AI Platform & Orchestration

One control plane to route, run, observe, and govern multi-model and multi-agent apps, with cost policies, guardrails, and audit trails.

Timeline: 3-5 weeks
Team: architect, MLE, platform eng, QA
Typical stack: LangGraph/Temporal/Argo for orchestration; multi-model routing (OpenAI GPT-5/4, Anthropic Claude, Google Gemini, Llama/Mistral); vector stores (Pinecone, Weaviate, pgvector); observability (Datadog/Grafana, OpenTelemetry); on-prem/GovCloud deployment; SSO/SAML (Okta, Auth0); secrets management (Vault, AWS Secrets Manager)

What you get

3-5 week build
orchestrator + guardrails
run log + observability

Outcomes

Orchestration graph with retries, timeouts, circuit breakers, and backoff logic for multi-agent coordination
Multi-model routing engine with cost/latency policies, automatic fallbacks, and budget enforcement
Central run log with PII redaction, prompt versioning, actor tracing, and compliance-ready audit trails
Guardrails blocking unsafe actions (jailbreak, toxicity, groundedness) with eval suite passing agreed thresholds
Observability stack with traces, metrics, and alerting for latency spikes, cost anomalies, and error rates
SSO/SAML integration, role-based access control (RBAC), and secrets management for secure enterprise deployment

Selected work

Fraud screening: $0.18 → $0.04 per transaction.

8-agent detection pipeline for a global bank: transaction analysis, risk scoring, KYC validation, document verification, decision engine. False positives ↓41% via model routing (fast models for scoring, frontier models for complex review). Full audit trace for FINRA and FinCEN.

Multi-agent · Model routing · Audit trails

Prior authorization: 4.2 days → 6 hours.

5-agent clinical workflow: intake, clinical review, policy check, approval, notification. PII redaction and HIPAA audit trails enforced at the orchestration layer. On-prem deployment with SSO for 1,200 clinicians.

Multi-agent · HIPAA · On-prem

Inspect the execution rules

Shared model routing and run visibility must preserve each workflow's permissions, approval rules and recovery behavior.

Run the approval and retry example to inspect authorization, expiring approvals and duplicate calls in a synthetic local workflow.
Read the service-sector automation overview for context on the process being automated.

Where teams use it

Finance

Multi-agent fraud detection with real-time coordination

Global bank deployed 8-agent fraud detection system: transaction_analyzer → risk_scorer → KYC_validator → document_verifier → decision_engine. Orchestration reduced false positives by 41% via intelligent model routing (Llama for fast scoring, GPT-5 for complex case review) and cut per-transaction cost from $0.18 to $0.04. Audit log provides full trace for regulatory compliance (FINRA, FinCEN).

Healthcare

Clinical workflow automation with HIPAA-compliant orchestration

Healthcare system automated prior authorization with 5-agent workflow: intake_bot → clinical_reviewer → policy_checker → approval_engine → notification_sender. Orchestration enforced PII redaction, provided HIPAA audit trails, and reduced authorization time from 4.2 days to 6 hours. On-prem deployment with SSO integration for 1,200 clinicians.

Manufacturing

Supply chain optimization with agentic planning

Manufacturer deployed 6-agent supply chain planner: demand_forecaster → inventory_optimizer → supplier_coordinator → logistics_router → risk_assessor → decision_reporter. Orchestration reduced planning cycle from 3 days to 4 hours, cut expedited shipping by 38%, and provided real-time visibility into agent decision logic for executive reporting.

What we need from you

Agent/tool inventory with dependencies and call graph (e.g., fraud_detector → KYC_agent → document_parser)
Model providers and cost/latency constraints (e.g., GPT-5 for complex reasoning, Llama for high-volume classification)
Safety policies and risk tolerance (jailbreak detection, toxicity thresholds, groundedness checks, PII redaction rules)
Audit and compliance requirements (SOC 2, HIPAA, FedRAMP, GDPR; data residency, retention policies)
Target SLAs (p95 latency <2s, success rate >99%, budget cap $X/month, uptime 99.9%)

