Capability brief · Fine-TuningCapability 14 of 14

Turn General-Purpose Language Models into Domain-Expert Systems Trained on Your Data

Neume Labs builds fine-tuned LLMs that speak your industry's language -- from legal clause interpretation to medical coding taxonomy -- delivering measurably higher accuracy, lower inference cost, and full data sovereignty throughout the training lifecycle.

Book a call with an engineer How it’s built

40%

Average accuracy improvement over base models on domain-specific tasks

Reduction in per-query inference cost versus prompt-engineered GPT-4 class models

95%+

Precision on specialized terminology extraction after fine-tuning

6 wks

Typical time from data audit to production-ready fine-tuned model

01Overview

Custom LLM Fine-Tuning

What it is

Custom LLM fine-tuning is the process of adapting a pre-trained foundation model to a specific domain, task, or organizational vocabulary by training it on curated, high-quality datasets drawn from your own operations. Unlike prompt engineering (which constrains a general model at inference time) or RAG (which retrieves external context at query time), fine-tuning fundamentally reshapes the model's internal weights so that domain knowledge is embedded directly into the model itself. The result is a smaller, faster, cheaper-to-run model that outperforms far larger general-purpose models on the tasks that matter to your business.

Why it matters

Base foundation models are trained on internet-scale data that skews heavily toward general knowledge. They routinely hallucinate on industry-specific terminology, misclassify domain-specific intent, and fail to follow the precise output formats that downstream systems require. For enterprises operating in regulated industries -- legal, healthcare, insurance, financial services -- these failures are not merely inconvenient; they create compliance risk, erode user trust, and block production deployment. Fine-tuning closes the gap between what a general model knows and what your business actually needs it to do, converting AI from a promising prototype into a reliable operational system.

How Neume does it differently

Neume Labs treats fine-tuning as an end-to-end engineering discipline, not a one-off experiment. We begin with a rigorous data audit that identifies high-value training signal within your existing document corpus, operational logs, and expert annotations. Our training pipeline incorporates human-in-the-loop evaluation at every checkpoint, ensuring that domain experts validate model behavior before it reaches production. We deploy continuous evaluation harnesses that monitor model drift, track per-class accuracy against golden test sets, and trigger retraining when performance degrades. The result is not a static model artifact -- it is a living system that improves as your data grows.

02Core capabilities

What this system can do.

Domain Data Curation & Synthesis

We audit your existing data assets -- contracts, claims, clinical notes, transaction logs, correspondence -- and engineer training datasets that maximize signal density. This includes deduplication, PII redaction, annotation schema design, and synthetic data generation to fill coverage gaps in underrepresented categories.

Task-Specific Model Architecture Selection

Not every task requires a 70B parameter model. We evaluate your latency, accuracy, and cost constraints to select the right base model -- from efficient 7B-parameter open-source models for high-throughput classification to larger models for nuanced generative tasks -- and apply the appropriate fine-tuning technique (full fine-tune, LoRA, QLoRA, or adapter-based approaches).

Fine-Tune vs. RAG vs. Prompt Engineering Decision Framework

Before any training begins, we run a structured evaluation to determine whether fine-tuning is the right approach for each use case. Prompt engineering suits tasks where the model already has the knowledge but needs formatting guidance. RAG is optimal when the knowledge base changes frequently and retrieval latency is acceptable. Fine-tuning wins when you need consistent terminology adherence, lower inference cost at scale, or tasks where context windows cannot accommodate the necessary reference material.

Continuous Evaluation & Regression Testing

Every fine-tuned model ships with a custom evaluation harness built from domain-expert-annotated golden test sets. We measure precision, recall, F1, and task-specific metrics (e.g., clause extraction accuracy, ICD-10 code match rate) across every training run. Automated regression tests catch performance degradation before it reaches production.

Privacy-Preserving Training Infrastructure

Training data never leaves your security perimeter unless you explicitly choose otherwise. We support on-premise training on customer-managed GPU infrastructure, VPC-isolated cloud training environments, and federated learning approaches for organizations that cannot centralize sensitive data. All training artifacts are encrypted at rest and in transit.

Model Governance & Versioning

Every model version is tracked with full lineage -- training data snapshot, hyperparameters, evaluation scores, and human approval records. We implement model registries with promotion gates (dev, staging, production), automated rollback capabilities, and audit trails that satisfy regulatory examination requirements in financial services, healthcare, and legal contexts.

03Architecture

How it’s built.

