Right-sized models measured against a real baseline

You do not need a slow, expensive 70B prompt for a task a calibrated gradient-boosted tree or fine-tuned 7B model solves in 80 milliseconds. We build, distill and benchmark applied ML models that hold up in production.

Scope My ML Model See What We Build
Machine learning evaluation curve and distillation benchmark diagram ml.evaluation.benchmark 500 held-out cases · f1 vs latency HELD-OUT EVAL BENCHMARK (ACCURACY VS LATENCY) Generic Zero-Shot Prompt (1,800 ms) FINE-TUNED · 94.2% · 110 MS 100 examples 2,000+ reviewed examples TABULAR ML XGBoost · LightGBM Churn, LTV & fraud SHAP EXPLAINED LORA / DISTILL 70B → 7B student Schema-locked JSON −62% UNIT COST SHADOW DEPLOY Live traffic mirror Drift & calibration AUTO ROLLBACK HUMAN FEEDBACK LOOP → NEXT TRAINING SNAPSHOT Operator corrections feed the held-out benchmark automatically

WHAT WE BUILD WITH MACHINE LEARNING

Six prediction and scoring systems that directly move revenue

We train models on your existing CRM, ERP, store and product tables to predict customer behavior, optimize stock and cut AI API bills.

Churn prediction and early-warning health scores CRM · Subscriptions · SaaS · Retail
ACCOUNT #4092: 84% churn risk in next 30d DRIVER: Usage down 42% · Triggered CS playbook
Customer Retention & Churn

Churn prediction and early-warning health scores

Spot which accounts or subscribers are about to cancel 30 to 60 days before they leave, and push the exact risk reasons straight into HubSpot or Salesforce.

Scope a churn model
Demand, SKU stockout and cash-flow forecasting Retail · E-Commerce · Manufacturing
SKU #8810: 4,200 units needed for Nov peak CONFIDENCE: P90 band · Reorder alert sent to ERP
Inventory & Supply Forecasting

Demand, SKU stockout and cash-flow forecasting

Predict weekly demand per SKU, store and warehouse accounting for seasonality, promotions and lead times so you stop tying up cash in dead stock.

Scope demand forecasting
Lead scoring and conversion-probability ranking B2B Sales · Real Estate · Clinics
INBOUND LEAD: Acme Corp · Fit score 94/100 ROUTED: Assigned to Senior AE in 12 seconds
Sales & Revenue Prioritization

Lead scoring and conversion-probability ranking

Rank thousands of inbound leads by their real likelihood to close and expected deal size so your best reps call the highest-value buyers first.

Build a lead scoring model
Real-time fraud, anomaly and default scoring in < 15 ms Fintech · Marketplaces · Lending
TX #99201: Anomaly score 0.96 · Hold placed LATENCY: 9 ms · Saved $4,800 chargeback
Payment, Credit & Fraud Risk

Real-time fraud, anomaly and default scoring in < 15 ms

Score transactions, seller payouts, claims or credit applications in milliseconds with calibrated false-positive thresholds and clear audit explanations.

Scope fraud / risk ML
Product recommendation, upsell and search ranking Storefronts · Marketplaces · Apps
SHOPPER SEGMENT: High-AOV repeat buyer RANKED: Margin-weighted bundle · +19% AOV
E-Commerce & Media Personalization

Product recommendation, upsell and search ranking

Rank search results and "frequently bought together" carousels by both customer affinity and contribution margin to lift average order value.

Build a recommendation engine
Compress slow GPT/Claude prompts into fast private models Model Distillation · 60% Cost Cut
BEFORE: $4,200/mo frontier API · 1.8s latency AFTER: $680/mo private 7B model · 110ms
Replacing Expensive LLM Prompts

Compress slow GPT/Claude prompts into fast private models

When an AI feature runs thousands of times a day, we train a compact specialist model on your reviewed logs that runs 5× faster at a fraction of the cost.

Cut our AI API bill

PICK THE RIGHT PARADIGM

Tabular trees vs Distilled 7B vs Frontier LLM

We choose the model family based on latency, unit cost and explainability requirements, not hype.

Best for: Churn, LTV, credit/fraud risk, lead scoring & demand forecasting on SQL tables

Never send structured database rows to an LLM prompt. Gradient-boosted trees beat frontier LLMs on accuracy, run 100× faster on CPU and explain exactly which columns drove the score.

Benchmark this approach
p50 Latency6 to 14 ms
Unit Cost$0.004 per 1,000 calls
Validation Score0.94 ROC-AUC (Calibrated)
ExplainabilityExact SHAP feature attribution per row

EXPLAINABLE PREDICTIONS

Every score comes with the "why" attached

A churn or fraud score is useless if your sales or risk team does not know what triggered it. We attach SHAP feature attributions to every prediction.

