Proven patterns across AI apps
Production-ready patterns across support, sales, RAG, and workflow automation — with measurable outcomes.
Choose by team, workload, and launch risk
Customer Support Copilot
Support, CX, operations
High-volume tickets need low latency and high accuracy.
Intent router + advisor mode + cached canonical answers
Keep latency low without routing complex cases to cheap models
- 30% cost reduction
- 99.6% success rate
- P95 < 900ms
Aurelis Bank reduced support token spend by 31%.
Sales/CRM Agent
Growth, RevOps, sales engineering
Outbound personalization is expensive and hard to govern.
Budget-aware generation + quality gates + CRM sync
Balance quality uplift against per-lead generation cost
- 2× reply rate lift
- 22% token savings
Northstar lifted replies 2x while cutting token spend 22%.
Knowledge Base QA
Product, education, knowledge teams
Model drift causes hallucinations in RAG pipelines.
Retrieval confidence routing + eval-backed fallback
Minimize hallucination without sending every query to premium models
- < 5% hallucination rate
- NRR > 120%
Composite profile: adaptive RAG for a 3.2M-student platform.
Developer Assistant
Developer tools, platform engineering
Agents call tools unpredictably and fail silently.
MCP tools + sandbox + trace replay
Let agents use tools while keeping execution scoped and replayable
- < 10% agent failures
- Root-cause in minutes
Composite profile: 12K+ daily agent tasks with failure replay.
Content Moderation & PII Guardrails
Trust & safety, security, compliance
Enterprise apps must filter sensitive data and unsafe content.
Policy-tagged routing + redaction + audit ledger
Block unsafe behavior without creating a brittle approval queue
- SOC2-ready audit logs
- Zero-trust tool access
Composite profile: HIPAA-bound support automation path.
AI App Marketplace
Founders, marketplace, product-led growth
Monetization and billing slow down launches.
Hosted app + dimensional pricing + Stripe-style billing
Launch monetization without building billing and entitlement plumbing
- Launch in days
- Full invoicing + seats
Modeled profile reaches a $480K ARR run-rate in the marketplace plan.
Every use case resolves the same production problems
| Production pain | Routing | Runtime | Governance |
|---|---|---|---|
| Model spend is unpredictable | budget-aware scoring | execution timeouts | spend alerts and invoices |
| Quality varies by workflow | goal-specific pools | eval-triggered retries | A/B reports |
| Agents fail silently | typed fallback reasons | trace replay | incident handoff |
| Security review blocks launch | regional controls | scoped tool execution | audit and DPA pack |
Stop rebuilding the same AI infrastructure for every app
Advisor mode
Let AI propose actions while a human approves high-risk writes.
Tiered model pools
Use small models for routine work and premium models for ambiguity.
Cache canonical answers
Cache repeated policy, help-center, and FAQ responses with tenant metadata.
Scoped tools
Give every tool an explicit scope, timeout, retry class, and audit record.
Eval before rollout
Gate policy and prompt changes on eval score, cost, latency, and failure rate.
Audit-ready traces
Keep model, prompt, policy, tool, and PII decisions attached to each workflow.
How to pick the first use case
Start with a high-volume workflow where success is measurable and humans can still approve risky actions. Prove cost, latency, success rate, and security evidence before moving into higher-risk automation.
Use the compatible endpoint to record traces without changing UX.
Turn on 10% policy routing for a workflow with human fallback.
Attach read-only tools first, then gradually allow writes.
Increase traffic based on evals, cost, failures, and audit evidence.
Make the first production use case reliable
We can help pick the first workflow, define success metrics, configure policies and traces, and prepare the security package for your internal review.