The Custom Code Intelligence Platform
Your software system, turned into a model people and AI can trust.
The platform reads the real source and the existing documentation of a system — however old, however large, in whatever language — and produces complete, source-cited documentation backed by a queryable knowledge graph.
Four capabilities, one spine
Every system passes through the same four capabilities — the reason the same product can describe a mainframe transaction and a modern web service in the same shape.
Document
A living knowledge base of every screen, program, batch job, interface, table and field — regenerated from the code, never hand-maintained.
Explain
Human-readable narrative in business language, grounded strictly in the cited facts.
Recommend
Evidence-backed transformation recommendations at system, module and artifact level — tuned to your declared goal.
Transform
A deterministic, machine-readable model AI agents can reason and act against, safely.
Navigate the system top-down
The documentation is an application you navigate — System → Module → Software Artifact → Code — opening with business summaries and growing more technical as you go deeper. The business meaning is never lost on the way down: every module, artifact and code unit carries its own plain-language explanation of what it is for, right next to the technical detail.
Read every artifact business → technical → implementation
A product owner reads the business value of a screen; an architect reads the call graph and complexity; an engineer reads the source line. Same artifact, same source of truth.
Business / functional
What it is for and the outcome it enables — rules, validations and calculations, in plain language.
Technical / architecture
Architectural role, call graph, interfaces, dependencies and structural complexity.
Implementation / code
How the code actually works, beside the source — with code-health signals for duplicated, unused and embedded open-source code.
Three editions, one platform
SAP
Flagship edition for ABAP / S/4HANA / RISE — custom code classified, scored against standard, and checked against real usage.
Learn moreMainframe
COBOL, PL/I, RPG, Natural and more — the systems that run the core, finally made transparent.
Learn moreSoftware
Modern stacks (Java, Python and beyond) — the same documentation, graph and AI surface.
Learn moreSee the platform on your own system
A 30-minute demo on a representative slice of your code.
Editions
Go deeper by system type
SAP
SAP customers facing the 2027/2030 S/4HANA deadline must rationalise years of accreted custom code under time pressure. Sysparency turns that from an opinion-driven exercise into an evidence-driven one.
Learn moreMainframe
Large mainframe and IBM i systems run the core business processes of banks, insurers and public institutions — and are barely understood. Sysparency reads the real source and makes the behaviour explicit, cited and queryable.
Learn moreSoftware
Custom Code Intelligence is not only for legacy. Modern stacks accumulate the same undocumented complexity — and the same key-person risk. Sysparency gives Java, Python and other systems the same documentation, graph and AI surface.
Learn moreFAQ
Straight answers.
Complete, navigable, source-cited documentation of a software system, a queryable knowledge graph, and an assistant that answers complex analytical questions. Every documented fact links back to the exact source it came from.
A general AI reads code and guesses — and hallucinates. Sysparency first extracts the system’s facts deterministically, with a citation on every fact, and only then has AI write explanations grounded in those facts. The AI explains; it never invents. Re-running on the same input yields the same output.
Yes. The deterministic core — the documentation structure, the knowledge graph and the query surface — works with no AI at all. AI enrichment is an opt-in you control. With it off, no AI request is ever made.
SAP, mainframe and modern software — across a broad, growing set of legacy and modern languages. The approach is language-agnostic: covering a new technology adds breadth without changing the rest.
Completeness is measured as recall against a manual inventory — the target is ≥95%, and the reference corpus measures 100%. Trust comes from provenance: every fact carries a citation, and every hidden dependency is explicitly flagged rather than silently omitted.
Enterprise single sign-on, encryption at rest, read-only access for the query layer, and an opt-in AI layer that can be kept EU-resident. The whole deterministic pipeline can also run fully on-premises and air-gapped — a proof of value can run before anything leaves your boundary.
The documentation is regenerated deterministically from the code on each upload, with caching so re-runs are cheap; the same input always yields the same output. Comparing two states, and producing documents from them (Markdown, DOCX, XLSX, PDF), is done by your AI client, such as Claude or ChatGPT, through the Sysparency MCP server from the cited sources.
With four views on the same code: modifications to the SAP standard with note provenance and a remediation path, the simplification catalog matched against the objects your code actually calls, the release status of every SAP object you use from the Cloudification Repository, and ATC findings combined with usage. From that, one recommendation per program: standard, key-user extension or side-by-side.
Only your custom code, never SAP standard. Seven read-only tools: graph schema, Cypher query, semantic search over the business descriptions, object list, metadata, read and search source. The server runs in your dedicated environment behind Microsoft Entra ID.
Book a 30-minute demo. A proof of concept on your own system takes about 30 minutes of your team's time: the export of your custom code. Two days later you are working with your analysed system, documentation, knowledge graph and assistant included.