The fine-tuning architecture operates as a four-layer pipeline: data curation prepares high-quality training signal, the training layer adapts model weights under controlled conditions, the evaluation layer validates performance against domain-specific benchmarks, and the deployment layer serves the model with monitoring and governance guardrails. Each layer is independently scalable and auditable.

Data Curation Layer

Ingests raw enterprise data, applies cleaning and normalization, performs PII detection and redaction, and produces annotated training datasets in standardized formats. Includes synthetic data generation to address class imbalance and coverage gaps.

Document ingestion pipeline (PDF, DOCX, HL7, EDI, structured DB exports)
PII detection and redaction engine (regex, NER-based, and LLM-assisted)
Annotation management platform with inter-annotator agreement tracking
Synthetic data generator for underrepresented categories
Data versioning and lineage tracking (DVC-compatible)
Quality scoring module that flags low-signal or contradictory examples

Training & Adaptation Layer

Executes fine-tuning runs across selected base models using appropriate techniques (full fine-tune, LoRA, QLoRA, adapter fusion). Manages hyperparameter search, distributed training orchestration, and checkpoint management across on-premise or cloud GPU infrastructure.

Base model registry (open-source and commercial model support)
LoRA / QLoRA adapter training framework
Distributed training orchestrator (multi-GPU, multi-node)
Hyperparameter optimization engine (grid search, Bayesian)
Checkpoint manager with automatic evaluation triggers
Cost estimator for training compute budgets

Evaluation & Validation Layer

Runs trained models against golden test sets, computes domain-specific metrics, performs adversarial testing for edge cases, and generates human-readable evaluation reports. Domain experts review model outputs at defined checkpoints before promotion.

Golden test set manager with stratified sampling
Automated metric computation (precision, recall, F1, BLEU, ROUGE, custom)
Adversarial test generator for edge-case robustness
Human-in-the-loop evaluation interface for domain expert review
A/B comparison framework (fine-tuned vs. base vs. RAG baselines)
Evaluation report generator with per-class breakdowns

Deployment & Governance Layer

Serves fine-tuned models via scalable inference endpoints, monitors production performance against baseline metrics, manages model versioning and rollback, and maintains audit trails for regulatory compliance.

Model serving infrastructure (vLLM, TGI, or managed endpoints)
Production monitoring dashboard (latency, throughput, accuracy drift)
Model registry with promotion gates (dev / staging / production)
Automated rollback triggers on performance degradation
Audit trail and compliance reporting module
API gateway with rate limiting, authentication, and usage metering

Integration approach

Fine-tuned models integrate into existing enterprise workflows through RESTful APIs that mirror the interface of the base models they replace -- allowing organizations to swap in domain-specific models without modifying downstream application code. We provide OpenAI-compatible API endpoints for drop-in replacement, gRPC endpoints for low-latency internal services, and batch processing interfaces for high-volume offline workloads. The deployment layer connects to existing observability stacks (Datadog, Splunk, CloudWatch) and identity providers (Okta, Azure AD) for seamless enterprise integration.

04Cross-industry deployments

Fine-Tuning in production.

Deployment 01Legal ServicesContract Clause Extraction & Classification

The problem

A mid-market law firm reviews 2,000+ commercial contracts per quarter during M&A due diligence. Base LLMs misclassify non-standard indemnification clauses 30% of the time and fail to recognize jurisdiction-specific liability language, forcing associates to manually verify every AI-flagged clause.

96% clause classification accuracy (up from 68% with base model); 70% reduction in associate review hours per deal

How it works: Neume fine-tunes a model on 50,000+ annotated clause examples drawn from the firm's historical contract corpus, covering 45 clause types across 12 contract categories. The model learns the firm's specific taxonomy, including non-standard clause variants that general models have never encountered. Training data includes partner-annotated edge cases where clause boundaries are ambiguous or where multiple clause types overlap.
Outcome: Clause extraction accuracy increases from 68% to 96%. Associates shift from full-document review to exception-only review, reducing per-deal review time by 70%. The firm can profitably offer fixed-fee due diligence engagements for the first time.

Deployment 02HealthcareAutomated Medical Coding (ICD-10 / CPT)

The problem

A regional health system processes 400,000 clinical encounters annually. Medical coders assign ICD-10 and CPT codes manually, achieving 88% first-pass accuracy. The 12% error rate triggers claim denials, delayed reimbursement, and compliance audit exposure. Coder shortages extend turnaround to 5+ days.

