AI Models

Train. Fine-Tune. Deploy.

Custom-built AI models tailored to your business goals — from generative content to predictive insights. We optimize every model for accuracy, performance, and real-world adaptability.

+34%
Avg accuracy lift vs. baseline
60%
Avg inference cost reduction
4 wks
Median time to first model
SOC 2
Type II compliant pipelines

Models, end to end

From data prep through deployment and monitoring — one team, one workflow.

Data Pipelines

Curate, clean, label, and version your data. Synthetic generation when ground truth is scarce.

Pretraining & Fine-Tuning

Adapter training, full-parameter tuning, RLHF, and DPO — chosen per task and budget.

Evaluation Suites

Custom evals that predict production quality — golden sets, regressions, A/B harnesses.

Optimization

Quantization, distillation, batching, and routing to cut latency and inference cost.

Deployment

Single-tenant or shared, your cloud or ours. CI/CD, canary rollouts, and safe rollbacks.

Monitoring & Drift

Live quality, latency, and drift dashboards with alerts when models start to wander.

Our model lifecycle

A repeatable workflow that's shipped over 80 production models.

01

Scope & Data

Define success metrics. Audit data. Build the eval set first — before any training.

02

Train

Iterate quickly across base models, training methods, and hyperparameters with full experiment tracking.

03

Evaluate

Hold-out evals, red-teaming, bias checks, and shadow-traffic comparisons.

04

Deploy & Monitor

Production rollout, online evals, drift alerts, and a continuous fine-tune loop.

Models we build

Domain LLMs

Language models that understand your jargon

Foundation models adapted to your terminology, format, and tone — from legal contracts to clinical notes.

  • Adapter (LoRA / QLoRA) and full fine-tuning
  • Instruction-tuned and chat-tuned variants
  • Domain eval sets co-built with your subject experts
Predictive Models

Forecasting, scoring, and classification at scale

Tabular and time-series models for churn, fraud, demand, risk — production-ready, with the eval rigor to back the numbers.

  • Gradient boosting, deep tabular, and ensemble methods
  • Calibrated probabilities and threshold optimization
  • Explainability via SHAP and counterfactuals
Multimodal Models

Vision, audio, and document understanding

Models that read, see, and listen — from invoice extraction to support-call summarization.

  • OCR + structured extraction with grounded confidence
  • Speech-to-text with domain vocabulary
  • Image classification, detection, and segmentation

Foundation models & platforms

We work across the leading providers and open-weight ecosystems.

OpenAI
Anthropic
Google
Mistral
Meta Llama
Cohere
Hugging Face
Databricks
Vertex AI
SageMaker
Azure ML
Modal

Model FAQs

Do we own the model weights?

Yes. For models we train or fine-tune for you, you own the weights, training data, and full deployment artifacts. No lock-in.

How much data do we need to fine-tune?

Depends on the task — instruction tuning often works with 500–2,000 high-quality examples. We help you bootstrap with synthetic data when needed.

What about privacy and PII?

We support PII redaction in pipelines, isolated training environments, on-prem deployment, and SOC 2 / HIPAA-compliant operations.

Can you take over an existing model project?

Yes. Many engagements start as a model rescue or eval rebuild. We meet your code where it is and improve from there.

Let's build a model that ships

Skip the science project — get something measurable in production.