AI & Machine Learning
ML systems that survive contact with production data.
Models trained on your data, deployed with monitoring, retrained when drift kicks in. No demo notebooks. We have run gradient-boosted classifiers, transformer-based NLP pipelines, and multi-model signal ensembles in production environments where wrong answers cost money.
63%
Directional accuracy on production ML signals
< 100ms
Inference latency on deployed models
3.2x
Lift on top-decile lead conversion
METRICS
By the numbers
< 100ms
Production inference latency
723M+
Training rows across deployments
100%
Model weights ownership
2 to 4 wks
Model to production
VALIDATION
Validation methodology
We split every dataset by time, never by random shuffle. Training stops at a cutoff date. Validation runs on the next 30 days. Holdout sits untouched until the final accuracy report. We have caught feature leakage three times this way on production engagements: a join key that included a future timestamp, an aggregated metric computed across the train and validation window, and a target encoding that bled label statistics. Walk-forward validation re-fits the model on a rolling window so degradation over time is visible before deploy, not three months in. Calibration is checked separately. A model that scores 0.84 AUC but predicts everything between 0.4 and 0.6 is worse than a calibrated 0.78 AUC model for any threshold-based decision. We measure both.
CAPABILITIES
What we build
01
Custom model training
We select the architecture that matches your label density and latency budget. LightGBM for tabular classification with sparse features. PyTorch for sequential or image-based problems. Transformer fine-tunes for domain-specific text. Validation uses held-out time splits, not random shuffles, so accuracy numbers reflect what the model will see after deploy.
02
LLM integration and RAG
Retrieval-augmented generation grounded in your actual knowledge base. We choose the embedding model and vector store based on your corpus size and query latency requirements, then add citation tracking so every answer traces back to a source document rather than hallucinating one.
03
NLP and text pipelines
Sentiment scoring, named-entity extraction, document classification, and structured data extraction from unstructured text. We have built pipelines processing hundreds of thousands of documents per day with per-document confidence scores and a rejection path for ambiguous inputs.
04
Production deployment and monitoring
Model served behind a versioned inference endpoint with input schema validation, prediction logging, and a drift monitor watching feature distributions. When the drift alarm fires, the retraining job runs automatically against the latest labeled window.
TECHNOLOGY
Tech stack
APPLICATIONS
Where this applies
- 01Lead scoring trained on your conversion history. Gradient-boosted classifier trained on firmographic, behavioral, and engagement signals produced a model that ranked the top decile of leads with 3.2x the conversion rate of the unscored baseline.
- 02Document extraction pipeline. An insurance client processed 80,000 policy documents per month. We built a pipeline that extracted 14 structured fields per document with 94% field-level accuracy, eliminating a team of manual reviewers.
- 03Regime detection for a trading system. A multi-label classifier identifies whether markets are trending, mean-reverting, or in a volatility spike. The signal runs in under 5ms per bar and conditions position sizing on the regime output.
- 04Internal knowledge base copilot. RAG system over 3 years of support tickets and documentation. Mean time to answer dropped from 8 minutes of manual search to 12 seconds.
PROCESS
How we deliver
Every engagement follows the same three phases. No surprises, no scope creep.
Data Audit + Model Selection
We assess your data quality, volume, and labeling state. We select or adapt a model architecture that matches your performance requirements and compute budget.
Train + Evaluate Against Your Data
Training runs against your dataset with held-out validation splits. We measure accuracy, latency, and calibration before any model touches production.
Deploy + Monitor + Retraining Cadence
Model served behind a low-latency inference endpoint. Drift monitoring and a scheduled retraining pipeline ensure the model stays current as your data evolves.
GET STARTED
Ready to build?
Most projects ship in 2 to 4 weeks. Fixed price. Full IP transfer.