01
Dataset package
Structured examples with source notes and review splits.
AI / ML Adaptation
Model adaptation for private workflows.
We adapt models around a defined task, then deliver usable artifacts, evaluation evidence, and activation notes for controlled deployment.
task boundaries / dataset package / LoRA adapters / fused artifacts / evaluation evidence
02
Adapter or artifact
LoRA adapter or fused artifact for deployment needs.
03
Training run record
Configuration, assumptions, run notes, and reproducibility context.
04
Evaluation evidence
Task checks, regression cases, failures, and limits.
05
Activation runbook
Loading, rollback, versioning, and handoff notes.
System boundary
runtimelocal | private-server | customer-cloud
datainternal | confidential | regulated
modelslocal | routed | hybrid
reviewhuman-in-loop
handoffdocs | runbook | repo
Fit
When this service fits
Best for teams with a real workflow, usable examples, and private deployment constraints.
- Known workflow needing better model behavior
- Repeatable inputs and expected outputs
- Sensitive data or controlled deployment needs
- Implementation help beyond model selection
Boundary
Task boundary and data package
Adaptation starts by defining behavior, examples, exclusions, and evaluation material before any training run.
- Target behavior and failure modes
- Input and output rules
- Allowed and excluded data sources
- Train, validation, and test split
Artifact
Adapter and artifact delivery
We prepare the adaptation artifact around the deployment path, not as an isolated experiment.
- LoRA adapter when appropriate
- Fused artifact when required
- Configuration and run record
- Activation notes for operators
Evidence
Empirical evaluation and limits
The delivery includes task evidence, regression checks, and known limits for customer review.
- Task-specific evaluation examples
- Regression checks across versions
- Failure and edge-case notes
- Customer review loop included
Runtime
Runtime and deployment boundary
Adapted artifacts are prepared for the customer's actual operating boundary and handoff model.
- Local workstation or on-prem GPU
- Private cloud or VPC context
- Air-gapped constraints when required
- Loading, rollback, and versioning
Patterns
Example engagement patterns
Typical work centers on constrained model behavior inside existing professional workflows.
- Domain summarization with approved examples
- Internal review routing or classification
- Structured professional document drafting
- Existing local model evaluation
Ledger
Deliverables ledger
A concrete package for technical review, controlled activation, and future maintenance decisions.
- Dataset package with provenance notes
- Adapter or fused model artifact
- Training configuration and run record
- Evaluation report and activation runbook
Boundary check
Bring the workflow, not a buzzword.
We scope adaptation from your task, examples, operating boundary, and review criteria.
Share the workflow, examples, deployment boundary, and current model or runtime constraints.
System signals
- You can show representative inputs and outputs.
- The workflow needs private or controlled deployment.
- Your team wants direct technical implementation support.
Boundary limits
- You only need generic chatbot exploration.
- You expect guaranteed accuracy or compliance certification.
- You need foundation model research from scratch.