AI & Machine Learning
AI models that work
in production.
Custom machine learning and AI solutions — predictive models, computer vision, NLP, and MLOps infrastructure — built for your specific data, deployed into your stack, and monitored in production.
97%+
Average model accuracy
<100ms
Inference latency target
MLOps
Full pipeline included
Model DashboardLive
ModelChurn Predictor v3.1
Accuracy94.2%
F1 Score0.912
Avg Latency67 ms
Predictions / day142,800
Confidence distributionlast 1,000 calls
73%
of ML projects never reach production
$3.1T
enterprise value unlocked by AI by 2025
10–100×
cheaper than LLM API at production scale
<100ms
average inference latency we engineer for
Why Custom AI
Generic models leave money on the table
Custom vs Generic: The accuracy gap
Measured on domain-specific classification tasks across 12 client engagements.
Generic GPT-4 API (zero-shot)71%
Ethersofts Custom Model94%+
Generic models are trained on broad internet data. Your domain has specific vocabulary, edge cases, and ground truth they've never seen. Custom models close this gap — and the gap is exactly where your business value lives.
Data stays yours
Your training data and inference requests never leave your infrastructure. No vendor exposure, no compliance risk.
10–100× cheaper at scale
At 100k+ daily inferences, custom model hosting costs a fraction of LLM API call fees. The math turns fast.
Model Types
Pick your problem. We fit the model.
Classification
Binary and multi-class decisions at scale
Fraud detection, churn prediction, credit scoring, and diagnostic classification. XGBoost, LightGBM, and ensemble methods trained on your labeled data — with full precision/recall/AUC reporting tied to business-impact metrics, not just benchmark numbers.
Use Cases
Fraud DetectionChurn PredictionCredit RiskSpam FilteringMedical Diagnosis
0.94+
Typical AUC
vs 0.71 baseline
Architecture
XGBoost / LightGBM / Neural Ensemble
Model Performance
Precision96%
Recall92%
F1 Score94%
AUC-ROC98%
vs 0.71 AUC on generic GPT-4 baseline
Our ProcessFrom raw data to
From raw data to
production model
Every engagement follows a disciplined four-stage process designed around one guarantee: the model ships and performs.
Data discovery to first prototype2–4 weeks
Full model development and validation6–12 weeks
Production integration and deployment1 week
01
Data Discovery & Audit
We assess your existing data — volume, quality, labeling, and gaps. If you lack training data, we design a collection strategy. Bad data makes bad models; we fix this first.
02
Research & Architecture
We select the right model architecture for your problem — not the trendiest one. LLM fine-tuning, classical ML, or custom neural networks, chosen by what fits your data and latency requirements.
03
Train, Validate & Audit
Model development with rigorous cross-validation, fairness audits, and bias testing. Interpretability reports so your team understands what the model is actually doing.
04
Deploy & Monitor
Production deployment with automated monitoring for data drift and performance degradation. CI/CD for models — not just code. Monthly ROI reports included.
Why Not Just Use an API
Custom ML vs Generic LLM API
At small scale, APIs win. At production scale, custom models win on every dimension that matters.
| Criterion | Custom ML (Ethersofts) | Generic LLM API |
|---|---|---|
| Accuracy on your domain data | 94%+ | 68–78% |
| Inference latency | <100 ms | 500 ms – 3 s |
| Data privacy | On-prem / your VPC | Sent to vendor |
| Cost at 100k inferences/day | ~$50 / month | $2,000 – $8,000 / mo |
| Training on your data | Fully customised | Generic internet data |
| Interpretability (SHAP / LIME) | Included | Black box |
| Model ownership | You own it forever | Vendor dependency |
Case Study
From 3 days to 4 seconds
Challenge
InsurTech SaaS with 500+ daily applications was underwriting manually — 3 days per decision, inconsistent risk scoring across adjusters, and mounting defaults.
What We Built
XGBoost risk model trained on 4 years of claims history, integrated into their underwriting portal via REST API with a SHAP explainability layer for regulators.
