Deploy
Turn a validated pilot into production
Production software integrated with your operations, data, systems, users, and business rules. Fully tested, deployed, and ready for real users at scale.
AI pilots fail when they are disconnected from operations
Your pilot proved the workflow works and creates value. Now it needs to operate reliably at scale. We handle end-to-end production development: architecture, infrastructure, integrations, security, QA, observability, and deployment.
Using a capable LLM is not enough. Production depends on access to the right data, integration with existing systems, user trust, business rules, governance, exception handling, auditability, and a measurable connection to operational value.
Deliverables
Production AI system
Fully built, tested, and deployed software integrated with your data, systems, identity, and users. Includes model and agent orchestration, data grounding, tool use, human handoffs, fallbacks, and exception handling.
Evaluation harness and quality gates
A task-specific evaluation harness built from real workflow data, edge cases, and failure scenarios. Measures output quality, task completion, agent decisions, tool use, and safety against defined release thresholds.
Guardrails and human control
Guardrails for inputs, outputs, data access, and tool use, with least-privilege permissions, human approval points, escalation rules, audit trails, and containment for unsafe or unexpected behavior.
AI observability and operational measurement
End-to-end traces across model calls, data retrieval, tool use, guardrails, and human handoffs. Dashboards track quality, task success, failures, latency, token cost, and operational value, with investigation views for exceptions.
AI release pipeline and change management
Automated CI/CD across staging and production, with infrastructure-as-code and versioning for models, prompts, tools, retrieval sources, and agent configurations. Every release passes evaluations and supports model fallback and rollback.
AI operating documentation and handoff
Architecture and data-flow diagrams, AI component inventory, evaluation standards, known failure modes, guardrail policies, and incident runbooks. Structured knowledge transfer gives your team what it needs to operate and improve the system.
Process
Architecture and setup
Turn the validated pilot into a production architecture. Define model and agent roles, data access, tool permissions, guardrails, human approvals, evaluation thresholds, and release criteria. Set up infrastructure, environments, CI/CD, and the first delivery sprints.
Build and integrate
Build in two-week sprints, with working software at the end of each sprint. Connect your data, systems, identity, and workflows. Implement agent orchestration, grounding, human handoffs, fallbacks, and exception handling.
Evaluate and harden
Test real cases, edge cases, and failure scenarios. Complete QA, security audits, performance optimization, and compliance verification until the system meets its release criteria.
Deploy and operationalize
Move the system through staging into production. Enable tracing, dashboards, alerts, and continuous evaluation. Support user onboarding and make a structured transition to ongoing operations.