01
Custom LLM solutions
Adapt language models to your documents, terminology, and tasks with retrieval, targeted fine-tuning, and practical evaluation.
APPLIED AI + DATA ENGINEERING
Custom AI.
Local compute.
Usable data.
Turn domain knowledge and raw data into practical systems. Eloquent Algorithmics helps with custom LLMs, curated datasets, local AI hardware and setup, and raw point cloud processing.
FROM RAW INPUT TO WORKING SYSTEM
MODEL / DATA
COMPUTE / GEOMETRY
Four disciplines. One practical workflow.
FOUR CONNECTED SERVICES
Built around the work you need to do.
Start with one service or connect several into a complete workflow. Each begins with your inputs, intended result, and operating constraints.
02
Dataset engineering
Prepare consistent, documented datasets for training, evaluation, and retrieval through cleaning, labeling, and quality checks.
03
Local LLM hardware & setup
Plan and configure local AI compute around your models, workload, users, and budget—from hardware sizing to inference setup.
04
Raw point cloud processing
Prepare raw LiDAR and 3D scan data for downstream work with filtering, alignment review, organization, and usable exports.
HOW THE WORK MOVES FORWARD
A clear process. A result you can evaluate.
Define the task, inspect representative data, and test a focused solution. Use the findings to guide deployment and a documented handoff.
01 / Define the task
Agree on inputs, expected outputs, constraints, and the criteria used to judge the result.
02 / Inspect the data
Review representative documents, records, or scans and identify gaps that affect the work.
03 / Build and evaluate
Develop a focused workflow, test it on relevant examples, and document the findings.
04 / Deploy and hand over
Configure the agreed environment and provide the process, settings, and notes needed to operate it.
A PRACTICAL START
Good questions.
Clear starting points.
A few things to consider before starting an LLM, dataset, local AI, or point cloud project.
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.
What would you like your data to do?
Tell us about the task, the data you have, and where the solution needs to run.