95% first-pass coding accuracy; 60% reduction in claim denials; same-day turnaround for 80% of encounters

How it works: Neume fine-tunes a model on 3 years of the health system's coded encounters, including physician notes, operative reports, and the corresponding code assignments validated by certified coders. The model learns institution-specific documentation patterns, physician shorthand, and the nuanced relationships between clinical language and the 70,000+ ICD-10 codes. A human-in-the-loop review queue handles low-confidence predictions.
Outcome: First-pass coding accuracy reaches 95%, reducing denial rates by 60%. Coding turnaround drops from 5 days to same-day for 80% of encounters. Certified coders focus exclusively on complex cases and audit review rather than routine coding.

Deployment 03InsuranceClaims Triage & Coverage Determination Language

The problem

A P&C carrier receives 15,000 first-notice-of-loss (FNOL) submissions monthly. Base LLMs cannot reliably distinguish between covered and excluded perils in policy-specific language, misinterpreting endorsements, sublimits, and named-peril exclusions. Adjusters spend 40% of their time on claims that should have been auto-routed.

92% coverage determination accuracy; 55% reduction in adjuster time on routine claims; 48hr to 4hr cycle time improvement

How it works: Neume fine-tunes a model on the carrier's complete policy form library (ISO and proprietary), historical claims adjudication decisions, and adjuster notes. The model learns to map FNOL language to specific policy provisions, identify applicable endorsements, and flag coverage questions that require human adjuster judgment. Training includes 5 years of coverage dispute outcomes to calibrate the model's confidence thresholds.
Outcome: Automated coverage determination accuracy reaches 92% on standard claims. Adjuster time on routine claims drops by 55%. Average cycle time from FNOL to first contact decreases from 48 hours to 4 hours for auto-triaged claims.

Deployment 04Financial ServicesRegulatory Filing Analysis & Compliance Extraction

The problem

A mid-size bank's compliance team manually reviews 200+ regulatory updates monthly from the Fed, OCC, CFPB, and state regulators. Analysts spend 3-4 days per update determining which internal policies, procedures, and controls are affected. Base LLMs lack the precision to map regulatory language to the bank's specific control framework and frequently miss cross-references between related regulations.

89% accuracy on control-to-regulation mapping; 3-4 day process compressed to same-day draft assessment

How it works: Neume fine-tunes a model on the bank's complete policy library, control matrices, and 3 years of compliance impact assessments. The model learns the bank's internal control taxonomy, maps incoming regulatory language to affected policies, and generates draft impact assessments with specific control references. Training data includes compliance officer annotations that capture the reasoning behind control-to-regulation mappings.
Outcome: Regulatory change impact assessments are generated in hours instead of days, with 89% of control mappings matching compliance officer determinations. The compliance team shifts from reactive analysis to proactive risk identification.

Deployment 05ManufacturingTechnical Documentation & Maintenance Procedure Generation

The problem

A heavy equipment manufacturer maintains 12,000+ service procedures across 400 product models. Technical writers spend 6-8 weeks creating new procedures for each product release. Base LLMs generate plausible-sounding but technically inaccurate maintenance steps, confuse torque specifications across models, and fail to reference the correct part numbers from the manufacturer's proprietary catalog.

93% technical accuracy on generated procedures; 80% reduction in documentation cycle time; 97% part number accuracy

How it works: Neume fine-tunes a model on the manufacturer's complete technical documentation library, engineering specifications, parts catalogs, and field service reports. The model learns precise technical vocabulary, correct part-number-to-procedure associations, and the manufacturer's documentation style guide. Training includes engineer-validated corrections to ensure safety-critical procedures are never hallucinated.
Outcome: Draft procedure generation time drops from 6-8 weeks to 1 week per product release. Technical accuracy on generated procedures reaches 93%, with engineers reviewing only flagged sections rather than full documents. Part number accuracy exceeds 97%.

Deployment 06Commercial Real EstateLease Abstraction & Portfolio-Specific Language Modeling

The problem

A REIT managing 500+ commercial properties processes lease abstracts for rent escalation tracking, CAM reconciliation, and renewal management. Base LLMs misinterpret non-standard rent escalation formulas, confuse gross and net lease structures, and fail to extract co-tenancy clauses that trigger rent abatement. Manual abstraction takes 2-3 hours per lease and error rates average 15%.

