Most fine-tuning projects ship a model that scores higher on the test set and worse in production. NKKTech ships fine-tuned LLMs that survive contact with real users — instruction-tuned, RLHF-aligned where needed, evaluated against a frozen test set you can audit, and deployed with the same observability stack that runs your core product. Llama 3.3, Mistral, Qwen, GPT/Claude via API fine-tuning, or your own pretrained base.
A complete fine-tuning engagement, not a Jupyter notebook handoff. Six capabilities every project ships with.
Most fine-tuning failures are dataset failures. We design labeling rubrics, build the labeling pipeline (in-house team or vendor), enforce inter-rater reliability ≥0.8 Cohen's kappa, and ship a dataset you can audit.
Honest tradeoff analysis across Llama 3.3, Mistral, Qwen 2.5, Phi, and the proprietary fine-tunable models. Picked against your latency budget, cost ceiling, hardware constraints, and licensing requirements.
LoRA and QLoRA for 90% of projects — same quality at 5–10× lower compute cost than full fine-tuning. We default here and only escalate to full fine-tuning when the eval shows it's necessary.
When the task needs subjective quality (writing tone, helpfulness ranking, refusal behavior), we add a preference-data collection pass and DPO or RLHF training. Direct Preference Optimization is the default — simpler and more stable than PPO-RLHF.
Every project ships a frozen eval set (50–500 cases), scoring functions for each task type, regression tracking, and a CI gate on eval scores. Same playbook we use on our own production agents.
Deployment on your stack (Bedrock, Azure OpenAI, self-hosted vLLM, Together AI), with model versioning, A/B harness, drift detection, and rollback path. We finish when the model is in production, not when training converges.
1–2 weeks. Define the task, agree on eval criteria, build the baseline (no fine-tuning, just prompt engineering + RAG). Confirm fine-tuning is actually needed.
2–4 weeks. Curate, label, and validate the training set. Quality is the bottleneck — we never shortcut this.
2–3 weeks. LoRA fine-tuning, eval-driven iteration, hyperparameter sweeps. Multiple checkpoints; pick the best by eval score, not loss curve.
1–2 weeks. Production deployment, monitoring stack, runbook, and engineering handoff to your team.
Compliance-aware customer support, financial document extraction with domain-specific terminology, internal-knowledge agents that respect data residency.
Clinical-note summarization, medical-coding assistance, HIPAA-aware agents that stay grounded in retrieved records.
Contract analysis with jurisdiction-specific terminology, case-law retrieval-and-summary, redlining assistants.
Product-specific support agents (your terminology, your features, your tone), in-app copilots that know your data model.
Equipment-manual Q&A, multilingual support for global field teams, technical-document drafting with company style.
Product-description generation in brand voice, customer-review summarization, merchandise-search query rewriting.
Fine-tuning wins when you need consistent style, format, or domain-specific reasoning patterns the base model doesn't have. RAG wins when the gap is missing knowledge. Prompt engineering wins when the gap is instruction-following on a task the model already understands. We default to prompt engineering, escalate to RAG, escalate to fine-tuning only when the eval shows it's needed. See our LLM Fine-tuning vs RAG vs Prompt Engineering guide for the decision tree.
For instruction-tuning with LoRA on a strong base model: typically 500–5,000 high-quality examples. Quality matters more than quantity. For domain-knowledge injection: usually RAG is better — fine-tuning isn't a good way to teach the model facts.
USD 40K–80K for a standard 8–12 week engagement covering dataset, training, eval, and deployment. Cost varies with dataset size (labeling is often the largest line item), base model size, and whether you need RLHF (adds 2–3 weeks). We give a fixed-fee quote after a 1-week scoping engagement.
We design for your infra from day one. If you're on AWS, we ship Bedrock-deployable or SageMaker-deployable models. Azure clients get Azure OpenAI fine-tuning or Azure ML. Self-hosted clients get vLLM or Triton deployment. We don't lock you into our preferred stack.
Frozen eval set agreed up front. We commit to a target eval score before training starts, and we don't ship until we hit it. For subjective tasks (writing quality, helpfulness), we use blinded human eval with the rubric you approve.
NKKTech delivered our LLM document processing pipeline on time and exactly on budget. The tech lead was available on Slack daily. First offshore team that actually worked the way we expected.
Tony's team understood our legacy PHP system faster than our internal team. Zero downtime migration, exactly as promised. The bilingual PM made communication seamless.
We went from 15 hours/week of manual prospecting to fully automated lead gen in 8 weeks. ROI in 60 days as Tony promised.
NKKTech delivered our LLM document processing pipeline on time and exactly on budget. The tech lead was available on Slack daily. First offshore team that actually worked the way we expected.
Last updated: · Reviewed quarterly for accuracy.
30-minute free discovery call with a senior NKKTech engineer (not a sales rep). We'll review your requirements, scope an engagement, and tell you honestly whether we're the right fit.
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