Production RAG: run evals before you ship to customers
Retrieval quality, citation coverage, and regression suites matter more than model choice. Here is the eval ladder we use before any copilot touches production traffic.
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We implement production-grade AI tooling across automation, models, data, and operations—aligned with your cloud standards and compliance requirements.
We treat agents like product features: scoped tools, golden conversations, rollout plans, and runbooks your team owns after launch.
Use-case discovery tied to deflection and handle-time KPIs
Golden conversations and eval gates before customer rollout
Tool permissions, citations, and human approval paths
Observability, cost controls, and handoff to your ops team
Support-heavy, document-rich, and workflow-driven teams benefit most when agents are grounded, auditable, and integrated—not bolted onto a generic chat window.
Clinical and member-support copilots with HIPAA-aware retrieval, review queues, and audit trails.
Governed assistants for policy, KYC, and servicing—with citations and role-based tool access.
Dispatcher and field copilots over SOPs, tickets, and shipment status with escalation paths.
In-product help and onboarding assistants with per-tenant budgets, evals, and feature flags.
Order-status and returns bots connected to OMS and CRM with measurable deflection.
Advising and enrollment assistants grounded in approved content—not open-web answers.
Citizen-service bots with retention rules, PII handling, and human takeover for edge cases.
Maintenance and quality copilots over manuals, sensor alerts, and work orders.
Representative programs across RAG, in-product copilots, and ops automation—with eval gates and handoff your team can run.
Discovery, golden sets, pilot channels, hardening, and operate—with one squad accountable from week one.
Map intents, data sources, and success metrics—plus a security review of tools and retention before build.
Ship thin slices with eval harnesses on real tickets and conversations—not synthetic demos only.
Add guardrails, rate limits, fallbacks, and production observability aligned to your cloud standards.
Runbooks, prompt versioning, and enablement so support and engineering can improve the agent after launch.
Common questions about building chatbots and AI agents for production.
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Practical notes on architecture, delivery, and shipping software your team can operate—not generic consulting filler.