Your data is your edge. We turn it into a model that understands your business.
Fine-tune foundation models on your proprietary data to build models that understand your business, domain, and customers. We handle data preparation, training, evaluation, and deployment.
The Problem
Off-the-shelf models hit a ceiling on your business work.
General models hallucinate on your specifics: GPT-4, Claude, Gemini are all trained on the internet. They know general concepts but nothing about your product documentation, underwriting criteria, legal framework, or customer communication standards. Put a general model on domain-specific tasks and you get confident outputs that are partially or entirely wrong. The more specialized your domain, the worse the gap.
Prompt engineering has a hard limit: When a general model underperforms, the instinct is better prompts. Prompting can get you surprisingly far, but it can't compensate for a model that lacks domain knowledge. You can describe underwriting rules in a system prompt, but a model that has never seen underwriting data will still miss edge cases. If your prompts keep growing in complexity, you need a fundamentally different approach.
RAG alone doesn't solve consistency or style: Retrieval-augmented generation grounds outputs in your documents, but retrieval has its own failure modes: missed context, over-retrieval noise, documents that aren't formatted for retrieval. RAG doesn't change how a model reasons or writes. If you need outputs with a specific analytical style, a defined voice, or a structured format consistent across thousands of outputs, retrieval alone won't get you there.
Every API call is a data leakage decision: Routing sensitive business data through a third-party API creates legal, compliance, and competitive risk. Customer financial data, proprietary contracts, internal communications, patient records, strategic documents all leave your environment every time you make an inference call. For regulated industries or companies with real IP concerns, running a fine-tuned model in your own infrastructure isn't optional.
Competitors who fine-tune compound the advantage over time: A fine-tuned model trained on your data today outperforms a general model. One retrained on accumulating proprietary data next year is much better and much harder for a competitor to copy. The longer you wait to build the training infrastructure, the larger the dataset gap grows. Your data is accumulating whether or not you're using it.
Our Approach
Data in. Domain-expert model out. Four phases from raw data to a deployed, evaluated model running in your environment.
Phase 1 — Data Audit & Preparation (Weeks 1-2): Fine-tuning quality is determined before a single training step runs. We audit your available data for volume, format, and relevance to the target task. Then we design the training data schema and build the preparation pipeline: deduplication, quality filtering, formatting into instruction-response pairs, and redaction of sensitive data. This phase often surfaces data quality issues that would have hurt training results if missed. Deliverable: Data audit report with quality assessment, prepared training dataset, and data schema documentation
Phase 2 — Model Selection & Training Pipeline (Weeks 2-4): We pick the right base model based on task type, latency requirements, deployment environment, licensing, and dataset size. We evaluate candidates across open-weight models (Llama 3, Mistral, Qwen, Phi) and commercial bases where appropriate. Then we configure the training pipeline: fine-tuning method (full fine-tune, LoRA, QLoRA, or DPO), hyperparameters, hardware setup, and checkpoint management. Early training runs catch overfitting or data quality issues before committing full compute. Deliverable: Model selection rationale document, training configuration, and initial training run results
Phase 3 — Evaluation & Iteration (Weeks 4-6): A model that scores well on training data but fails on production inputs is overfit. We put together a task-specific evaluation framework with a held-out test set from your actual data distribution, automated metrics (exact match, ROUGE, BLEU, task-specific scorers), and human evaluation where automated metrics fall short. We iterate on training data and method until defined performance thresholds are met. Every change is benchmarked so we know exactly what improved. Deliverable: Evaluation framework with benchmark results, comparison against baseline (general model), and iteration log
Phase 4 — Deployment & Integration (Weeks 6-8): A trained model that isn't in production is a research project. We handle the full deployment path: model quantization where it helps efficiency, serving infrastructure (managed endpoint, on-premise GPU, or edge), API design and documentation, latency and throughput benchmarking, and monitoring for inference quality over time. We document the retraining process so your team can run future fine-tuning cycles independently. Deliverable: Deployed model endpoint, integration documentation, performance benchmarks, and retraining runbook
Deliverables
Data & Training (Weeks 1-4)
- Data audit report with quality assessment and gap analysis
- Prepared training and evaluation datasets with full schema documentation
- Model selection rationale with base model evaluation and fine-tuning method
- Trained model checkpoint with full training configuration and reproducibility documentation
Evaluation & Deployment (Weeks 4-8)
- Evaluation framework with benchmark results and comparison against general model baseline
- Deployed inference endpoint with latency and throughput benchmarks
- API documentation for engineering integration
- Monitoring setup for inference quality tracking after deployment
Handoff
- Retraining runbook so your team can run future fine-tuning cycles with new data
- Model card documenting performance characteristics, known limitations, and recommended use cases
Who This Is For
Right for you if: You have a task where a general LLM gives inconsistent or inaccurate outputs, and you have enough proprietary data (documents, conversations, records) to train a better model.. You're in a regulated or IP-sensitive environment where routing data through third-party APIs creates compliance exposure. You need a model you can run in your own infrastructure.. You've invested in prompt engineering and RAG and hit a ceiling. Outputs are still inconsistent in style, accuracy, or format, and retrieval can't fix the problem.. You're building a differentiated AI product and your proprietary data is your moat, but only if you build the training infrastructure to use it..
Not right if: You don't have enough proprietary data to fine-tune on. The minimum varies by task, but general-purpose text tasks typically need hundreds to thousands of quality examples. If you're below that, RAG or prompting makes more sense right now.. Your use case is general enough that a well-prompted foundation model already meets your quality bar. Fine-tuning is a real investment, only worth it when general models genuinely fall short for your task.. You need a prototype or proof-of-concept. Fine-tuning is an investment in a production system that has to perform reliably at scale..
