AI Consulting Service

Custom Model Development

Fine-tuned and purpose-built AI models for your data, your workflows, your outcomes.

Overview

Off-the-shelf AI is generic by design. When your problem demands domain-specific accuracy — patient records, financial transactions, internal policies, proprietary product data — generic models hit a ceiling fast.

We build custom AI models that understand your business context. From fine-tuning frontier LLMs on your proprietary data to training specialized classifiers and recommendation systems, every model we ship is purpose-built for the outcome you need.

We handle the full lifecycle: data preparation, model selection, training, evaluation, deployment, and ongoing monitoring.

How We Work

Our Approach

01

Data Audit

Review the data we have to work with. Identify quality gaps, labeling needs, and what needs to be cleaned or augmented before training.

02

Model Selection

Choose the right base architecture for the job — LLM fine-tune, custom classifier, embedding model, or hybrid system.

03

Training

Prepare datasets, train, iterate. Track experiments rigorously until benchmarks are met against real-world test cases.

04

Evaluation

Measure against acceptance criteria that actually matter — not just synthetic accuracy, but the metric that ties to business outcomes.

05

Deployment

Ship to production with proper monitoring, versioning, and rollback. Whether that's your cloud, your on-prem stack, or a managed endpoint.

06

Iteration

Continuous improvement as new data comes in. Drift monitoring, retraining schedules, and ongoing accuracy tracking.

Output

What You Get

  • Production-ready model deployed to your infrastructure
  • Evaluation reports against your acceptance criteria
  • Monitoring dashboard for drift, accuracy, and cost
  • Documentation for retraining and updating
  • Optional ongoing model maintenance retainer
Fit Check

When to Engage Us

Generic AI is not accurate enough

Off-the-shelf tools cannot handle your domain — medical terminology, legal precedent, financial nuance — and you need precision.

Proprietary data advantage

You have years of proprietary data that gives you a moat. A custom model can turn that data into a defensible product feature.

Compliance requirements

You need to keep sensitive data on-premise or in a private environment, which rules out most third-party AI APIs.

Own the IP

You want to own the model — not rent it from a vendor whose pricing or terms could change overnight.

Next Step

Ready to talk through custom models?

Start with a free AI readiness assessment, or reach out directly to scope an engagement.

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