96% abstraction accuracy (up from 85%); 20 minutes per lease (down from 2-3 hours); real-time portfolio-wide visibility

How it works: Neume fine-tunes a model on the REIT's historical lease corpus (8,000+ executed leases), including the corresponding validated abstracts and the portfolio team's annotation of complex provisions. The model learns the REIT's specific abstraction schema, handles regional lease language variations, and correctly interprets mathematical formulas embedded in rent escalation clauses.
Outcome: Lease abstraction accuracy improves from 85% to 96%. Abstraction time drops from 2-3 hours to 20 minutes per lease, with analysts reviewing only AI-flagged exceptions. The portfolio team gains real-time visibility into upcoming escalations and renewal deadlines across the entire portfolio.

05Comparison

Why not off the shelf?

Using Base Foundation Models Directly (GPT-4, Claude, etc.)

Limitation

Base models lack domain-specific vocabulary precision, hallucinate on specialized terminology, require lengthy prompts that increase per-query cost, and cannot enforce consistent output formats across thousands of queries. They also send all data to third-party APIs, creating data sovereignty concerns for regulated industries.

Neume advantage

Fine-tuned models embed domain knowledge directly into model weights, eliminating the need for verbose prompts, reducing per-query inference cost by 5-8x, and achieving 30-40% higher accuracy on domain-specific tasks. Models can run on your own infrastructure, keeping sensitive data within your security perimeter.

Retrieval-Augmented Generation (RAG) Alone

Limitation

RAG depends on retrieval quality -- if the relevant document chunk is not retrieved, the model cannot use that knowledge. RAG adds latency from the retrieval step, struggles with knowledge that cannot be easily chunked (e.g., learned patterns across thousands of examples), and still relies on a base model that may misinterpret the retrieved context in domain-specific ways.

Neume advantage

Fine-tuning encodes learned patterns and domain understanding into the model itself, eliminating retrieval latency and retrieval failure modes. For tasks that require consistent application of learned rules (coding taxonomies, clause classification, terminology mapping), fine-tuning outperforms RAG. We also deploy hybrid architectures where fine-tuned models are augmented with RAG for rapidly changing knowledge bases.

Prompt Engineering & Few-Shot Learning

Limitation

Prompt engineering hits a ceiling on complex domain tasks -- context windows cannot accommodate enough examples to cover the full range of domain variation. Prompt-based approaches are fragile, requiring ongoing maintenance as model versions change. They also consume expensive input tokens on every query, making them cost-prohibitive at enterprise scale.

Neume advantage

Fine-tuning converts prompt-based knowledge into persistent model behavior, reducing input token consumption by 60-80% per query. The model consistently applies learned patterns without needing in-context examples, making it robust to edge cases that prompt engineering cannot anticipate. This translates to dramatically lower operating cost at enterprise query volumes.

Building Custom Models from Scratch

Limitation

Training a foundation model from scratch requires hundreds of millions of dollars in compute, billions of tokens of training data, and a team of ML researchers. It is economically viable only for the largest technology companies and produces a model that still requires fine-tuning for specific tasks.

Neume advantage

Fine-tuning leverages the massive general knowledge already embedded in foundation models and adapts it to your domain at a fraction of the cost. A fine-tuning engagement costs $50K-$200K and completes in 6-8 weeks, versus $10M+ and 6-12 months for pre-training. The resulting model inherits the base model's reasoning capabilities while gaining domain-specific precision.

Off-the-Shelf Vertical AI SaaS Products

Limitation

Vertical SaaS products offer fixed taxonomies and workflows that cannot adapt to your organization's specific terminology, classification schemes, or output requirements. They provide no control over the underlying model, no visibility into training data, and no ability to improve accuracy on your specific edge cases.

Neume advantage

Custom fine-tuned models are trained on your data, your taxonomy, and your edge cases. You own the model artifact and can run it on your infrastructure. As your operations evolve and new patterns emerge, the model is retrained to keep pace -- unlike SaaS products that update on the vendor's timeline, not yours.

06Implementation

What deployment looks like.

6-8 weeks for a standard engagement

01Week 1-2
02Week 3-4
03Week 5-6
04Week 7-8

01Week 1-2
data audit, annotation schema design, and training data preparation
02Week 3-4
base model selection, training runs, and hyperparameter optimization
03Week 5-6
evaluation, human review, and iteration
04Week 7-8
production deployment, monitoring setup, and knowledge transfer. Complex multi-model engagements or engagements requiring extensive data curation may extend to 10-12 weeks.

