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Fine-Tuning Program for Enterprise Consulting Firm
United States
Project Overview
Webscrapping
LLM Fine-Tuning
AI Infrastructure
AI Products & Platforms
Strategy & Advisory
A global enterprise technology provider engaged SFAI Labs to improve task accuracy and consistency for a customer-facing GenAI experience. The core challenge was that a general-purpose model performed well on broad queries, but underperformed on domain-specific language, formatting requirements, and edge cases that mattered in production (high-cost hallucinations, policy violations, and inconsistent outputs).
SFAI Labs designed a confidential fine-tuning program to align the model to the organization’s domain language, response structure, and safety constraints—without exposing sensitive data or internal customer information. We established a secure data workflow, created high-signal training examples, and implemented an evaluation harness that measured gains across accuracy, refusal behavior, and format adherence.
The result was a deployable fine-tuned model and an operating system to continuously improve it: clear dataset standards, automated regression tests, and a release process that makes model upgrades predictable and auditable.
Key Takeaways
Higher Accuracy
Safer Outputs
Stable Formatting
Faster Iteration
Auditable Releases
Challenge
Baseline model behavior was inconsistent on domain tasks and edge cases, creating reliability risk for production. The team needed a way to increase task success rate and reduce unsafe or non-compliant responses while maintaining latency and controlling operational complexity.
Strategy
Use fine-tuning only where it creates durable improvements (domain language + structured outputs), and pair it with a rigorous evaluation and release process. Build a privacy-first data pipeline, define success metrics, and prevent regressions with automated tests and gated deployments.
Solution
Confidential data curation workflow (PII redaction, labeling standards, dataset versioning)
Fine-tuning dataset design (high-signal exemplars, counterexamples, hard negatives)
Evaluation harness (golden set, slicing, regression suite, error taxonomy)
Safety + compliance alignment (refusal patterns, policy constraints, format contracts)
Production rollout plan (shadow testing, gated release, monitoring + retraining loop)
Execution
Secure intake + governance (data handling rules, access boundaries, auditability)
Training set creation and iterative refinement based on error analysis
Fine-tune runs with controlled experiments (hyperparameters, dataset variants)
Automated evaluation across core tasks + edge-case slices
Release readiness: thresholds, rollback plan, and monitoring instrumentation
Results
Improved domain-task reliability via fine-tuning with measurable gains on the golden evaluation set
Increased format adherence and reduced failure modes on high-impact edge cases
Established a repeatable model-release process (dataset versioning + regression tests)
Business Value
This program reduced production risk by making model behavior more predictable, safer, and easier to maintain over time. The evaluation and release system enables continuous improvement without reintroducing regressions—supporting faster iteration cycles and higher customer trust.
Why SFAI Labs
We deliver end-to-end applied AI programs that combine model improvement (fine-tuning), rigorous evaluation, and production deployment discipline—so teams get measurable gains, not just experiments.
