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AI & Machine Learning Engineering
Machine learning systems trained on your data
We design, train, and ship production machine learning — predictive models, forecasting, natural language processing, and computer vision — on the data your business already generates. Engineered for production, not demos
ML
Custom Model Development
NLP
Language & Document AI
Vision
Image & Video Models
MLOps
Training & Deployment
Core Capabilities
What We Engineer
From data pipelines to deployed inference — the full machine learning lifecycle, built around the data you already hold.
Predictive Analytics & Forecasting
Supervised models that learn from your historical transactions to score risk, project demand, and flag churn before it shows up in the numbers.
- Time-series & demand forecasting
- Churn and propensity scoring
- Risk & credit classification models
- Feature engineering from source systems
Natural Language Processing
Language models tuned to your domain vocabulary for extraction, classification, summarization, and semantic search over your own corpus.
- Entity & clause extraction
- Document classification & routing
- Summarization and semantic search
- Domain vocabulary fine-tuning
Computer Vision
Image and video models for inspection, counting, reading, and detection — deployed to cloud GPUs or to edge devices installed on site.
- Defect & anomaly detection
- OCR and document capture
- Object detection & tracking
- Edge inference on constrained hardware
Data & Feature Engineering
The unglamorous half of machine learning: pipelines that clean, label, version, and serve the data your models train and infer on.
- Training data pipelines
- Labeling workflows & QA
- Feature stores and reuse
- Dataset versioning & lineage
MLOps & Model Lifecycle
Reproducible training runs, versioned artifacts, and deployment paths designed so a model can be retrained and rolled back safely.
- Experiment tracking & model registries
- Automated retraining triggers
- Drift and performance monitoring
- Shadow and canary rollouts
Model Evaluation & Governance
Evaluation harnesses, bias checks, and human review points so model behavior stays explainable to the people accountable for it.
- Held-out and adversarial test sets
- Bias & fairness evaluation
- Explainability reporting
- Human-in-the-loop review gates
Use Cases
Where Machine Learning Earns Its Place
Problems where a trained model outperforms a hand-written rule.
Demand & Inventory Forecasting
Forecast SKU-level demand from sales history, seasonality, and promotions to inform purchase and production planning.
Time-series modelsSeasonality-awareSKU-level output
Designed to replace spreadsheet guesswork with modeled forecasts
Document Intelligence
Extract structured fields from invoices, contracts, claims, and scanned records so downstream systems receive clean data.
OCR + NLP pipelineField-level extractionConfidence scoring
Built to turn paper and PDFs into structured records
Visual Quality Inspection
Vision models trained on your own defect images to grade parts, surfaces, and packaging as they move down the line.
Custom defect classesEdge deploymentRetrainable on new defects
Engineered for consistent inspection criteria across shifts
Customer Scoring & Segmentation
Propensity, churn, and lifetime-value models that rank customers for retention, credit, and campaign targeting.
Propensity modelsChurn scoringSegment discovery
Designed to prioritize outreach by modeled likelihood
Semantic Search & RAG Retrieval
Embedding pipelines and vector indexes over your internal knowledge, so search returns meaning rather than keyword matches.
Embedding pipelinesVector indexesChunking strategy
Built to make internal knowledge retrievable by meaning
Anomaly & Fraud Detection
Unsupervised and supervised detectors that surface transactions, sensor readings, or log patterns breaking from the norm.
Unsupervised baselinesAlert thresholdsAnalyst feedback loop
Designed to surface outliers for human review
Key Benefits
How We Build ML That Survives Production
Engineering practices designed to keep models useful after launch day.
Custom
Models Trained on Your Data
Architectures chosen for your problem and your dataset rather than a generic off-the-shelf endpoint.
MLOps
Retrainable by Design
Versioned pipelines and registries so models are refreshed as data shifts, not rebuilt from scratch.
Eval
Measured Before Release
Held-out evaluation and error analysis run before a model ever reaches production traffic.
Edge
Deployed Where It Runs
Inference packaged for cloud, on-premise, or edge hardware depending on latency and data-residency needs.
Our Process
From Question to Deployed Model
A staged approach that proves value before committing to a full build.
Step 01
Frame the Problem
Translate the business question into a modeling task with a defined target variable and a success metric.
Step 02
Data & Baseline
Assess data availability and quality, build the pipelines, and establish a simple baseline worth beating.
Step 03
Train & Evaluate
Iterate on features and architectures, with held-out evaluation and error analysis at every round.
Step 04
Deploy & Monitor
Ship inference into your environment, then watch for drift and retrain on an agreed cadence.
FAQ
Machine learning questions, answered
How we scope, train, deploy, and maintain machine learning models.
How is this different from your AI agents service?
Our AI agents service covers autonomous agents that plan, call tools, and orchestrate multi-step workflows. This service covers the models underneath: training predictive, language, and vision models on your data, plus the MLOps pipelines that keep them current. Many projects use both — a trained model producing the score, an agent deciding what to do with it.
How much data do we need before machine learning is viable?
It depends on the task. Tabular forecasting and classification often work from the history already sitting in your ERP or CRM. Vision and language tasks usually need labeled examples, so we scope a labeling plan during discovery. If the data genuinely is not there yet, we say so and start with instrumentation rather than modeling.
Do you build custom models or fine-tune existing ones?
Both, chosen per problem. Classical gradient-boosted and time-series models still win on tabular data. For language and vision we typically start from a pre-trained backbone and fine-tune on your domain data, which costs less and needs far fewer labeled examples than training from scratch.
Can models run on-premise or at the edge?
Yes. We package inference for cloud GPUs, on-premise servers, or edge devices on the shop floor, choosing model size and quantization to fit the hardware. On-premise deployment is common where data residency rules or plant connectivity make sending images and records off site impractical.
What happens when a model starts getting worse over time?
That is drift, and it is expected rather than a defect. We instrument inputs and predictions so distribution shifts and accuracy decay become visible, define retraining triggers up front, and keep previous model versions available so a rollback is a deployment step rather than a rebuild.
How do you evaluate a model before it goes live?
Against a held-out dataset the model never saw during training, using metrics chosen for the business cost of each error type — precision matters more where false positives are expensive, recall where missed cases are. We also run error analysis on failure cases and, where relevant, check performance across segments for bias.
Get started
Have a prediction problem worth solving?
Bring us the question and the data you already collect. We will tell you honestly whether machine learning is the right tool for it.
No commitment required
Free consultation
Expert support