We take a base foundation model and calibrate it against your data, your task, and your risk tolerance — with a measurable checkpoint at every stage, from data curation through post-deployment drift monitoring.
Full fine-tuning isn't always the right tool. We start from your latency budget, data volume, and compliance requirements, then choose (or combine) the methods below.
Updating all model weights on your task-specific dataset. Highest ceiling on accuracy, used when you have a large labeled dataset and the infrastructure to support it.
Parameter-efficient tuning that trains small low-rank adapter matrices instead of the full model. Cuts GPU memory needs sharply while preserving most of full fine-tuning's gains.
Reinforcement Learning with Human Feedback — a reward model trained on human preference rankings, then used to steer generation quality via PPO or similar policy optimization.
Reinforcement Learning with AI Feedback — a scalable alternative to RLHF where a calibrated judge model supplies preference signal, reserved for narrower, well-specified tasks.
Training on curated instruction/response pairs so the model reliably follows task framing, output format, and tone constraints across a broad range of prompts.
Continued pre-training on your domain's unlabeled corpus (contracts, clinical notes, product catalogs) before task tuning, so the model's vocabulary and priors match your world.
Joint tuning across text, image, and structured/tabular inputs so a single model reasons across modalities instead of stitching separate pipelines together.
Training a smaller student model to reproduce a larger teacher model's behavior, for cases where inference cost or latency rules out running the full-size model in production.
A rough guide to how the three most-requested approaches trade off. Actual numbers depend on base model size and task.
| Dimension | Full Fine-Tuning | LoRA / QLoRA | RLHF |
|---|---|---|---|
| Typical GPU memory | High — full optimizer state | Low — adapters only | High — policy + reward model |
| Data required | Thousands+ labeled examples | Hundreds to thousands | Preference-ranked samples |
| Best for | Deep domain shift, max accuracy | Fast iteration, multiple task variants | Tone, safety, and preference alignment |
| Training time | Longest | Shortest | Longest (two training stages) |
| Risk of catastrophic forgetting | Higher without care | Lower — base weights frozen | Moderate — regularized against base policy |
No stage starts until the previous one has passed its checkpoint — this is what keeps fine-tuning a controlled process instead of a black box.
Sourcing and consolidating your production data, documents, transcripts, or logs, then filtering for relevance, duplication, and licensing constraints.
Structured annotation with inter-annotator agreement checks, using subject-matter reviewers for specialized domains like clinical or legal text.
Benchmarking candidate foundation models against your task on a held-out sample before committing to one, weighing license terms, context length, and inference cost.
Setting learning rate schedules, batch size, regularization, and adapter rank (for PEFT), then running a search pass rather than relying on defaults.
Distributed training runs with regular checkpoints, loss tracking, and early-stopping rules so a bad run is caught in hours, not after it finishes.
Task accuracy, held-out perplexity, human evaluation, and adversarial red-teaming, compared against the pre-tuning baseline before sign-off.
Staged rollout, real-time drift detection against the production data distribution, and a defined retraining trigger rather than a fixed calendar schedule.
Every engagement starts from your model's baseline and is calibrated against your production data — this is the shape a tuning run takes when it's converging correctly.
DDP, FSDP, and DeepSpeed ZeRO across multiple GPUs to compress training cycles for large-parameter models without sacrificing convergence stability.
Post-training quantization (INT8/INT4), structured pruning, and distillation to hit your latency and cost targets without a large accuracy trade-off.
Structured audits of model outputs across sensitive attributes, with counterfactual testing before a model is cleared for production.
Data anonymization, encrypted storage and transit, and access-controlled training environments for regulated or sensitive datasets.
Real-time drift and quality tracking in production, with alerting thresholds tied to your business metric, not just model loss.
Models tuned on regulatory filings and internal policy language to reduce false positives in AML and fraud-review workflows.
Fine-tuning on de-identified clinical notes for summarization and coding support, under an advisory-only, human-reviewed workflow.
Tuning on product catalogs and query logs so search relevance and recommendations reflect actual customer intent, not generic embeddings.
Instruction tuning plus RLHF on historical support transcripts to keep responses on-brand and reduce escalation rates.
A defined dataset, base model, and target metric, delivered as a tuned, benchmarked model with a handover report. Best for a single, well-specified task.
Ongoing tuning, monitoring, and retraining as your data and requirements evolve, with a dedicated point of contact and monthly evaluation reports.
Our ML engineers work inside your existing infrastructure and tooling alongside your team, for organizations building long-term in-house AI capability.
Answer honestly about your data and constraints — this points you to one of the eight methodologies above, before you talk to us.
It depends on the method: full fine-tuning generally needs thousands of labeled examples to avoid overfitting, while parameter-efficient methods like LoRA can produce useful results from a few hundred. Domain adaptation on unlabeled text can use a much larger, less curated corpus.
A fixed-scope project with clean, ready data usually runs four to eight weeks from data collection through evaluation. Timelines extend when labeling, domain adaptation, or multi-round RLHF are involved.
We use regularization against the base model's outputs, held-out evaluation on general tasks alongside the target task, and parameter-efficient methods where full retraining isn't necessary — all checked at the evaluation checkpoint before deployment.
Yes. We regularly train inside a client's own cloud environment or on-premises GPU cluster, particularly for regulated data that can't leave your infrastructure.
We set up drift and quality monitoring against production traffic, with a defined threshold for when retraining is triggered, rather than leaving the model to degrade silently between manual reviews.