Nomograph Labs Nomograph Labs

Instruments that help people leverage AI

Context engineering. Retrieval workflows. Reproducible benchmarking. Spec management for the humans and agents doing the work.

DOMAIN MBSE SysML v2 toolchain + benchmark SDLC graphs GitLab Knowledge Graph eval code repair feedback signal primitives EVALUATION APPARATUS define tasks build tools vary conditions measure replicate structured engineering queries parsers, CLI, MCP servers models x tools x representations per-field scoring, statistics N trials, ablations, baselines open source | open data | open licensed designed for independent replication designed to foster community exploration where is leverage? DISCOVERIES representation > retrieval examples essential in tools precision > brevity OUTPUTS papers preprint, submission MRs upstream open tools CLI, bench, data community open method discoveries inform what to explore next
benchmarks
2
SysML v2 + GitLab KG
tasks evaluated
194
132 SysML + 62 SDLC
experimental conditions
50+
across 5 models
papers
7
5 preprints + 2 venues
The consistent finding

How you present information to the model matters more than how you retrieve it.

Retrieval interventions (vector search, graph traversal, planning tools) produced null results. Representation interventions (pre-rendered views, tool selection guidance) produced large, replicable effects at a fraction of the cost.

O12

One sentence of tool selection guidance eliminated a 13-point accuracy penalty from over-tooling.

 O4

Pre-rendered model views scored 0.893 vs 0.558 for agent-assembled context. d=1.01, N=10. 4x cheaper.

Exploratory study, single corpus, N=3-10 replications. Full methodology and threats →

What we build

synthesist
spec management

Tracks work through orient-plan-agree-execute-reflect-report. Task DAGs, propagation, stakeholder dispositions. Go.
rune
skill registry

Syncs reusable AI agent instructions from git-based registries. Bidirectional, multi-registry, drift detection. Rust.
kit
tool verification

Manages developer toolchains from git registries. Checksum and signature verification. Generates mise config. Rust.
muxr
session manager

Tmux sessions organized into verticals and remotes. Save, restore, server isolation. One keybind to switch. Rust.
sysml
CLI + MCP server

Structural retrieval, graph traversal, and completeness checking for SysML v2 models. Rust. 14 commands, 10 MCP tools.
sysml-bench
benchmark harness

Reproducible evaluation of tool-augmented LLMs on structured engineering tasks. Python.
lever
reference architecture

Four primitives for LLM-correct codebases. Derived obligations, prescriptive failure, bundled enforcement.
kebnf
grammar converter

Converts OMG KeBNF specs to ANTLR4 and tree-sitter. Bridges OMG specifications and working parsers. Rust.
tree-sitter-sysml
parser

Tree-sitter grammar for SysML v2. 6 language bindings. The parsing foundation for the MBSE toolchain.

How this started

This work started as an academic exploration of how AI interacts with structured engineering artifacts. We built tools, ran benchmarks, wrote papers. Along the way, we found alignment with GitLab's Knowledge Graph team, who are solving related problems in context engineering and retrieval at production scale. We've been contributing findings on prescriptive failure patterns and tool description effectiveness into their eval methodology.

Everything is MIT-licensed and on GitLab. If you work with engineering models and are curious about how AI performs on them, we'd like to talk.

About us →