Give general models a specific advantage.
Prompts only take you so far.
Deepdense brings the adaptation loop into one
focused workspace. Here’s what we’re building.
01instructionextract invoice fields
02contextyour domain examples
03expectedstructured output
A planned workspace for organising examples, reviewing training data, and defining the task your model needs to learn.
Configure task-specific fine-tuning experiments, with a clear record of the data and settings behind each run.
Compare baseline and adapted outputs against the same evaluation set. Inspect failures before choosing a model.
Start with one task. Set a baseline.
Make a change. Measure the difference.
Bring representative examples and decide what a good answer looks like.
Evaluate the base model before investing in adaptation.
Run focused experiments and inspect the outputs that changed.
Planned export of model adapters and evaluation reports for your own stack.
Explore the proposed API experience.
Switch tasks and run a local simulation.
CONCEPT API · NO LIVE REQUESTS
/v1/adaptations// Run the example to preview a response. // No data is uploaded. No training job is created.
Explore model adaptation for turning domain-specific documents into consistent structured outputs.
DOCUMENTS FIELDSExplore extractionDevelop classifiers around your terminology, categories, and examples of the edge cases that matter.
TEXT DOMAIN LABELSExplore support routingBuild specialised assistants with task-specific behaviour and repeatable checks on response quality.
CONTEXT USEFUL RESPONSESExplore assistantsExplore datasets, experiments, evaluations, and the tools connecting them.
Explore productTurn inconsistent descriptions into a shared attribute vocabulary.
Explore the use caseExplore request formats, model previews, and API key management.
Developer toolsWe’re looking for engineering teams with a focused use case
to help shape Deepdense’s first product.