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Selected work

Real workflows, governed systems, shipped products.

A compact view of the systems I build across product engineering, applied AI and AEC technology. Each project is organized as Problem → Built → My role → Evidence.

01 · Engineering Intelligence Platform

Turning fragmented engineering evidence into a defensible determination.

Owner requirements arrive in prose, drawings, schedules and BIM models. The system compiles them into predicates, investigates the evidence graph and keeps the final decision deterministic.

Read the full case study →
Coordinated building services model showing structural steel, cable tray, conduit racks and switchgear.
Physical context · the system begins with the real workflow.
RoleProduct definition · architecture · implementation · deployment
SurfaceRevit add-in · backend · evidence graph · review workflow
Decision modelAI investigation · governed deterministic verification

02 · Production systems

Revit API engineering that survives the host.

A production add-in is not a script that runs once. It has to respect Revit's document lifecycle, transaction model, external event queue, model state, version upgrades and the people using it.

Problem

Engineering automation often works in a controlled demo and becomes unreliable inside a long-running desktop host.

Built

Version-aware C#/.NET add-ins with WPF/MVVM surfaces, deterministic QA and explicit evaluation runs.

My role

Technical ownership from workflow discovery and architecture through implementation, validation and release.

Read the Revit API notes →

Featured interaction

A decision should be able to show its work.

Trace a synthetic requirement from claim and scope through sources, model elements, predicate, findings and final determination.

Fig. 01Evidence topologySynthetic data

Trace a nodeHover for one hop · click to lock the full chain

Drag nodes to rearrange. Hover for one hop. Click to lock the full chain. Keyboard focus is supported.

View the reasoning chain as text
  1. Claim: exterior luminaires must be mounted at least 10 ft.
  2. Scope: all applicable exterior fixtures in the loading dock and Corridor B12.
  3. Sources: drawing E1.01 and the lighting schedule inform the evaluation.
  4. Predicate: evaluate h(x) ≥ 10 ft for every element in the population.
  5. Counterexample: Fixture L-114 is recorded at 8.5 ft.
  6. Determination: NOT MET. One valid counterexample disproves the universal claim.
Relation keyscope / containsinforms / evaluatescontradicts / drives failure
ConclusionNOT MET · L-114 is 8.5 ft against a 10 ft requirement

One valid counterexample is enough to disprove the universal claim. The graph makes that reasoning inspectable.

Verification playground

From language to proof.

Run a small synthetic requirement check. The output keeps the distinction visible: AI can investigate the sources, while governed logic makes the determination.

Requirement“All exterior luminaires shall be mounted above 10 ft.”

∀x ∈ P,  h(x) ≥ 10ft

Ready to run

Featured interaction · AEC

The same building, three ways of seeing it.

Topology asks where the spaces and structure are. Systems asks how services move through them. Evidence asks where a requirement meets physical reality.

Topology · room volumes, floor plates and the structural frame establish the physical building.

Drag to orbit · click to activate zoom · use the modes to change the question.

TopologyRooms · bays · frame
SystemsPower · controls · HVAC
EvidenceRequirement · source · finding

Product surface

The output belongs inside the workflow.

A good system does not stop at a model response. It gives the reviewer a traceable result, a source, a counterexample and a next action inside the environment where the work already happens.

Requirements + models + drawings

Evidence and engineering intelligence

AI investigation

Governed verification

Review, report and deployment

Selected experiments

What I am building next.

Independent of client-confidential work. Public artifacts go live as each project ships.

AI Memory OrchestratorA git-native memory layer for AI coding agents: canonical project memory, machine indexes and validators.Published · view source →Digital Twin VisualizationTopology, Systems and Evidence as three questions asked of the same abstract building.Live above ↑
Engineering Requirement CompilerA generic implementation of requirement parsing and compilation into populations and predicates.In development
BIM Knowledge GraphRevit/IFC relationships represented as an inspectable graph of systems, spaces and elements.Prototype · private