing rules, no code webhook: bpm-transition-v2 zero-shot prediction, not generation relational-store LLM Provider (external) 1 call per new flow. Server-side only — holds the API key.
Get started

Bring us how your organization runs today.
We'll show you what it grows into.

A short walkthrough, built around your own processes — not a generic demo.

nhi.lam@sabiseeds.com · +84 903 093 027 · Mon–Fri, 9:00–18:00