Proof points

SLA verification: p95 latency and success-rate targets met on 3 critical flows with load testing results
Cost governance: Budget caps enforced with real-time alerts; model switchovers proven with A/B test logs
Audit trail: Run log traces every request with actor ID, timestamp, prompt version, tool calls, outputs, and PII redaction proof
Safety validation: Guardrails block unsafe actions in test scenarios; eval suite passes agreed accuracy/safety thresholds
Orchestration graph sample: DAG visualization with retry logic, timeouts, and agent dependencies (available on request)
Cost optimization report: Model routing savings breakdown with before/after spend analysis (available on request)

Built for procurement

Pricing tiers: (1) Per-agent: $2,500-$5,000/agent/month for 1-5 agents; (2) Platform fee: $15k-$30k/month for 6-20 agents with observability and governance; (3) Custom enterprise: Quote for 20+ agents with SLA commitments and dedicated support
SLA commitments: 99.9% uptime, p95 latency <2s, success rate >99%, 1-hour response for P0 incidents, 4-hour response for P1; post-mortem reports within 48 hours
Security and compliance: SOC 2 Type II, HIPAA, FedRAMP, ISO 27001 certifications; PII redaction, data residency controls, encryption at rest and in transit; compliance evidence package available on request
Deployment options: SaaS (multi-tenant), dedicated VPC (single-tenant), on-prem (your data center), GovCloud/Azure Government; SSO/SAML integration (Okta, Auth0, Azure AD)
Deliverables: Orchestration platform with DAG editor, run log dashboard, cost tracking, guardrails config, observability stack; technical documentation, runbooks, training sessions; 30-day post-launch support
Observability and monitoring: Real-time dashboards for latency, cost, error rates; alerting for budget spikes, SLA violations, guardrail triggers; OpenTelemetry traces exported to your stack (Datadog, Grafana, Splunk)
Model provider flexibility: Bring your own API keys (OpenAI, Anthropic, Google) or use our managed endpoints; support for local models (Llama, Mistral) and custom fine-tuned models; no vendor lock-in
Knowledge transfer and handoff: Full source code, IaC (Terraform/CloudFormation), CI/CD pipelines, runbooks, and architecture diagrams; 2-week shadowing period with your team; optional ongoing managed services

Acceptance criteria

Orchestration graph deployed with retries, timeouts, circuit breakers, and backoff logic for multi-agent coordination
Multi-model routing engine configured with cost/latency policies, automatic fallbacks, and budget enforcement tested on 3 critical flows
Central run log capturing 100% of requests with actor ID, timestamp, prompt version, tool calls, outputs, and PII redaction validated
Guardrails blocking unsafe actions (jailbreak, toxicity, groundedness) with eval suite passing agreed accuracy thresholds (e.g., >95% precision)
Observability stack (Datadog/Grafana) integrated with traces, metrics, alerting for latency spikes, cost anomalies, and error rates
SSO/SAML authentication configured with RBAC for platform access; secrets management (Vault/AWS Secrets Manager) integrated
SLA targets met on load testing: p95 latency <2s, success rate >99%, cost per request within budget cap
Compliance requirements validated: Audit logs meet SOC 2/HIPAA/FedRAMP standards; data residency and retention policies enforced
Knowledge transfer completed: Technical documentation, runbooks, IaC, and 2-week shadowing period with your team
Post-launch support: 30-day on-call coverage with 1-hour P0 response, 4-hour P1 response; post-mortem process documented

Frequently asked questions

AI orchestration is a centralized control layer that manages routing, cost, and governance across multiple AI models and providers. Without it, teams independently spin up OpenAI, Anthropic, and open-source models with no visibility into total spend, no consistent safety policies, and no way to compare performance. Orchestration gives you one place to manage all of it.

Requests are routed to different models based on rules you define: complexity, cost sensitivity, latency requirements, or data sensitivity. Simple queries go to cheaper, faster models. Complex reasoning tasks go to more capable models. Sensitive data stays on private models. The routing logic is configurable and auditable.

Typically by 30 to 60 percent. Most enterprises are sending every request to their most expensive model because it is the only one configured. Smart routing sends 70 to 80 percent of requests to cheaper models that handle them just as well, reserving expensive models for the queries that actually need them. Budget caps prevent cost overruns.

Yes. The orchestration layer sits between your applications and the model providers. Your apps send requests to the orchestrator instead of directly to OpenAI or Anthropic. The switch typically requires changing one API endpoint per application. No model retraining, no application rewrites.