acAIberry

AI Model Development

Custom-built AI models trained on your data, tuned to your problem, and engineered to perform in production, not just in a notebook.

Service Details

Models built for your problem, not the other way around.

We design and train AI models from the ground up, from large language models to computer vision and predictive systems, so you're not stretching a generic off-the-shelf model to fit a use case it was never built for.
Our Offerings

AI Model Development Capabilities

From foundation-model adaptation to custom predictive systems, we build models around the data, constraints, and outcomes that matter to your organization.

Custom LLM Development

Domain-tuned large language models fine-tuned on your proprietary data and vocabulary.

Predictive & Classification Models

Forecasting, scoring, and classification models trained for your specific business signals.

Computer Vision Models

Object detection, image classification, and visual quality-control models built for real-world conditions.

Retrieval-Augmented Generation (RAG)

Models grounded in your knowledge base, reducing hallucination and keeping answers current.

Model Fine-Tuning & Adaptation

Adapting existing foundation models to your domain without training from scratch.

Ongoing Model Monitoring

Drift detection and retraining pipelines that keep model performance from decaying over time.

Our Methodology

A Disciplined Path from Data to Deployed Model

We move from data assessment to deployment handoff through clear checkpoints, measurable evaluation, and production-minded engineering.

Start Your Project
01

Data Assessment

We audit your available data for volume, quality, and labeling gaps before committing to an approach.

02

Architecture Selection

We match model architecture to the problem, weighing accuracy, cost, and latency trade-offs.

03

Training & Evaluation

We train iteratively, with held-out test sets and clear evaluation metrics at every checkpoint.

04

Validation & Stress Testing

We test against edge cases, adversarial inputs, and real-world data drift before greenlighting deployment.

05

Deployment Handoff

We package the model with documentation, monitoring hooks, and retraining pipelines for long-term ownership.

Models Built to PerformBeyond the Demo

Build a model with the documentation, monitoring hooks, and reproducible training pipeline your team needs to trust it in production.

95%+
Target accuracy threshold
100%
Reproducible training pipelines
<2 weeks
Avg. feasibility assessment
Zero
Black-box deliverables