Use Cases
Financial Services, Lending: A mid-market insurance broker needed to automate preliminary review of loan application documents: income verification letters, bank statements, and credit bureau reports. A general LLM extracted information inconsistently and missed entity types specific to local financial documents. Error rates were too high for the team to trust outputs without full human review, which eliminated the efficiency gain. — Fine-tuned a Llama 3 8B model on 3,400 labeled document extraction examples from the client's historical applications. Training data preparation focused on edge cases: handwritten notes, regional language terms, non-standard date formats, and document variations across different lenders and bureaus. Evaluated on a held-out set of 400 documents not seen during training.. Outcome: Field-level extraction accuracy went from 71% to 94% compared to a prompted general model on the same evaluation set. The team reduced human review to spot-checks on 15% of documents, freeing two analyst hours per 100 applications processed.
Legal Tech, Contract Review Platform: A legal tech startup had built a contract review product on a general LLM. The model flagged clauses inconsistently across contract types and produced explanations that lawyers found imprecise. Sales cycles were stalling because law firm partners couldn't trust the outputs enough to recommend the tool to associates. — Worked with the client's legal team to label 2,800 contract clause examples across six clause categories and five risk levels. Fine-tuned using DPO (Direct Preference Optimization) to align the model's risk assessments with senior lawyer judgments on the same clauses. Evaluation included blind comparison tests where lawyers assessed outputs from the fine-tuned model versus the general model without knowing which was which.. Outcome: Lawyers preferred fine-tuned model outputs in 81% of blind comparisons. The product team relaunched the review feature at a 40% price premium. Three law firms that had declined during the trial period signed within sixty days of relaunch.
Healthcare, Clinical Documentation: A health-tech company building a clinical documentation assistant needed a model that could transcribe physician-patient conversations and structure them into SOAP notes using the terminology, abbreviation conventions, and clinical logic used at its partner hospital network. A general model produced notes that were grammatically correct but clinically imprecise, requiring heavy physician editing before the tool saved any documentation time. — Prepared a training dataset from 1,600 de-identified conversation-to-note pairs, working with clinical staff to make sure examples reflected the documentation standards and abbreviation conventions of the specialty and hospital context. Fine-tuned with focus on structured output consistency. The SOAP format had to be reliable enough that physicians could read notes without scanning for structure failures.. Outcome: Physician editing time per note dropped from 6.2 minutes to 1.8 minutes after deployment. The hospital's clinical informatics team confirmed that note completeness scores (measured against their internal audit criteria) were higher for AI-assisted notes than unassisted notes from the same cohort of physicians.
Results
What domain expertise in a model looks like.
Legal Technology, Contract Review: 81% lawyer preference in blind evaluation, 40% price premium on relaunch. A legal tech startup had built its core contract review feature on a general LLM and was losing enterprise deals because senior lawyers didn't trust the model's outputs. We fine-tuned a replacement model on 2,800 labeled clause examples using DPO to align outputs with how experienced lawyers reason about risk in contract language. Lawyers preferred the fine-tuned model's outputs over the general model's in more than four out of five blind comparisons. The startup relaunched the feature at a higher price point and converted three law firms that had previously passed. The fine-tuned model became the central differentiator in their next funding round pitch.
Frequently Asked Questions
How much data do I need to fine-tune a model?
Depends on the task. For structured extraction or classification, 500 to 2,000 quality labeled examples is often enough. For generative tasks where style, reasoning, or judgment matters (clinical notes, legal analysis, financial commentary), you typically need 2,000 to 10,000 examples to see consistent improvement over a prompted general model. We assess your data volume and quality in week one and tell you whether you're above or below the threshold.
What base models do you work with?
We work across open-weight models: Llama 3 (8B and 70B), Mistral and Mixtral, Qwen 2.5, Phi-3 and Phi-4, and Gemma 2. We also work with commercially licensed bases where the use case warrants it. Selection depends on your task type, deployment environment, latency requirements, and dataset size. We document the rationale so your team understands the tradeoffs.
What fine-tuning methods do you use (LoRA, QLoRA, full fine-tune)?
Depends on your dataset size, performance requirements, and compute budget. LoRA and QLoRA are efficient for most mid-size datasets and let you fine-tune on smaller hardware with good results. Full fine-tuning gives stronger performance for large datasets or tasks requiring deep behavioral change, but needs more compute. DPO (Direct Preference Optimization) is our go-to when you have human preference data (rankings or comparisons) rather than input-output pairs. We explain the method choice in plain terms in the training configuration document.
Where does the model get deployed, and can it run in our own infrastructure?
Yes. We design deployment for your environment. Options include managed endpoints on AWS, GCP, or Azure (SageMaker, Vertex AI, or Azure ML), self-hosted on your own GPU infrastructure (on-premise or private cloud), and edge deployment for latency-sensitive applications. For regulated industries where data sovereignty is a hard constraint, on-premise or private cloud is the default. We handle the full deployment configuration and provide documentation for your infrastructure team.
How do you evaluate whether the fine-tuned model is actually better?
We build a task-specific evaluation framework before training begins: held-out test set from your actual data distribution, automated metrics appropriate to the output type, and human evaluation on a sample where automated metrics don't fully capture quality. We benchmark the fine-tuned model against a prompted general model on the same evaluation set so you get a rigorous before-and-after comparison with statistical confidence intervals.
What happens when we get more data and want to retrain?
We deliver a retraining runbook at handoff: a documented process your team can follow to run future fine-tuning cycles with new data using the same configuration and evaluation framework. For clients who want us to manage ongoing retraining, we offer a retainer covering quarterly or triggered retraining cycles, evaluation, and model updates. The goal is a model that gets better over time as your proprietary dataset grows.
