Most generative AI pilots never ship. We build the ones that do.

A running GenAI system on your data, handed over with its source. The evaluation harness: your test set, scoring logic and acceptance thresholds. Measured unit economics at expected volume. And a scale, revise or stop decision presented with the evidence behind it. Two to four weeks, fixed scope, with a fixed fee agreed in writing before work starts.

No. The pilot is where most clients start because it produces evidence quickly, but the practice exists to put systems into production and keep them there. That covers production builds, integration with your identity, data and review path, and AI Change Assurance for agents that are already live.

We build the evaluation harness before the system. Three layers: a labeled test set with ground-truth answers, model-graded scoring for open-ended quality, and human review for edge cases. Every prompt, model or retrieval change re-runs it.

It depends on data sensitivity, cost per call at your volume, and your existing cloud footprint. Regulated data usually points to a model hosted inside your own cloud tenancy, under enterprise terms. High-volume simple tasks often run cheaper on small open-source models. Complex reasoning justifies a frontier model. Most systems we ship route across two or three. We hold no exclusive incentives; the recommendation follows the workload.

Hallucinations: grounding through retrieval, structured output constraints, and confidence-thresholded refusal. PII: classifier-based scrubbing on input and output. Prompt injection: input sanitization, channel separation between system and user messages, and output validation. We red-team every system before launch.

Model change is routine, not exceptional. One major cloud platform documents deprecation at twelve months and retirement at eighteen, with standard deployments auto-upgraded region by region and a 410 Gone response at retirement. AI Change Assurance builds a replay harness so you can prove a new model behaves acceptably against recorded production traffic before it reaches customers.

There is currently no broad regulatory requirement that enterprises revalidate a generative or agentic application every time an underlying model changes; certain regulated systems carry change-management or reassessment obligations, but those are use-case and jurisdiction specific. The stronger argument is commercial: an undetected change in agent behavior affects customers, revenue and operational cost well before it attracts a regulator.

Four reasons, consistently. The pilot ran on clean sample data and real data is messy. There was no evaluation harness, so nobody could prove the system was good enough to ship. Governance was deferred and legal blocked launch. And cost per transaction was never modeled, so finance stopped it at scale. Every engagement in this practice is built to close all four.

A RAG system retrieves from your data and generates a grounded answer: one turn, one answer. A copilot lives inside a workflow and helps a user complete tasks: many turns, one user. An agent takes a goal and acts: planning, calling tools, retrying, escalating. Most enterprise builds combine two of the three. The right pattern depends on the task and how much autonomy your risk posture allows.