Live SHAP Attribution Sample account_id: #4092 · p(churn) = 0.84
days_since_last_core_action +0.38 churn risk
failed_payment_retries_30d +0.29 churn risk
active_team_seats_ratio −0.31 retention lift
api_webhook_volume_trend −0.24 retention lift
support_ticket_sentiment_delta +0.16 churn risk

Pushed automatically to HubSpot / Salesforce so the account manager sees the exact risk drivers before calling.

01 · Stratified Ground Truth

Zero data leakage between train and test

We split datasets by time window and customer entity so models are tested on true future behavior, never memorized rows.

Build our eval benchmark
02 · Probability Calibration

When the model says 80%, it happens 8 out of 10 times

We apply isotonic and Platt scaling so raw model scores translate directly into expected dollar value and automated threshold rules.

Scope a scoring model
03 · Shadow-Mode Validation

Proven silently on live traffic before cut-over

Every new model runs in shadow mode against live events for two weeks before it is allowed to trigger customer-facing actions.

Distill a heavy prompt

ML LIFECYCLE

Four stages from raw logs to calibrated model

01

Evaluation harnesses and dataset curation

Build the 500-case benchmark that tells you the truth.

With our data engineers and trained data interns, we clean, deduplicate and label held-out test sets from your real domain so every prompt or model change is scored objectively.

Golden eval setsHuman labeling & QAAutomated CI scoringZero training-set leak
Build our evaluation set
02

Fine-tuning and model distillation (LoRA / SFT)

Turn a slow frontier prompt into a fast 3B to 22B specialist.

We fine-tune open-weights models (Llama, Gemma, Mistral, Qwen, DeBERTa) on your reviewed cases so you own the weights and cut inference latency and cost.

LoRA / QLoRA SFT70B → 7B model distillationSchema-locked outputsOwn your weights
Fine-tune a specialist model
03

Tabular ML: forecasting, scoring and ranking

Lead scoring, churn prediction, fraud detection and demand forecasting.

For structured database tables, classical ML outperforms LLMs on accuracy, speed and explainability. We ship calibrated XGBoost/LightGBM pipelines with clear feature importance.

Lead & churn scoringDemand forecastingAnomaly & fraudSHAP explainability
Build a scoring / forecast model
04

Shadow deployment and human-in-the-loop feedback

Test the new model silently against live traffic before switching over.

New models run in shadow mode alongside your current rule or human process until precision and recall clear the gate, and operator corrections feed the next training cycle.

Shadow mode testingConfidence thresholdsOperator correction loopDrift alerts
Deploy a model safely
12Models serving in production
0.91Median AUC, trailing quarter
48hRetrain cadence, champion set
Model quality trend across retrain cycles
  • RETRAINchurn v8 promoted+0.02 AUC
  • DRIFTincome_dist shiftedwatching
  • SHAPtop driver days_since_orderexplained
  • CALIBreliability gap 0.01pass
  • ALERTfraud precision 0.94hold

WAYS TO START

Benchmark first, then train and deploy

Start here

Dataset & Evaluation Benchmark Sprint

Create a clean, labeled 500-case benchmark and baseline score for your task.

2 weeks
  • Data audit, cleaning & PII removal
  • 500 stratified & human-verified test cases
  • Baseline prompt vs compact model comparison
  • Cost, latency & accuracy recommendation
Build an eval benchmark ↗
Most asked

Custom Model / Fine-Tuning Build

Train, evaluate and deploy a production classifier, scorer or fine-tuned LLM.

4 to 8 weeks
  • Training dataset curation & augmentation
  • Model training / LoRA fine-tuning
  • Shadow-mode validation + inference API
  • Monitoring & retraining pipeline
Scope a custom model ↗
Cost cut

LLM Cost & Latency Distillation Sprint

Replace an expensive high-volume prompt with a distilled specialist model.

3 to 4 weeks
  • Production log sampling & teacher labeling
  • Compact model distillation & eval pass
  • Router setup with frontier fallback
  • 35 to 70% inference cost reduction
Cut our inference bill ↗

Adjacent disciplines

Where Machine Learning connects

Pipelines

Data Engineering

Feature pipelines, dbt models and dataset cleaning underneath ML.

 Operations

MLOps & Governance

Model serving, drift monitoring, SLOs and rollback gates.

 Applications

AI Integration

Wire your models into customer assistants, documents and workflows.

 Weights

Deep Learning

LoRA/QLoRA fine-tuning, DPO alignment and GPU serving.

Benchmark · Train · Calibrate

Ready for models that beat promptson both speed and cost?

Share your dataset or prediction goal under NDA. We reply with a benchmark plan and fixed scope within one business day.

Scope My ML ModelSenior reply within 1 business day