Prerequisites

Minimum viable training corpus: 5,000-50,000 domain-specific examples depending on task complexity (we help identify and curate these from existing data assets)
Access to domain experts (2-4 hours per week) for annotation review, evaluation checkpoint validation, and edge case adjudication
Defined target task with measurable success criteria (e.g., classification accuracy, extraction F1, generation faithfulness score)
GPU compute budget or access to on-premise GPU infrastructure (we provide sizing recommendations based on model and dataset scale)
Data governance approval for training data usage, including PII handling protocols and data retention policies
Identified production integration point (API endpoint, batch pipeline, or application embedding) with latency and throughput requirements

Deliverables

Fine-tuned model artifact with full training lineage documentation (base model, dataset version, hyperparameters, training curves)
Custom evaluation harness with golden test sets, per-class metric breakdowns, and regression test suite
Production deployment package (containerized model server, API specification, monitoring dashboards)
Data curation pipeline code and annotation guidelines for ongoing training data expansion
Model governance documentation (model card, risk assessment, bias audit results, compliance artifacts)
Knowledge transfer sessions covering model retraining procedures, evaluation interpretation, and incident response
Ongoing monitoring and retraining recommendations with defined trigger thresholds

Human in the loop

Domain experts participate at three critical checkpoints: (1) annotation schema validation, where they confirm the training data taxonomy captures the distinctions that matter operationally; (2) mid-training evaluation, where they review model outputs on held-out examples and flag systematic errors before the final training run; and (3) pre-production acceptance, where they validate model behavior on realistic production scenarios and approve promotion to the production model registry. In production, low-confidence predictions are routed to human reviewers, and their corrections feed back into the training pipeline for the next model iteration.

07Security & compliance

Engineered for trust.

Training Data Privacy

All training data undergoes automated PII detection and redaction before entering the training pipeline. We support configurable redaction policies (full removal, token replacement, differential privacy noise injection) to match organizational and regulatory requirements. Data processing agreements (DPAs) are executed before any data transfer, and data provenance is tracked throughout the pipeline.

Infrastructure Isolation

Training runs execute in dedicated, single-tenant compute environments that are provisioned at engagement start and destroyed at completion. We support on-premise training on customer-managed GPU infrastructure for organizations that cannot allow data to leave their network. Cloud-based training uses VPC-isolated instances with no shared tenancy.

Model Security

Fine-tuned model weights are encrypted at rest (AES-256) and in transit (TLS 1.3). Model artifacts are stored in customer-controlled registries with access controls that mirror existing identity management policies. We implement model extraction defenses and watermarking for models served via API endpoints.

Regulatory Compliance

Our training and deployment processes are designed to satisfy examination requirements across regulated industries: SOC 2 Type II controls for the training pipeline, HIPAA-compliant data handling for healthcare training data, and financial services model risk management (SR 11-7 / OCC 2011-12) documentation for banking deployments. We produce model cards, bias audit reports, and validation documentation as standard deliverables.

Model Governance & Auditability

Every model version is tracked with immutable lineage records: the exact training data snapshot, hyperparameter configuration, evaluation metrics, and human approval decisions that produced it. Audit trails satisfy regulatory requirements for model explainability and decision traceability. Automated alerts fire when production model behavior deviates from validated baselines.

Data Retention & Deletion

Training data, intermediate artifacts, and model checkpoints follow configurable retention policies aligned with organizational data governance requirements. We support cryptographic deletion verification and provide certificates of data destruction at engagement conclusion. Customers retain full ownership of all training data and model artifacts.

08FAQ

Common questions.

How much data do we need to fine-tune a model for our domain?

The minimum viable dataset depends on task complexity. For straightforward classification tasks (e.g., document categorization into 10-20 categories), 5,000-10,000 labeled examples typically suffice. For complex extraction or generation tasks (e.g., medical coding, clause extraction), 20,000-50,000 examples produce significantly better results. We conduct a data audit at engagement start to assess your existing data assets and identify gaps. When data is scarce, we use synthetic data generation, data augmentation, and few-shot fine-tuning techniques to maximize performance from smaller datasets.

When should we fine-tune versus use RAG or prompt engineering?

Fine-tune when: your task requires consistent adherence to domain-specific terminology or taxonomies, you need lower inference costs at scale, or the knowledge the model needs cannot be effectively retrieved from documents (e.g., learned patterns across thousands of examples). Use RAG when: the knowledge base changes frequently (daily/weekly), the relevant information can be cleanly chunked and retrieved, and retrieval latency is acceptable. Use prompt engineering when: the base model already has adequate knowledge, you need rapid iteration without training infrastructure, or the task is simple enough that a few examples in the prompt achieve target accuracy. We often deploy hybrid architectures that combine fine-tuning with RAG.

