Reactive Natural Language Compiler

Documentation in.
Code out.

Write requirements — get working software. Change a document — every dependency updates automatically.

Three problems AI didn’t solve

Coding agents accelerated code production. They didn’t fix the process around it.

01

More code, more burden

AI coding agents generate volumes of code that humans must review. The creative-to-tedious work ratio shifts against you — more verification, less design. 83% of developers report emotional exhaustion.

02

Docs rot in days

Documentation and code are separate artifacts that inevitably diverge. In regulated industries, this isn’t just inefficiency — it’s a compliance risk. In every team, it means wrong assumptions.

03

Domain experts locked out

Business analysts, product managers, and domain experts hold critical knowledge but can’t contribute directly. Every requirement passes through interpreters, losing fidelity at each step.

How Docency works

Documentation is the source code. Code is the compiled output.

01

Write in natural language

input.md

Describe requirements, architecture, and business rules as interconnected documents — in plain language your whole team understands.

## User Authentication

The system **shall** authenticate users
via OAuth 2.0 with PKCE flow.

Session timeout: 30 minutes.
Max concurrent sessions: 3.
02

Define the dependency graph

graph.resolve()

Declare which documents depend on which. The graph constrains change propagation — only affected documents update.

auth.md
├── api-routes.md
├── db-schema.md
└── middleware.md
    └── rate-limit.md
03

Change one, cascade all

cascade.propagate()

Edit any source document. Docency’s reactive engine identifies every dependent document and artifact, propagating context-aware updates through the entire graph.

auth.md           [changed]
├── api-routes.md  [updated]
├── db-schema.md   [updated]
└── middleware.md   [updated]
    └── rate-limit.md [updated]
04

Code compiles from documentation

compile.emit()

The NL-to-code compiler generates source code, configurations, API definitions, and data schemas — all traceable to their documentation source.

export async function authenticate(
  req: Request
): Promise {
  const token = await verifyPKCE(req);
  return createSession(token, {
    timeout: 30 * 60,
    maxConcurrent: 3,
  });
}

How Docency compares

SDD tools structure a single agent task. Docency manages the continuous production process.

Capability Docency Tessl AWS Kiro Copilot
Reactive cascading Graph-wide
Documentation drift 0 (structural) Per-file only > 0 N/A
Governance & audit
Traceability (req → code) Automatic Per-spec Per-task
Non-coder participation Native Limited Limited
SaMD / EU MDR ready

Built for teams that need it most

60–80% effort reduction

SaMD & Compliance

EU MDR / IEC 62304 require traceable documentation. Docency provides it by architecture — not by process. Documentation IS the source; traceability is automatic.

Dev Teams

Eliminate the doc-code gap. Your team writes requirements in natural language; Docency compiles and maintains code. Code review shifts from “is this correct?” to “is this what we meant?”

Non-coding Specialists

Business analysts and domain experts write requirements directly. No translation step, no lost-in-translation errors. The feedback loop shrinks from weeks to minutes.

The next step in how software is made

Each step adds what the previous lacked. SDD added structure to vibe coding. Docency adds governance, cascading, and continuity.

2023

Vibe Coding

Ad-hoc prompting. “Just ask the AI.”

2025

Spec-Driven Development

Structured specs for AI agents. Kiro, Tessl, Spec Kit.

2026

Governed Code Compilation

Continuous production from documentation.

Docency

Start compiling.

Join the waitlist for early access. We’ll notify you when it’s your turn.