Custom AI Training & Fine-Tuning
Custom AI Training & Fine-Tuning is a professional ai intelligence service delivered by Pakish.NET with end-to-end setup, quality checks, and implementation support.
Starting from
PKR 150,000
What is Custom AI Training & Fine-Tuning?
Custom fine-tuning adapts a base model to your domain using curated labeled examples, improving consistency on classification, extraction, and tone tasks where generic prompts drift. We audit whether fine-tuning is appropriate versus RAG or structured prompting, clean and split your dataset to prevent leakage, run evaluation against holdout sets, and guard against overfitting and unsafe outputs before deployment. Production serving uses the same API integration patterns as base models with versioned rollback.
Problems This Service Solves
- Prompt engineering works in demos but fails on edge phrasings in production.
- Classification labels vary run-to-run because the base model interprets instructions loosely.
- RAG retrieves correct docs but the model still formats answers inconsistently for downstream parsers.
- Expensive human review catches errors that a specialized smaller model could handle cheaply.
- Brand voice requirements are too nuanced for a single system prompt.
When This Service Is Not the Right Fit
- Knowledge-heavy Q&A where answers change weekly with documentation updates (RAG fits better).
- Datasets smaller than a few hundred high-quality examples without augmentation plan.
- Tasks requiring factual recall of rapidly changing prices or inventory without retrieval.
- Organizations unable to label or review training examples for safety and bias.
Ideal Use Cases
- Intent classification routing support tickets to specialized queues.
- Structured entity extraction from invoices, resumes, or medical intake forms.
- Consistent JSON field population from semi-structured user paste.
- Tone-aligned response generation for regulated industries with approved phrasing.
- Specialized code or SQL generation against a fixed internal schema.
What We Need From You
- Historical examples of desired input-output pairs or classification labels
- Labeling rubric or reviewer notes explaining edge cases
- List of failure modes seen with current prompt-only approach
- Acceptable accuracy target and error cost asymmetry (false positive vs false negative)
- Policy on using customer data in training and retention duration
- Compute budget ceiling for training experiments
Discovery and Implementation Stages
1. Approach selection
We compare fine-tuning, RAG, and advanced prompting on a sample set. Proceed with fine-tune only if measurable lift justifies maintenance cost.
2. Dataset audit & preparation
Duplicates removed, label inconsistencies resolved, train/validation/test splits stratified to prevent leakage from near-duplicate rows.
3. Training & evaluation cycles
Hyperparameters swept within budget. Checkpoints scored on holdout metrics and manual review of worst errors.
4. Safety review & deployment
Adversarial prompts tested. Winning checkpoint deployed behind existing API layer with monitoring for drift.
What's Included
Acceptance Criteria
- Holdout metrics meet agreed threshold vs prompt-only baseline
- Safety test suite passes without increased harmful output rate
- Deployed model integrates with existing API abstraction without client changes
- Rollback drill completed successfully in staging
- Documentation explains when to retrain vs adjust prompts
Security and Privacy Considerations
- Training data stored encrypted with access limited to project team
- PII scrubbing applied before training unless explicitly scoped otherwise
- Fine-tuned weights treated as confidential artifacts in customer-controlled storage
- Evaluation logs redact sensitive fields in shared reports
Service Decision Guide
| Decision factor | This approach | Common alternative | Notes |
|---|---|---|---|
| Approach fit analysis | Documented comparison of fine-tune vs RAG vs prompts on your sample set | Fine-tune recommended because it sounds advanced | Unnecessary fine-tunes incur retraining cost when RAG would suffice. |
| Dataset hygiene | Leakage checks, deduplication, and label consistency audit | Raw CSV uploaded directly to training job | Duplicate rows inflate metrics and fail on fresh production inputs. |
| Evaluation rigor | Holdout metrics plus worst-case manual error review | Training loss curve only | Loss curves hide catastrophic failures on minority classes. |
| Production safety | Adversarial eval and checkpoint rollback wired before traffic | Deploy latest epoch automatically | Later epochs often overfit and increase unsafe completions. |
Failure and Fallback Handling
- Production model regression triggers automatic route back to previous checkpoint
- Low-confidence classifications route to human review queue
- Training job failure preserves last good deploy; no partial weights promoted
Post-Launch Support Scope
- Monthly drift check comparing live errors to evaluation set
- Retraining trigger guidelines when new labeled volume threshold hit
- Assistance incorporating negative examples from production failures
- Optional annotation workflow design for continuous improvement
Custom AI Training & Fine-Tuning FAQs
Common questions about our AI Intelligence service.
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