Forecasting, recommendations, churn, fraud detection, computer vision — we scope, build, deploy and operate ML systems that earn their keep.
Machine learning solutions are custom ML models and pipelines designed to solve a specific business problem — demand forecasting, recommendation, churn prediction, fraud detection, computer vision or NLP — engineered, deployed and monitored in production.
Demand, revenue, inventory, capacity — with proper backtesting.
Product, content and next-best-action systems.
Propensity models with clear uplift measurement.
Detection pipelines with review queues.
Detection, classification, OCR on your images or video.
Training, serving, monitoring, drift detection, retraining.
A plain answer up front. We'd rather not sell you something you don't need.
Pricing is quoted after discovery based on scope, team shape and delivery timeline.
The people you meet in discovery stay involved through architecture, delivery and launch.
Metadata, schema, page performance and semantic markup are part of delivery, not a post-launch add-on.
Tradeoffs, integrations and scope changes are documented so your team can audit decisions later.
Repos, infra, analytics and documentation live in your accounts from the beginning.
Real delivery examples tied to this service area, so buyers can move from claims to shipped work.
A logistics network reduced warehousing costs by deploying a custom machine learning forecasting model to predict regional inventory demand.
A product team replaced a brittle Python knowledge surface with a grounded Next.js and RAG stack to improve onboarding and support resolution.
A regulated fintech team needed Arabic retrieval and bilingual answer quality without moving sensitive data to external infrastructure.
“The difference was that Cuibit treated retrieval quality, evals and guardrails as part of the product, not as cleanup after launch. That is why the system earned trust internally.”
“The automation worked because Cuibit did not try to remove judgment from the wrong places. The workflow got faster, but the team still kept control where quality really mattered.”
Maybe. We audit data quality and volume before committing. Often the answer is 'yes for X, not yet for Y' — we start where the data supports it.
Use an LLM when the task is language-heavy and examples aren't labeled. Use ML when you have structured data and labels — it's faster, cheaper and more accurate.
Yes — full MLOps including monitoring, drift detection and retraining pipelines.
Pricing is quoted after discovery based on scope, team shape and delivery timeline. A focused prediction model, a full ML platform with MLOps and monitoring, and a computer-vision pipeline are each scoped differently, so we share a written proposal after a data audit.
A single prediction model takes 6–12 weeks including data audit, training, validation and deployment. Multi-model systems with MLOps take 3–6 months.
We set up automated monitoring for prediction drift, data distribution shifts and accuracy degradation. Retraining pipelines trigger automatically or on schedule depending on your needs.
Tell us about your project. A senior strategist replies within one business day — with a written first take.