Custom Models
Models built for your problem, not the other way around
From traditional ML to fine-tuned transformers—we choose the approach that hits your accuracy, latency, and cost targets. Not the one that's trendy.
Not sure where to start? Ask us →
MODEL
Accuracy
↑ 94.2%
lr
1e-4
batch
32
epoch
147
Trusted by industry leaders
7 Years
Building ML Models
AWS ML Partner
SageMaker, Bedrock
45+
Models Developed & Deployed
When you need a custom model
APIs and prompting handle most problems. Custom models are for the cases they can't.
Domain-specific knowledge
Medical, legal, financial, or industry-specific data where general LLMs weren't trained deeply enough to be reliable.
Latency or cost constraints
API calls are too slow for real-time use, or per-call pricing doesn't scale with your volume.
Data residency requirements
Compliance, security, or competitive reasons mean you need a model you own and run in your own environment.
Multimodal or specialized tasks
Video + text analysis, voice quality coaching, or tasks that need actionable feedback—not just pattern recognition.
Not sure whether you need a custom model?
Ask us →Where we've shipped custom models
Custom document classification and extraction models for mortgage packages. Fine-tuned LayoutLMv3 for document understanding across hundreds of document variants.
Production deploymentFine-tuned DONUT model for document understanding—extracting and verifying data from messy financial documents at scale.
Production deploymentRecommendation and clustering models for automotive sales—propensity scoring and customer segmentation using gradient boosting and classical ML.
Production deploymentEnterprise Call Center (Confidential)
Custom voice analysis model for real-time agent coaching—not just transcription, but actionable feedback on tone, pacing, and call quality.
On-premise deploymentReferences and deeper technical details available on request.
Why Softmax
We pick the right tool, not the shiny one
Sometimes a gradient boosted tree beats a transformer. We recommend what works.
Deep learning isn't always the answer. We've shipped recommendation engines with XGBoost, clustering models with classical ML, and fine-tuned transformers—choosing based on what the problem actually needs, not what sounds impressive.
7 years shipping models to production
Not consultants who discovered ML last year—engineers who've debugged training loops at 2am.
We've shipped models under strict latency, limited compute, and data that can't leave premises. We know why your loss isn't converging and what to do about it.
Full stack: data to deployment
We don't hand you a weight file. We ship production systems.
Data pipeline, training infrastructure, evaluation harness, production serving, monitoring. SageMaker, Databricks MLflow, custom infra—whatever fits your stack.
How we build custom models
Different problems need different approaches. We choose based on your data, constraints, and acceptance bar.
Traditional ML
XGBoost, random forests, clustering. When you need interpretability, fast inference, or deep learning is overkill.
Recommendation, propensity, clusteringFine-Tuning
Adapting foundation models (LayoutLM, DONUT, BERT) to your domain. Power of large models, tailored to your task.
Document understanding, domain languageCustom Architectures
Purpose-built models for your exact requirements—when off-the-shelf doesn't fit your latency, size, or accuracy needs.
Edge deployment, real-time inferenceWhat you get
Not a Jupyter notebook. Production artifacts your team can deploy, run, and maintain.
Trained Model
Weights, adapters, or full architecture—optimized for your target environment
Data Pipeline
Collection, cleaning, preparation—automated and ready for continuous retraining
Evaluation Suite
Metrics, test sets, acceptance criteria—so quality is measurable over time
Training Pipeline
Reproducible, documented, ready for retraining as your data evolves
Deployment Pipeline
Model selection, versioning, rollout—from training to production automatically
Documentation + Handoff
So your team can own, operate, and extend it without us
How we work
01
Strategy
Define the problem, success metrics, and constraints. Validate whether custom models are the right approach—or if simpler solutions can hit your bar.
02
Build
Data pipeline, model selection, training infrastructure. Iterative evaluation until we hit your acceptance bar—baseline measured, improvements tracked.
03
Launch
Production deployment, monitoring setup, performance validation. Handoff with documentation so your team can run and extend it.
From our engineering blog
Deep dives on model development, fine-tuning, and what we've learned shipping custom models.
Not sure if you need a custom model?
Book a discovery call. We'll tell you honestly whether custom models are the right approach—or if APIs and prompting can hit your bar.
Not sure where to start? Ask us →