For ML Engineers

For ML engineers who design model systems at the whiteboard.

BoardSnap is an iOS app that turns whiteboard photos into structured summaries and action plans in ten seconds. For ML engineers, that means model architecture design, training pipeline decisions, and inference system planning produce documented artifacts — not just a session that lives in the memory of whoever was in the room.

Download on the App Store Free to start. Pro from $9.99/mo or $69.99/yr.

What hurts today

Model architecture design sessions produce a clear diagram of layers, attention mechanisms, and training objectives that gets partially re-interpreted when the code is written
Training pipeline architecture decisions — data preprocessing, augmentation strategy, batching approach — happen verbally at a board and get reimplemented with subtle differences across experiments
Inference serving architecture discussions about latency targets, model versioning, and fallback behavior produce verbal agreements that don't make it into the serving infrastructure doc
Evaluation methodology sessions produce a clear framework on the board that doesn't get documented in the model card
ML system design reviews generate feedback that evaporates between the review and the implementation
Cross-functional model deployment discussions — what the model outputs, how the product uses it, what the failure modes are — produce different understandings in each team

How BoardSnap helps

Snap a model architecture session and get a structured description of the architecture choices, the training objectives, and the open engineering questions — the first draft of your model doc
Training pipeline design boards produce a step-by-step written description of each pipeline stage — data ingestion, preprocessing, training, evaluation — ready for your ML platform documentation
Inference serving diagrams produce a structured architecture summary: model endpoint design, latency targets, fallback behavior, and monitoring strategy
Evaluation methodology boards produce a written framework — metrics, test sets, baseline comparisons, and acceptance criteria — the methodology section of your model card
Brand-aware AI writes summaries using your team's actual framework names, your model naming conventions, and your ML platform vocabulary
Pin your ML platform architecture or your current research objectives so every future board chat already knows the technical context

A day with BoardSnap

Model architecture design
Sketch the model architecture — layers, connections, training objectives, key hyperparameters. Snap. BoardSnap produces a structured description of the architecture and the design decisions — source material for your model documentation.
Training pipeline design
Draw the pipeline — data sources, preprocessing steps, augmentation strategy, training setup, evaluation loop. Snap. The summary describes each stage's responsibilities and the inter-stage dependencies.
Inference system design
Map the serving architecture — model endpoint, request flow, latency targets, caching strategy, fallback behavior. Snap. BoardSnap produces an inference architecture summary ready for your serving infrastructure doc.
Experiment planning
Define the experiment on the board — hypothesis, architecture variant, training configuration, evaluation metric. Snap. The structured summary becomes your experiment record entry — the 'what we tried and why' that experiment logs should always have.
Model review
Present training results, error analysis, and proposed next steps on the board. Snap. BoardSnap captures the findings, the feedback, and the action items as distinct sections — the meeting summary writes itself.

Features that matter for ml engineers

Architecture diagram reading

BoardSnap AI reads neural network architecture diagrams, pipeline flow charts, and system dependency maps. It produces structured written descriptions of the design — components, data flow, and decision rationale.

Brand-aware AI

Paste your team's ML platform docs, your model registry, or your research wiki URL. BoardSnap AI learns your framework names, your model naming conventions, and your ML team's technical vocabulary.

Pinned context

Pin your research objectives, your current model architecture baseline, or your evaluation framework. Every board chat already knows the ML context — follow-up questions get technically grounded answers.

Projects per model or system

Separate projects for different model development tracks — recommendation systems, fraud models, NLP features. Each project has isolated board history and technical context.

Auto-generated subtasks

High-level ML tasks break into concrete implementation steps: implement the data pipeline, train the baseline, run the evaluation suite, profile the inference latency. Scoped to individual engineering work items.

Frequently asked

Can BoardSnap read a neural network architecture diagram?

Yes. BoardSnap AI reads layered diagrams — encoder/decoder structures, attention mechanism diagrams, skip connections — and produces a written description of the architecture. The more annotated the diagram, the richer the output.

How does it help with ML experiment documentation?

Snap your experiment design board before you run the experiment. BoardSnap produces a structured experiment record — hypothesis, architecture variant, training configuration, and evaluation plan — the kind of documentation that makes experiment logs actually useful.

Is it useful for model cards and model documentation?

Yes — snap each design session separately (architecture, training pipeline, evaluation methodology, inference system). Each produces a structured artifact. Combine them to build the model card instead of writing it from scratch after training completes.

Can it capture a training pipeline with complex preprocessing steps?

Yes. Draw the pipeline stages, the data transformations, and the augmentation steps on the board with clear annotations. BoardSnap AI reads the step-by-step flow and produces a structured pipeline description with each stage's role and dependencies.

What about cross-functional ML deployment discussions?

Snap the board at the end of an ML + product + engineering deployment discussion. Everyone gets the same structured summary of what the model outputs, how the product uses it, and what the failure modes are — no diverging assumptions across teams.

Built for ml engineers who ship.

Snap a whiteboard. Ship the action plan. In ten seconds.

Download on the App Store

Free · 1 project, 30 boards Pro $9.99/mo · everything unlimited Pro $69.99/yr · save 42%