A general model knows everything about everything. It knows nothing about you.
We fine-tune foundation models on your proprietary data (your documents, conversations, domain terminology, and business logic), building a model that performs at expert level on the tasks that actually matter for your company.
Off-the-shelf models hit a ceiling for serious business applications.
General models hallucinate on your specifics
GPT-4, Claude, Gemini: all trained on the internet. They know a lot about general concepts, but nothing about your product documentation, your underwriting criteria, your legal framework, or your customer communication standards. When you put a general model on domain-specific tasks, you get confident outputs that are partially or entirely wrong. The more specialized your domain, the worse the gap gets.
Prompt engineering has a hard limit
When a general model underperforms, the instinct is to fix it with better prompts. Prompting can get you surprisingly far, but it can't make up for a model that fundamentally lacks domain knowledge. You can describe your underwriting rules in a system prompt, but a model that has never seen underwriting data will still miss edge cases. If your prompt engineering keeps growing in complexity, that's a sign you need a different approach, not a longer prompt.
RAG alone doesn't solve consistency or style
Retrieval-augmented generation is a strong pattern for grounding outputs in your documents, but retrieval has its own failure modes: missed context, over-retrieval noise, documents that exist but aren't formatted for retrieval. RAG doesn't change how a model reasons or writes. If your business needs outputs with a specific analytical style, a defined voice, or a structured format that has to be 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 of it leaves 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. A fine-tuned model 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.
Data in. Domain-expert model out.
A four-phase process covering everything from raw data to a deployed, evaluated model running in your environment.
Data Audit & Preparation
Weeks 1-2
Fine-tuning quality is determined before a single training step runs. We start by auditing your available data: what you have, how much of it, what format it's in, and how much is relevant to the target task. We then design the training data schema (how examples should be structured to teach the model what you need it to learn) and begin the preparation pipeline: deduplication, quality filtering, formatting into instruction-response pairs, and handling sensitive data that needs redaction or anonymization. For most clients, this phase turns up unexpected 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
Model Selection & Training Pipeline
Weeks 2-4
We pick the right base model for your use case. This isn't a default choice. It depends on your task type, latency requirements, deployment environment, licensing constraints, and dataset size. We evaluate candidates across open-weight models (Llama 3, Mistral, Qwen, Phi) and commercially licensed bases where appropriate. Then we configure the training pipeline: fine-tuning method (full fine-tune, LoRA, QLoRA, DPO depending on data volume and performance targets), hyperparameter configuration, training hardware setup, and checkpoint management. Initial training runs get evaluated early to catch overfitting, underfitting, or data quality issues before committing full training compute.
Deliverable: Model selection rationale document, training configuration, and initial training run results
Evaluation & Iteration
Weeks 4-6
A model that performs well on training data but fails on real inputs isn't trained. It's overfit. We build an evaluation framework specific to your task: a held-out test set drawn from your actual data distribution, automated metrics appropriate to the output type (exact match, ROUGE, BLEU, task-specific scorers), and human evaluation on a sample of outputs where automated metrics don't capture quality well enough. We iterate on the training data, the fine-tuning method, or both until the model meets defined performance thresholds. Every change is evaluated against the same benchmark set so we know exactly what improved and by how much.
Deliverable: Evaluation framework with benchmark results, comparison against baseline (general model), and iteration log
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 inference efficiency), serving infrastructure setup (managed endpoint on your cloud provider, on-premise GPU, or edge deployment), API design and documentation so your engineering team can integrate it, latency and throughput benchmarking, and monitoring to track inference quality over time. We also document the retraining process so your team can run future fine-tuning cycles on their own or with minimal outside help.
Deliverable: Deployed model endpoint, integration documentation, performance benchmarks, and retraining runbook
Data Audit & Preparation
Weeks 1-2
Fine-tuning quality is determined before a single training step runs. We start by auditing your available data: what you have, how much of it, what format it's in, and how much is relevant to the target task. We then design the training data schema (how examples should be structured to teach the model what you need it to learn) and begin the preparation pipeline: deduplication, quality filtering, formatting into instruction-response pairs, and handling sensitive data that needs redaction or anonymization. For most clients, this phase turns up unexpected 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
Model Selection & Training Pipeline
Weeks 2-4
We pick the right base model for your use case. This isn't a default choice. It depends on your task type, latency requirements, deployment environment, licensing constraints, and dataset size. We evaluate candidates across open-weight models (Llama 3, Mistral, Qwen, Phi) and commercially licensed bases where appropriate. Then we configure the training pipeline: fine-tuning method (full fine-tune, LoRA, QLoRA, DPO depending on data volume and performance targets), hyperparameter configuration, training hardware setup, and checkpoint management. Initial training runs get evaluated early to catch overfitting, underfitting, or data quality issues before committing full training compute.
