What others say:
The Protocloud Difference:
Notebook handed off, you figure out deployment 
Full API + CI/CD + monitoring delivered 
Custom ML projects at Protocloud start from $8,000 for focused, single-model solutions (e.g., a churn predictor or demand forecaster). Mid-complexity projects with data engineering, feature stores, and API deployment typically run $25,000–$60,000. Enterprise MLOps platforms with multiple models, monitoring, and governance run $60,000–$120,000+. Every project starts with a free data readiness assessment and fixed quote – no surprises. FAQ Schema Markup Note: Apply FAQ schema (application/ld+json) to this section for Google rich results.
A focused ML project (single model + API deployment) typically takes 8–12 weeks from kickoff. Projects requiring significant data engineering or multiple models take 12–20 weeks. We break every project into 2-week sprints with working demos – you’re never waiting 4 months to see something.
For tabular models (churn, forecasting, scoring), we typically need 12–18 months of clean transaction or event data with at least 1,000 labelled examples of the target event. For NLP, 500+ labelled documents is a reasonable starting point. Our data readiness assessment will tell you exactly what you have and what you need before any contract is signed.
Yes. We scope data integration as part of every project. Our data engineering team builds pipelines to consolidate data from CRM, ERP, marketing platforms, support systems, and databases into a unified feature store before model training begins. This is often the highest-value part of the engagement.
Absolutely. We regularly add ML prediction layers to existing BI dashboards, ERP systems, and CRM platforms. Your team keeps the reports they know, but they now include forward-looking ML predictions alongside historical data.
Yes – if your data is ready and the business problem is clear. We run a free data readiness assessment first. If your data isn’t ready for ML, we’ll tell you, and we’ll tell you what to fix first before spending a dollar on modelling.
🛡 Guarantee: If our ML solution doesn’t improve the target business metric by a measurable amount within 90 days of production deployment, we’ll work at no additional cost until it does.
We’ve seen messier data than yours – and cleaned it. Our data engineering team handles missing values, inconsistent schemas, duplicate records, and low signal-to-noise ratios. We scope data cleaning into the project budget upfront so there are no surprises.
🛡 Guarantee: Data quality scope is defined and priced before the project starts. We will not begin model development without signed-off data quality gates.
We define “good enough” as a business metric (e.g., forecast MAPE < 10%, churn recall > 85%) – not a vanity accuracy score. If we don’t hit the agreed business metric on holdout data before deployment, we iterate until we do.
🛡 Guarantee: We do not consider a model delivered until it meets the agreed business performance criteria on a held-out validation set.
Yes. We include SHAP explanations and model cards for every production model. Your team will know which features drive each prediction and how to override the model when business context warrants it.
🛡 Guarantee: Every model we deploy includes explainability documentation that your team can use to audit and interrogate predictions.
We build data drift monitoring and model performance tracking into every deployment. You receive automated alerts when performance drops below threshold, and our team investigates and retrains within 5 business days.
🛡 Guarantee: 3 months of post-launch monitoring, incident response, and one retraining cycle are included in every ML project at no additional cost.
