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Model Finetuning

Custom domain models trained on your proprietary data for higher accuracy, lower operating cost, and outputs your business can trust.

The market's best model
still does not know your business.

General-purpose models are trained for breadth, not the operating realities of your enterprise. They do not understand your terminology, decision rules, workflow exceptions, or the output contracts your systems depend on. Teams compensate with longer prompts, more review, and brittle post-processing. Finetuning changes that equation. You get a model that reflects your institutional knowledge, performs with greater consistency, and scales across critical workflows without inflating cost or risk.

3-5x

Lower inference cost than frontier models on domain workloads

<2%

Domain hallucination rate on proprietary terminology

40%

Reduction in prompt overhead for production tasks

The problem with generic models

Prompt engineering does not create durable advantage.

If your team is relying on prompt layers to force a generic model into specialized work, you are paying a tax in latency, API spend, and operational complexity. Market leaders do not build AI programs on workarounds. They train systems on proprietary data that competitors cannot access and convert knowledge into repeatable performance.

The compounding advantage

Your proprietary data is a strategic asset.

Years of decisions, documents, approvals, and exceptions contain the operating logic of your business. Once that signal is embedded in a model, you gain faster execution, more consistent judgment, and an asset that compounds with every retraining cycle. The architecture matters. The differentiated value comes from what only your organization can teach.

Six phases. One enterprise-ready model.

We execute a disciplined, security-first pipeline from corpus design through continuous improvement, all inside your environment.

Domain Corpus Engineering
We turn fragmented enterprise data into a high-signal training corpus that reflects how your business actually operates. Clean labels, edge-case coverage, and decision history determine whether a model succeeds in production.
Base Model Selection
We align the foundation model to your security posture, latency targets, licensing constraints, and deployment strategy. The right choice creates leverage across performance, economics, and governance.
Training Pipeline
We build a controlled training pipeline for supervised finetuning, instruction tuning, and preference optimization where appropriate. Every run stays inside your environment so your IP remains protected.
Evaluation Framework
We define evaluation against the metrics your leadership team cares about: domain accuracy, policy adherence, structured output quality, and exception handling. Success is measured against business outcomes, not lab benchmarks.
Deployment Architecture
We optimize the model for throughput, latency, and cost before launch, then deploy into your VPC, on-prem estate, or hybrid stack. The result is production readiness with clear operating economics.
Continuous Improvement Loop
We instrument performance, monitor drift, and schedule retraining as your data and workflows evolve. Your model improves with the business instead of degrading after go-live.

Case Studies

The strongest finetuning programs combine proprietary data, specialized language, and workflow requirements that generic models cannot meet reliably.

01

Financial Services

Credit Underwriting Intelligence

A large financial institution trained a model on a decade of internal credit memoranda, approval decisions, and exception histories. The result was a review assistant that reflected the firm's risk philosophy, cut senior analyst effort by 70%, and improved consistency across high-volume underwriting workflows.

70% analyst time reductionLower exception rate than baseline
02

Healthcare

Clinical Documentation at Scale

A regional healthcare network finetuned a model on EHR notes, coding corrections, and documentation standards to convert physician narratives into structured, code-ready records. Accuracy reached 94% for targeted workflows while coding teams reduced manual effort by 60%.

94% ICD coding accuracy60% reduction in coding hours
03

Legal

Contract Intelligence Engine

A corporate legal team trained a domain model on more than 50,000 executed agreements, fallback positions, and policy libraries. The model surfaced non-standard clauses, identified material deviations, and compressed first-pass review from hours to seconds per document.

50k+ contract training corpusHours-to-seconds per document
04

Industrial Operations

Predictive Maintenance Reasoning

An industrial operator finetuned a model on maintenance logs, equipment manuals, telemetry patterns, and failure histories across 2,000+ assets. The system identified fault patterns in the language of the plant floor and helped teams act earlier on preventive maintenance opportunities.

2,000+ machine fleet coverageNative maintenance vocabulary
Runs entirely in your environment
Training, evaluation, and inference run within your controlled environment so sensitive data stays inside your security boundary from start to finish.
Built for inference economics
We design for enterprise unit economics, using smaller tuned models where they outperform larger general models on domain work. That means lower cost per task without sacrificing accuracy.
Output format as a first-class requirement
We train for exact outputs your systems can consume, from structured JSON to compliance-ready schemas and entity extraction formats. Reliability at the interface layer is non-negotiable.
Alignment to your policies
Model behavior is shaped around your compliance rules, decision policies, brand voice, and risk tolerance. The operating standard is yours, not a vendor default.
Versioned, auditable, reproducible
Every dataset snapshot, training configuration, and model artifact is versioned and traceable. Your team can audit changes, reproduce results, and defend decisions with confidence.
Designed for retraining
We deliver a repeatable capability, not a one-time experiment. As requirements shift, retraining becomes a managed operating motion rather than a new initiative.

Governed, auditable, and built for scale.

Your data stays under your control. Your model lifecycle is versioned and reproducible. Your operating economics are designed up front.

Guaranteed Outcomes

What enterprise teams gain when domain expertise is embedded directly into the model.

Performance & Cost

  • Domain performance that matches or exceeds frontier models at 3-5x lower cost
  • Less prompt overhead and fewer downstream workarounds
  • Reliable format compliance for automation and system integration
  • Hallucination rate under 2% on proprietary terminology

Governance & Sovereignty

  • Training data remains inside your environment throughout the lifecycle
  • Full model versioning with rollback to prior approved states
  • Reproducible training and evaluation records for audit readiness
  • Behavior aligned to your compliance policies and operating standards

Common question

Why finetuning instead of RAG?

RAG is effective when freshness of information is the primary requirement and retrieval quality is sufficient. Finetuning is the better choice when you need the model to internalize domain judgment, terminology, and output behavior. In high-value workflows such as underwriting, clinical coding, and contract review, that difference is material. Mature enterprise AI programs often deploy both: RAG for current knowledge, finetuning for depth, consistency, and control. We architect the mix around your business case.

Get a model tuned precisely for your unique workflow

Start Your ProjectSee Case Studies