Classical ML and generative AI built on your data
Recommenders, forecasts, personalization, and generative pipelines — designed, trained, and deployed against real data with monitoring that survives contact with production traffic.
The full AI/ML delivery surface
Classical machine learning still moves the biggest numbers in most businesses. We combine it with generative work when the problem actually calls for it.
Model development
Supervised, unsupervised, and generative models trained against your data with reproducible pipelines.
Feature engineering
Feature stores, transformation pipelines, and offline/online parity so models see the same data in training and production.
Recommender systems
Content, product, and next-best-action recommenders with offline evaluation and online A/B testing built in.
Forecasting & prediction
Demand, revenue, churn, and risk models with proper backtesting, confidence intervals, and drift detection.
Generative content pipelines
Copy, image, and audio pipelines with prompt templating, quality scoring, and human-review checkpoints.
Model monitoring
Live dashboards for accuracy, latency, drift, and business KPIs — with alerts before quality slips.
A path from raw data to production ML
Four phases that pair modeling with the evaluation and deployment work most teams underestimate.
Data audit
We assess data availability, quality, and labeling, and align on the business metric the model is meant to move.
Modeling sprint
Baselines, feature iterations, and candidate architectures compared against a fixed evaluation protocol.
Evaluation & tuning
Offline metrics, error analysis, and business-facing dashboards drive tuning until the model earns its release.
Deployment & monitoring
We ship behind feature flags, wire prediction logging and drift detection, and document the retraining cadence.
The ML stack we build on
Modeling libraries, MLOps platforms, and the data infrastructure that keeps training and serving in sync.
Where AI/ML moves real numbers
Well-scoped ML problems with enough data, clear success metrics, and room in the product to act on predictions.
Personalization engines
Ranking, targeting, and next-best-action systems tuned against real conversion and retention metrics.
Demand forecasting
Multi-horizon forecasts for inventory, staffing, and revenue with proper handling of seasonality and shocks.
Fraud detection
Real-time scoring pipelines with feedback loops, human review queues, and cost-aware threshold tuning.
Content recommendations
Hybrid recommenders with cold-start handling, diversity constraints, and A/B evaluation.
Generative asset pipelines
Copy, imagery, and video generation with brand-safe prompts and QA gates before publication.
Customer segmentation
Behavioral and value-based segments powering lifecycle marketing and product decisions.
Outcomes tied to business metrics
We only ship a model when it moves a metric leadership already tracks.
Better business decisions
Forecasts, segmentations, and predictions grounded in real data instead of gut feel or last-quarter averages.
Personalization at scale
One-to-one experiences delivered through recommender pipelines that keep working as catalogs and users grow.
Automated content generation
Generative pipelines that produce brand-consistent assets faster than manual workflows can.
Data-driven revenue lift
Targeting, pricing, and lifecycle programs measurably improved through models tuned to the KPIs that matter.
ML systems in production
A grounded assistant built with a full retrieval and evaluation stack.
AI • RAG
NeuraDesk
A retrieval-augmented support copilot that grounds answers in a company's own knowledge base.
Read Case Study →Why teams choose us for AI/ML delivery
A senior team that treats modeling and deployment as one problem, not two disconnected sprints.
Learn more about us →Senior ML engineers, no hand-offs
The same engineers who build the model deploy it and monitor it after launch.
Business metrics first
Every model has a KPI it exists to move, tracked from prototype through production.
MLOps built in
Pipelines, feature stores, and monitoring are part of the first delivery — not a follow-up project.
Own the outcome, not just the notebook
We stay through rollout, retraining, and drift so the model keeps earning its keep.
Answers on modeling and deployment
Practical questions on data, evaluation, and how ML actually reaches production.
How much data do we need?
Do we need our own data platform first?
How do you compare models?
How do you handle drift?
Can generative and classical models work together?
What does handover look like?
Ready to put ML to work on your data?
Book a working session and we'll shape a first model around a KPI your business already tracks.
Book a Discovery Session →