Deliverable: Model selection rationale document, training configuration, and initial training run results
Evaluation & Iteration
Weeks 4-6
A model that performs well on training data but fails on real inputs isn't trained. It's overfit. We build an evaluation framework specific to your task: a held-out test set drawn from your actual data distribution, automated metrics appropriate to the output type (exact match, ROUGE, BLEU, task-specific scorers), and human evaluation on a sample of outputs where automated metrics don't capture quality well enough. We iterate on the training data, the fine-tuning method, or both until the model meets defined performance thresholds. Every change is evaluated against the same benchmark set so we know exactly what improved and by how much.
Deliverable: Evaluation framework with benchmark results, comparison against baseline (general model), and iteration log
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 inference efficiency), serving infrastructure setup (managed endpoint on your cloud provider, on-premise GPU, or edge deployment), API design and documentation so your engineering team can integrate it, latency and throughput benchmarking, and monitoring to track inference quality over time. We also document the retraining process so your team can run future fine-tuning cycles on their own or with minimal outside help.
Deliverable: Deployed model endpoint, integration documentation, performance benchmarks, and retraining runbook
Model, infrastructure, and the knowledge to maintain it.
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 covering base model evaluation and fine-tuning method
- Trained model checkpoint with complete 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 post-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
Fine-tuning is one layer. Here's what sits above and below it.
This engagement covers the model itself: data, training, evaluation, and deployment. Adjacent engineering and strategy work is scoped separately.
Application-layer product development
Fine-tuning produces a model. Building the user-facing application, workflow integrations, or internal tools that consume the model is a separate product development engagement.
Data infrastructure and pipelines
We prepare the training dataset from data you provide. If your underlying data is unstructured, siloed, or requires significant extraction and pipeline work before it's usable for training, that infrastructure build is scoped separately.
Ongoing model maintenance and retraining
The engagement delivers a trained model and a retraining runbook. Continuous retraining cycles as new data accumulates, model monitoring in production, and ongoing performance tuning are covered under a retainer arrangement priced separately.
Is this the right fit?
Right for you if
- You have a specific, high-value 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 operate 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 a retrieval layer can't fix the problem.
- You're building a differentiated AI product and your proprietary data is your moat, but only if you've built 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 high-quality examples. If you're below that threshold, a RAG or prompting approach 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. It's only worth it when general models are genuinely insufficient for your task.
- You need a prototype or proof-of-concept, not a model ready for production. Fine-tuning is an investment in a system that has to perform reliably at scale.
What this looks like in practice.
Problem
A mid-market NBFC needed to automate the 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 the outputs without full human review, which eliminated the efficiency gain.
What we did
Fine-tuned a Llama 3 8B model on 3,400 labeled document extraction examples drawn from the client's historical applications. The training data was prepared with careful attention to 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 improved from 71% to 94% compared to a prompted general model on the same evaluation set. The team reduced human review to a spot-check process covering 15% of documents, freeing two analyst hours per 100 applications processed.
Problem
A legal tech startup had built a contract review product on top of 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.
What we did
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. The evaluation framework included blind comparison tests where lawyers assessed outputs from the fine-tuned model versus the prior 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 with the new model at a 40% price premium. Three law firms that had declined during the trial period signed contracts within sixty days of relaunch.
Problem
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 specific terminology, abbreviation conventions, and clinical logic used by doctors at its partner hospital network. A general model produced notes that were grammatically correct but clinically imprecise, requiring significant physician editing before the tool reduced documentation time.
What we did
Prepared a training dataset from 1,600 de-identified conversation-to-note pairs, working with clinical staff to ensure the examples reflected the documentation standards and abbreviation conventions of the specific specialty and hospital context. Fine-tuned with particular attention to structured output consistency. The SOAP format had to be reliable enough that physicians could navigate the notes without scanning for structure failures.
Outcome
Physician editing time per note dropped from an average of 6.2 minutes to 1.8 minutes after deployment. The hospital's clinical informatics team verified that note completeness scores (measured against their internal audit criteria) were higher for AI-assisted notes than for unassisted notes from the same cohort of physicians.