Deployment and Integration
The Pod ships production ready AI solutions into your stack with evaluation, observability, and controls built in.
Deploy with confidence, integrate where work happens
What these cover
Deployment and integration are the bridge from a successful build to reliable value at scale. We align environments, connect data, enforce access, instrument quality and cost, and roll out safely to users.
Scope
Environments and pipelines
Identity, access, and secrets
Data connectors and retrieval
Evaluation and release gates
Observability and incident response
Rollout, training, and support
Handover and ongoing governance
Environments and Pipelines
Environments: dev, test, and production with clear promotion rules
Pipelines: automated packaging and deployment for prompts, templates, and code
Versioning: prompts, datasets, and models tracked with change history
Release gates: evaluation thresholds for quality, cost, and latency before promotion
Identity, Access, and Secrets
Directory integration: SSO with your identity provider
Role based access: least privilege by job role and region
Secrets management: rotation and audit
Data handling: residency, retention, and encryption per policy
Data Connectors and Retrieval
Sources: data warehouses, lakes, document systems, ticketing, CRM, and knowledge bases
Retrieval: approved sources with citations and freshness checks
Caching: tuned for performance with clear invalidation
Lineage: datasets and transformations documented and owned
Evaluation and Quality Gates
Test sets: representative questions, documents, and scenarios
Metrics: accuracy, coverage, faithfulness, cost per interaction, and latency
Policies: human review for material decisions and edge cases
Reports: dashboards and release notes shared with product and risk
Observability and Incident Response
Telemetry: traces, logs, costs, and user signals
Alerts: drift, anomalies, and quality regressions
Runbooks: triage steps, rollback, and communication templates
Reviews: regular post incident and quality reviews with agreed follow ups
Rollout Plan
Audience: target roles and size of first release
Change steps: message map, leaders briefed, quick references published
Training: short sessions, office hours, and champions network
Support: help channel, response windows, and escalation path
Security and Compliance
Controls: mapped to risk tiers and delivery stages
Evidence: decision logs, approvals, and evaluation results retained
Privacy: DPIA templates, regional handling, and data subject processes
Reviews: scheduled control checks and periodic audits
Service Levels and Support Model
SLOs: availability, response time, and error budgets
Support hours: business hours with optional extended coverage
Ownership: product, platform, and risk roles with a clear RACI
Feedback: feature requests and usage insights looped into the backlog
Handover and Ongoing Governance
Artifacts: reference architecture, runbooks, dashboards, and checklists
Access: admin and operator roles with training
Reviews: cadence for performance, cost, and adoption
Scale plan: next groups and adjacent workflows with prerequisites
Deliverables
Deployment plan and architecture diagram
Configured pipelines with release gates
Connectors and retrieval over approved sources
Evaluation dashboard and observability setup
Security and compliance pack with evidence
Training materials and a support playbook
FAQs
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Yes. We prefer to deploy inside your cloud and tools whenever possible.
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No. We work with what you have and add only what is required for reliability and control.
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Retrieval with citations, strong evaluation, release gates, and human review where the risk is material.
What happens after going live
We provide support windows, dashboards, and a clean handover. Your teams own the solution with us on call as needed.