THE SERVICES
From raw inputs to useful systems.
Four connected services for teams working with language models, complex datasets, local compute, and 3D scan data. Scope the work around your goals, source material, and target environment.
01 / Custom LLMs
Custom LLM solutions
Build a language model workflow around a specific job: finding information, extracting structured data, drafting with context, or assisting a repeatable process. Explore existing models, document retrieval, and fine-tuning where the task and available data support it.
TYPICAL SCOPE
- Define the use case, expected outputs, and evaluation examples.
- Assess model options and deployment requirements.
- Build document retrieval and context preparation workflows.
- Develop prompts, structured outputs, or targeted model adapters.
- Evaluate results and connect the workflow to its intended application.
WHAT TO BRING
A description of the task, representative documents or examples, desired outputs, and deployment constraints.
WHAT WE DEFINE TOGETHER
A scoped model workflow or prototype, evaluation findings, and configuration and integration notes.
02 / Datasets
Dataset engineering
Turn scattered documents, records, and source files into a dataset with a clear structure and purpose. Define the schema, preserve useful source information, and establish checks that make the data easier to review, update, and use.
TYPICAL SCOPE
- Inspect source formats, field structure, and data quality.
- Extract, normalize, clean, and deduplicate records.
- Develop labeling rules and annotation guidance.
- Prepare training, validation, and test splits as needed.
- Document provenance, versions, and known limitations.
WHAT TO BRING
Representative source files, the intended use, required fields or labels, and any handling or access requirements.
WHAT WE DEFINE TOGETHER
Prepared data in agreed formats, a schema or data dictionary, quality findings, and a repeatable preparation process.
03 / Local AI
Local LLM hardware & setup
Make hardware and software decisions together. Assess the memory, storage, power, cooling, and runtime support needed for the models and workloads you want to run, then configure and test a local inference environment.
TYPICAL SCOPE
- Assess existing hardware and define the target workload.
- Size GPU memory, system RAM, storage, and supporting components.
- Configure supported drivers, runtimes, and model serving software.
- Set up local endpoints and agreed network and access controls.
- Benchmark representative requests and document operation.
WHAT TO BRING
Target models or tasks, expected users and context length, current hardware, budget, and network requirements.
WHAT WE DEFINE TOGETHER
A hardware and deployment plan, an agreed local inference setup, benchmark findings, and operating notes.
04 / Point clouds
Raw point cloud processing
Build a processing workflow around the way your scans were captured and the output you need. Review a representative sample, its units and coordinate information, and the required level of detail before defining the processing steps and quality checks.
TYPICAL SCOPE
- Inspect scan formats, metadata, coverage, and coordinate information.
- Filter noise and downsample to an agreed level of detail.
- Assess registration and alignment across scan sets.
- Apply classification, segmentation, or tiling where appropriate.
- Export agreed formats with processing and quality notes.
WHAT TO BRING
Representative scan files, capture details, units and coordinate information when available, and the desired output.
WHAT WE DEFINE TOGETHER
Processed point clouds or agreed derivative outputs, a documented processing workflow, and quality findings.
Formats, alignment requirements, and deliverables are confirmed after reviewing a sample.
DESIGNED TO WORK TOGETHER
Connect the pieces when the project needs it.
A dataset can support a custom model. A local server can host the resulting workflow. A scan processing pipeline can produce organized spatial data for analysis. Scope these connections around a concrete use case and agreed outputs.
PROJECT QUESTIONS
Before we begin.
Do I need to train a language model from scratch?
A project can often begin with an existing model, a retrieval workflow, or targeted fine-tuning. The choice depends on the task, available data, operating constraints, and evaluation results.
What makes a dataset ready for use?
Readiness depends on its purpose. Useful checks include consistent structure, clear labels, duplicate handling, representative evaluation data, and documented sources and limitations. These checks are defined for the intended workflow.
How is local LLM hardware selected?
Sizing starts with the model and workload: memory requirements, context length, concurrent users, response expectations, and budget. Hardware compatibility and runtime support are assessed together, then checked with representative requests.
What information is needed for point cloud work?
Start with a representative scan, its source format, capture details, units and coordinate information when available, and the output you need. These establish the processing steps and the appropriate quality checks.
Can a project combine several services?
Yes. Dataset preparation, model development, local deployment, and scan processing can be scoped together when they support the same objective. The inputs, dependencies, and deliverables are agreed for each part.
Start with a task and a representative sample.
A short description of the inputs, desired result, and target environment is enough to begin the conversation.