Production AI for real decisions
Whether you’re forecasting demand or putting a copilot in front of your operations team, RWS builds the pipelines, evaluation, and guardrails that turn AI from a demo into infrastructure your business depends on.
Trusted by data and operations teams at growing companies
Build AI systems that actually hold up
Connect your data sources, evaluation harnesses, and runtime guardrails into one engineered system so models stay accurate, costs stay predictable, and failures get caught before your users do.
Pipelines that keep models fed
Versioned data pipelines move clean, labelled inputs from your warehouse into training and inference, with lineage you can audit on any prediction.
Evaluation as a habit
Golden sets, regression suites, and live scoring let every prompt change ship behind a measurable lift, not a gut feel.
Observability for every call
Latency, cost, accuracy, and drift land on the same dashboard, so the team responsible for the model can see exactly how it’s behaving in production.
Guardrails before the user
Input validation, output filters, and fallback chains keep hallucinations, prompt injections, and outages from reaching your customers.
“RWS built the pipelines and evaluation harness our forecasting models were missing. We finally know when a model is getting worse — and we know it before our planners do.”
Head of Data, Meridian Logistics
AI that moves the numbers leaders watch
Turn your forecasts, internal copilots, and decision systems into measurable lift on the metrics your board already tracks, with the precision operators need to act on the output.
Forecasts you can plan against
Demand, churn, and capacity models trained on your own data, evaluated against real outcomes and tuned for the cost of being wrong.
Copilots that earn their seat
Internal assistants grounded in your documentation, tickets, and tools so analysts and ops teams get answers in seconds instead of hours.
Decision systems, not dashboards
Routing, pricing, and triage models wired into the workflows where decisions actually happen, with humans kept in the loop where they should be.
ROI you can defend
Every deployment ships with a measurement plan, so the value the model is delivering is something you can put in front of finance, not infer.
Forecasts you can plan against
Demand, churn, and capacity models trained on your own data, evaluated against real outcomes and tuned for the cost of being wrong.
Copilots that earn their seat
Internal assistants grounded in your documentation, tickets, and tools so analysts and ops teams get answers in seconds instead of hours.
Decision systems, not dashboards
Routing, pricing, and triage models wired into the workflows where decisions actually happen, with humans kept in the loop where they should be.
ROI you can defend
Every deployment ships with a measurement plan, so the value the model is delivering is something you can put in front of finance, not infer.
How a logistics operator cut forecast error by a third
By replacing a spreadsheet model with a versioned pipeline and weekly retraining, the planning team reduced demand-forecast error enough to take a full day of safety stock out of the network.
An analyst copilot that paid back in one quarter
RWS grounded a copilot in the company’s own data warehouse and runbooks, turning hours of SQL and dashboard hunting into a question-and-answer flow the analytics team now relies on daily.
Catching model drift before customers did
Live evaluation and alerting flagged a silent accuracy drop in a credit-risk model the week it began, letting the team retrain and redeploy before a single bad decision reached production.
FAQs
RWS builds production AI systems end-to-end — data pipelines, model selection, evaluation, guardrails, and the integrations that put predictions inside the workflows where decisions get made.
Both. We pick the model that fits the job — frontier APIs where reasoning matters, open-source models where cost or control matters, and bespoke training where your data is the moat.
Your data stays in your environment by default. We work within your cloud accounts, use customer-managed keys, scope access by role, and avoid sending sensitive data to third-party providers without explicit approval.
Every system we ship integrates with the warehouse, identity, and tools your team already uses, and exposes the predictions through APIs, internal apps, or the workflows they belong in.
Every engagement ships with an evaluation harness and a measurement plan tied to the metric you’re trying to move, so the lift the model is producing is something you can show finance, not estimate.