Results
4 sec
Decision time (was 3 days)
+34%
Adjuster consistency
-12%
Default rate in 6 months
Ready to put your data to work?
Tell us your dataset and business problem. We'll come back with a realistic feasibility assessment and approach — no sales pitch.
Industries
AI across every vertical
Fintech & Banking
Prevent fraud before it happens, automate credit decisions at scale, and surface trading signals no analyst could catch manually.
Fraud DetectionCredit ScoringAlgo Trading
Healthcare
Accelerate diagnostics, predict patient risk earlier, and uncover drug interactions hidden in clinical data.
Diagnostics AIDrug DiscoveryPatient Risk
E-commerce
Personalise at scale — right product, right person, right moment. Forecast demand to eliminate stockouts and markdowns.
RecommendationsDemand ForecastDynamic Pricing
Manufacturing
Catch defects before they leave the line. Predict equipment failures weeks ahead. Optimise supply chain in real time.
Quality ControlPredictive MaintenanceSupply Chain
Legal & Compliance
Review contracts in minutes, flag regulatory exposure automatically, and score compliance risk across your entire document library.
Contract AnalysisRegulatory AIRisk Scoring
SaaS Products
Embed intelligence into your product — churn signals, feature recommendations, usage anomalies — without building an ML team.
Feature IntelligenceChurn PredictionUser Segmentation
"The model Ethersofts built cut our underwriting time from three days to under five seconds. Our adjusters now spend time on edge cases, not routine decisions."
RK
R. Krishnan
Head of Data Science, InsurTech SaaS · London
AI & ML SolutionsBuild ML that
Build ML that
ships to production
Tell us your use case. We will assess feasibility, recommend the right model approach, and give you a realistic timeline and cost.
Free consultation24-hour responseNDA on request
FAQ
Questions we get all the time
If yours is not here, reach out. We respond within 24 hours with a real answer from an engineer — not a sales pitch.
Not necessarily. Modern transfer learning and fine-tuning techniques allow us to build effective models with as few as 500–2,000 labeled examples. We assess your data reality in the discovery phase and recommend a strategy — whether that's synthetic data generation, data augmentation, or active learning to label the highest-value examples first.
Every model we build goes through cross-validation, holdout testing, and fairness auditing across demographic splits. We report precision, recall, F1, and AUC alongside business-impact metrics. For regulated industries, we provide SHAP-based interpretability reports so you can explain any decision the model makes.
GPT APIs are great for generic language tasks. Custom models excel when you need: (1) domain-specific accuracy GPT can't match, (2) sub-100ms inference latency, (3) data privacy — your data never leaves your infrastructure, (4) cost efficiency at scale — custom inference is 10–100× cheaper than API calls at volume.
Yes — integration is a core part of every engagement. We expose models as REST or gRPC APIs, as native library calls, or embedded directly in your application. We've integrated with Node.js, Python, Java, .NET, and Go backends, as well as SAP, Salesforce, and custom ERPs.
We build automated MLOps pipelines that monitor model performance in production, trigger retraining when drift is detected, and deploy updates with zero-downtime blue-green deployment. Monthly performance reports so you always know whether the model is still delivering ROI.
A focused model with clean data and a single use case typically runs $20k–$50k, while complex multi-model systems with computer vision or NLP and full MLOps run $50k–$120k. At production scale a custom model is often 10–100× cheaper to run than per-call LLM APIs, so the build cost is usually recovered through lower inference bills within months.
We ship a first working prototype in 2–4 weeks, full model development and validation in 6–12 weeks, and production integration in about a week after that. Timelines depend mostly on data readiness — if labelled data is missing, we add a collection or augmentation phase, which we flag honestly during discovery.
Yes. For language tasks we fine-tune open-source models like Llama 3 and Mistral on your own corpus, or build RAG systems over hosted models such as Claude (Anthropic) and GPT-4o when fine-tuning isn't required. Model choice depends on your accuracy, latency, cost, and privacy needs — and for sensitive data we can keep everything on your own infrastructure.