Models built for production and innovation.
Custom model production, fine-tuning, and engineered pipelines built for effective deployment.
Custom Model Training
Models trained on your data for your problem — classification, regression, forecasting, ranking, recommendation, and anomaly detection. We pick the right model class for the problem, not the most fashionable one.
Fine-Tuning & Transfer Learning
When a pretrained model gets you most of the way, we fine-tune it on your domain data — LoRA and full fine-tuning for language and vision models, plus transfer learning to reach accuracy with far less labeled data.
Feature Engineering & Data Pipelines
Reproducible feature pipelines that run the same way in training and production — so a model can be retrained on the exact same logic later. Train/validation/test splits built to prevent leakage from the start.
Model Evaluation & Error Analysis
Evaluation on held-out data with the metric that matches your business goal — plus slice-based error analysis that shows where the model fails, not just a single headline accuracy number that hides the weak spots.
Deployment & Serving
Models packaged behind a versioned scoring API or scheduled batch job your systems can call — with staged rollout and a rollback path. This is where most models die; closing the gap is the core of the engagement.
Monitoring, Drift & Governance
Data-drift and performance monitoring with retraining triggers, an experiment-tracking trail, and a model registry. PII in training data is identified and handled at the pipeline layer to support your CCPA and data-governance obligations.
The Model Lifecycle
The never-ending loop.
Training a model is the easy 20%. The other 80% is the loop for conscious deployment — and that's what we build.
The loop closes here
Drift signals from Monitor feed back into Data & Features to trigger retraining. The model stays accurate instead of quietly degrading.
Click any stage to see what we implement and what your team receives at handoff.
Across the Production Gap
ML systems built to ship — serving infrastructure, versioning, and rollback designed for real traffic, not a model that lives in a notebook and never reaches a single user.
Accuracy You Can Defend
Evaluation on held-out data with leakage checks and slice-based error analysis, so the number you report to your stakeholders holds up on live data — not just on the training set.
Models That Don’t Rot
Drift detection and retraining triggers built in from the first sprint. When your data shifts, you get an alert and a retraining path — not a silent accuracy collapse discovered in a quarterly review.
You Own the Model
Trained on open frameworks with a documented pipeline, model registry, and retraining playbook your team can run. No black box, no dependency on us to keep the model alive.
Right Problem, Right Model
We frame the business question into a measurable ML problem with an honest baseline before training anything — so effort goes to problems ML can actually solve, not science projects.
Key Capabilities
- Problem framing and ML feasibility assessment with baselines
- Supervised model training (classification, regression, ranking)
- Forecasting and time-series modeling
- Recommendation and anomaly-detection systems
- Fine-tuning and transfer learning (LoRA, full fine-tune)
- Reproducible feature pipelines and dataset versioning
- Leakage-safe evaluation and slice-based error analysis
- Model serving (real-time API and batch scoring)
- MLOps: experiment tracking, model registry, CI for models
- Drift monitoring, retraining triggers, and PII-aware data handling
Technologies
Engagement Models
ML Feasibility Sprint
- Problem framing and success-metric definition
- Data audit and leakage-risk assessment
- Baseline model and feasibility verdict on your real data
- Go / no-go recommendation with production estimate
Production Model Build
- Feature pipeline and dataset versioning
- Model training, fine-tuning, and evaluation
- Deployment (real-time API or batch scoring)
- Drift monitoring and retraining triggers
- Model registry, runbooks, and retraining playbook
ML Build + Managed Models
- Everything in Production Model Build
- Ongoing drift monitoring and incident response
- Scheduled retraining and model refresh
- Periodic performance reviews and tuning
- Direct Slack/Teams channel with ML engineers
Frequently Asked Questions
How do you make sure a model actually reaches production instead of dying in a notebook?
Production is the design target from day one, not an afterthought. We frame the problem against the system that will consume the predictions, build feature pipelines that run identically in training and production, and package the model behind a versioned scoring API or batch job your systems can call. Deployment includes staged rollout and a rollback path. The model-to-production gap is where most ML efforts fail, so the engagement is structured around closing it — the trained model is only one stage of five in our lifecycle, not the finish line.
How do you prove the accuracy is real and not overfit or leaking?
We evaluate on held-out data the model never saw during training, using train/validation/test splits designed to prevent leakage — for example, splitting by time for forecasting or by entity to avoid the same customer appearing in both sets. We report the metric that matches your business goal, not a flattering one, and we run slice-based error analysis to show where the model is weak rather than hiding behind a single headline number. We also establish an honest baseline first, so every reported gain is measured against something real.
What happens when our data changes after the model is deployed?
That is exactly what the monitoring stage is built for. We instrument data-drift and prediction-drift detection, and where ground-truth labels become available we track live performance against them. When drift crosses a threshold, you get an alert and a retraining path — the drift signal feeds back into the feature and data stage to trigger a retrain on fresh data. The goal is that you find out a model is degrading from a monitoring alert, not from a stakeholder noticing the predictions got worse.
Do we own the model, or are we locked into you to keep it running?
You own it. Models are trained on open frameworks (PyTorch, scikit-learn, XGBoost, Hugging Face) with a documented, reproducible pipeline, a model registry, and a retraining playbook your team can run. Handoff includes runbooks and structured knowledge transfer so your engineers can retrain, redeploy, and extend the model without us. If you choose the managed option, that is a convenience, not a dependency — you can take operations in-house at any point.
How do you handle PII and data governance in training data?
PII in training data is identified at the feature-pipeline layer and handled according to your governance requirements — masking, exclusion, or aggregation depending on what the model actually needs. Datasets are versioned so you have a traceable record of what data trained which model, which supports CCPA and internal data-governance obligations. We architect the pipeline so privacy decisions are explicit and documented rather than buried in a notebook. We do not provide legal compliance certification; we build the model and data layer so it supports your governance program rather than working against it.
What if we are not sure ML is even the right approach for our problem?
Start with the ML Feasibility Sprint. In two weeks we frame your problem into a measurable ML question, audit your data for quality and leakage risk, build a baseline on your real data, and give you an honest go / no-go verdict with a production estimate. Sometimes the answer is that a simpler rules-based approach wins, or that the data is not ready yet — and saying so is a legitimate outcome. You leave with a clear decision and the evidence behind it, not a six-figure commitment to a model that might not pay off.
Ready to build a model that ships and stays accurate?
Book a 30-minute call. We will discuss your use case, what data you have, and whether a feasibility sprint or a full production build is the right place to start.