Dataset engineering
Source review, cleaning, normalization, labeling guidance, splits, lineage and quality checks aligned with the target task.
Adapt model behavior with evidence, not guesswork
Model Fine-Tuning
MODEL TRAINING & FINE-TUNING
Dataset preparation, task-specific training, fine-tuning, evaluation and inference integration for products whose requirements exceed an off-the-shelf model.
WHAT WE BUILD
A data and evaluation pipeline for changing model behavior deliberately.
When prompting and retrieval cannot meet the requirement, we prepare domain data, run controlled training or fine-tuning experiments and integrate the resulting model into a measurable inference path.
Training experiments
Supervised fine-tuning, continued adaptation or task-specific models where evidence supports the added complexity.
Evaluation and regression
Held-out datasets, task metrics, qualitative rubrics, safety cases and comparison against simpler baselines.
Inference integration
Serving, batching, latency, hardware, privacy, monitoring and retraining triggers within the surrounding product.
HOW WE WORK
How we choose between retrieval, fine-tuning and training
Training is not the default answer. We use the cheapest valid baseline first, measure the gap and add model adaptation only when it improves the behavior the product actually values.
- 01
Define the task and metric
Specify expected inputs, outputs, edge cases, quality threshold, privacy constraints and inference envelope.
- 02
Audit and prepare data
Assess volume, representativeness, rights, leakage, labeling consistency and the cost of maintaining the dataset.
- 03
Run controlled experiments
Compare baselines and adaptations with tracked configurations and held-out evaluation.
- 04
Integrate and maintain
Package inference, monitor drift and failures and define the conditions for future data or model updates.
DELIVERABLES
Model adaptation deliverables
Work is documented so a result can be reproduced, compared and maintained rather than depending on one opaque experiment.
- Dataset specification and preparation pipeline
- Baseline and experiment record
- Fine-tuned or task-specific model artifacts where applicable
- Evaluation and regression suite
- Inference and retraining integration plan
FAQ
Questions specific to this path.
Can you train a foundation model from scratch?
That is rarely the economical product decision. We evaluate task-specific training and adaptation honestly and do not imply foundation-model scale without the data, compute and program required.
How much data do we need?
It depends on task complexity, model choice, label quality and the performance gap. A data audit and baseline experiment provide a more credible answer than a universal minimum.
How do you prevent evaluation leakage?
We separate training, development and held-out evaluation data, track provenance and review near-duplicates and contamination risks.
NEXT STEP
Bring us the constraint, not a polished brief.
A technical lead will review the current state and recommend the smallest useful next step.
Start a project
