Generic AI gives generic results. We build custom models that understand your domain, trained on your data, solving your specific problem — and we get them into production with monitoring, explainability, and a clear path to continuous improvement.
Six reasons ML initiatives
never reach production.
Pilots That Never Reach Production
87% of data science projects never make it to production. A model in a Jupyter notebook is a science experiment, not a business system. The gap between "the model works" and "the model is serving production traffic reliably" is where most ML initiatives die.
The Data Readiness Gap
Teams discover their data is too sparse, too dirty, or too poorly labeled only after committing to an ML project. The model is only as good as the data — and most enterprise data isn't ML-ready out of the box.
Generic APIs for Specific Problems
Wrapping the OpenAI API and calling it "AI" works for general tasks but fails on domain-specific problems requiring specialized terminology, sub-50ms latency, or data privacy. Generic models give generic results.
Model Drift Killing Accuracy
A model trained on last year's data degrades silently as the world changes. Without drift monitoring and retraining pipelines, accuracy erodes until someone notices the predictions are wrong — usually after a costly business decision.
Black-Box Compliance Risk
Regulated industries can't deploy models they can't explain. A model that can't justify its decisions is a regulatory and reputational liability — especially in lending, healthcare, and insurance.
Inference Too Slow for Real-Time
A model that takes 800ms to respond is useless for real-time fraud detection or live personalization. Most data science teams optimize for accuracy in the notebook and never address production latency requirements.
Real ML. Not demos.
Everything we build runs in production with monitoring, explainability, and a clear path to improvement.
Custom Model Development
We train models on your data, for your specific problem. Classification, regression, sequence modeling — we pick the right architecture for the task and the data, not the architecture that's trending on Twitter.
Predictive Analytics
Forecasting models that tell you what's going to happen before it does — demand prediction, churn risk, equipment failure, fraud probability. Built on your historical data with rigorous validation against held-out sets.
Computer Vision
Object detection, defect identification, document processing, and real-time video analysis. Deployed in manufacturing quality control, healthcare imaging, and retail environments at production scale.
MLOps & Infrastructure
A model that lives in a notebook is not in production. We build the training pipelines, model registries, feature stores, monitoring, and deployment infrastructure that turn experiments into reliable systems.
Fine-Tuning & RAG Systems
LLMs that know your domain, your terminology, and your policies — through fine-tuning or retrieval-augmented generation on your data, not generic internet text. With guardrails and hallucination controls.
Real-Time Inference Optimization
Low-latency model serving that handles production traffic. We've optimized inference pipelines from 800ms to under 50ms through quantization, distillation, and serving architecture — without sacrificing accuracy.
Six categories of ML
we build and maintain.
Each model type has a distinct data profile, complexity level, and business use case. Here's what we deploy in production.
Classification
- Fraud detection
- Churn prediction
- Sentiment analysis
- Document categorization
Regression
- Demand forecasting
- Price optimization
- Risk scoring
- Lifetime value prediction
Computer Vision
- Defect detection
- OCR / document extraction
- Object tracking
- Medical imaging
NLP / LLM
- Custom chatbots
- Document summarization
- Entity extraction
- Semantic search
Time Series
- Anomaly detection
- Capacity planning
- Equipment failure prediction
- Market forecasting
Recommendation
- Product recommendations
- Content personalization
- Next-best-action
- Cross-sell modeling
From notebook to
production system.
The difference between a science experiment and a business system is everything after model.fit(). Here's our end-to-end MLOps lifecycle.
Data & Discovery
- Data audit and quality assessment
- Feature engineering and feature store setup
- Baseline model and feasibility analysis
- Success metric definition
Model Development
- Architecture selection and experimentation
- Training pipeline with experiment tracking
- Validation against held-out test sets
- Explainability and bias analysis
Production Deployment
- Inference optimization for latency targets
- Model registry and versioning
- A/B testing and gradual rollout
- Serving infrastructure and scaling
Monitor & Improve
- Data and concept drift detection
- Automated retraining pipelines
- Performance monitoring and alerting
- Continuous improvement loops
Before ML. After ML.
Real outcomes from production ML systems. Measured, not projected.
| Use Case | Before | After SKIFIN ML | Gain |
|---|---|---|---|
| Fraud detection | Rule-based, 40% false positives | ML model, <3% false positive rate | 92% |
| Document processing | Manual extraction, 200/day | AI pipeline, 50,000/day | 250× |
| Demand forecasting | Excel-based, 30% MAPE error | ML model, 8% MAPE | 73% |
| Customer churn | Reactive — churn happened first | Predictive, 60-day early warning | 100% |
| Equipment maintenance | Scheduled, frequent failures | Predictive, 85% failures prevented | 85% |
| Inference latency | 800ms response time | <50ms optimized endpoint | 16× |
Models that
earn their keep.
A payments company's rule-based fraud system flagged 40% of legitimate transactions as suspicious, frustrating customers and overwhelming the fraud review team. Meanwhile, sophisticated fraud patterns slipped through the static rules undetected.
We built a real-time fraud detection model trained on 2 years of transaction data with engineered behavioral features. Deployed with <30ms inference latency, explainability for every decision (regulatory requirement), and an active learning loop that incorporates fraud analyst feedback.
False positive rate dropped from 40% to under 3%. Fraud detection rate improved 34%. Fraud review team workload reduced 85%. Every decision is explainable for regulatory audit. The model retrains weekly on new fraud patterns.
A manufacturer was losing $2.8M annually to unplanned equipment downtime. Scheduled maintenance was either too frequent (wasting resources) or too late (causing failures). They had sensor data but no way to use it predictively.
Built a predictive maintenance system using time-series sensor data (vibration, temperature, current draw) to predict equipment failures 7–14 days in advance. Integrated with their maintenance management system to auto-generate work orders when failure probability crossed thresholds.
85% of equipment failures predicted and prevented. Unplanned downtime reduced 71%. Maintenance costs reduced 23% by eliminating unnecessary scheduled maintenance. The system paid for itself in under 5 months.
A health system was manually extracting structured data from clinical documents — referral letters, lab reports, discharge summaries — at a rate of 200 documents per day per staff member, with a 2-3 day backlog and frequent transcription errors.
Deployed a domain-specific NLP pipeline combining medical entity extraction, ICD-10 code suggestion, and clinical document classification. Fine-tuned on de-identified clinical text with physician validation. Built with full HIPAA compliance and human-in-the-loop review for low-confidence extractions.
Document processing capacity increased from 200/day to 50,000/day. Backlog eliminated. Extraction accuracy of 96% (vs. 91% manual). Clinical coding staff redeployed to complex cases requiring judgment. Zero PHI exposure incidents.
What ML teams ask us.
Have a prediction problem worth solving?
Bring us the business problem and a sample of your data. We'll assess feasibility, give you a realistic timeline, and tell you honestly whether ML is the right tool — or whether something simpler would work better.