How do we prevent the fine-tuned model from losing the base model's general capabilities?

Catastrophic forgetting -- where fine-tuning on domain data degrades general capabilities -- is a well-understood challenge that we mitigate through several techniques. We use parameter-efficient fine-tuning methods (LoRA, QLoRA) that modify only a small subset of model weights, preserving the base model's general knowledge. We include a calibrated mix of general-purpose examples in the training data to maintain broad capabilities. We run regression tests against general benchmarks alongside domain-specific evaluations. And for use cases that require both domain expertise and strong general reasoning, we deploy adapter-based architectures that can blend domain-specific and general capabilities at inference time.

What happens when our domain evolves and the model needs updating?

We design every fine-tuning engagement for continuous improvement, not one-time delivery. The data curation pipeline we build is reusable -- as new examples accumulate from production usage and human corrections, they feed into the next training cycle. We establish retraining triggers based on production monitoring (e.g., accuracy drops below a threshold, new terminology appears in incoming data). Retraining runs are faster and cheaper than initial training because the model only needs to learn the delta. Typical retraining cadence is quarterly for most domains, with ad hoc retraining for significant domain shifts (e.g., new regulatory frameworks, new product lines).

Can we fine-tune models that run entirely on our infrastructure?

Yes. We support fully on-premise fine-tuning and deployment for organizations with strict data residency or air-gapped requirements. We work with open-source base models (Llama, Mistral, Qwen, and others) that can be downloaded and fine-tuned on customer-managed GPU infrastructure without any data leaving your network. The fine-tuned model runs on your servers, behind your firewall, with no external API dependencies. For organizations that prefer cloud-based deployment, we support VPC-isolated training and serving in AWS, Azure, and GCP with customer-managed encryption keys.

How do you measure whether fine-tuning actually outperforms the base model?

We run rigorous A/B evaluations before any fine-tuned model reaches production. The evaluation framework compares the fine-tuned model against three baselines: the base model with no modifications, the base model with optimized prompts, and the base model with RAG. All four approaches are evaluated on the same held-out golden test set annotated by domain experts. We report per-class precision, recall, and F1 alongside task-specific metrics (extraction accuracy, code match rate, generation faithfulness). The fine-tuned model is promoted to production only when it demonstrates statistically significant improvement over all baselines on the metrics that matter to your operations.

Where this system ships

Legal Services

Fine-tuned models trained on firm-specific contract corpora deliver 96%+ clause extraction accuracy, enabling fixed-fee due diligence and automated lease abstraction at scale.

Healthcare

Domain-adapted models for medical coding, clinical note summarization, and prior authorization achieve accuracy levels that satisfy CMS compliance requirements and reduce denial rates.

Insurance

Models fine-tuned on carrier-specific policy forms and adjudication history enable automated coverage determination, claims triage, and subrogation identification with carrier-specific precision.

Financial Services

Fine-tuned models map regulatory language to internal control frameworks, automate compliance extraction, and generate risk assessments using institution-specific taxonomies and historical examination data.

Manufacturing

Technical documentation models trained on proprietary parts catalogs, engineering specifications, and service procedures generate accurate maintenance content with correct part numbers and torque specs.

Commercial Real Estate

Lease abstraction models trained on portfolio-specific lease libraries extract rent escalation formulas, co-tenancy provisions, and CAM structures with portfolio-calibrated accuracy.

Next step

Neume Labs brings engineering rigor to a space crowded with one-off experiments. We have built and deployed fine-tuned models in production across legal, healthcare, insurance, and financial services -- not in lab conditions, but in operational environments where errors have real consequences. Our team combines deep ML engineering expertise with industry-specific domain knowledge, which means we know both how to train a model and what the model needs to get right for your business. Every engagement produces a production system with monitoring, governance, and retraining infrastructure -- not a Jupyter notebook and a slide deck.

We are the only AI services firm that delivers fine-tuned models with integrated human-in-the-loop evaluation at every training checkpoint, continuous production monitoring with automated drift detection, and full model governance infrastructure that satisfies regulatory examination requirements out of the box. Our clients do not just get a better model -- they get an operational ML system that their compliance and risk teams can defend under audit.

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