Multi-Model AI Routing

    The right model for every request: cheaper, faster, PHI-safe, and vendor-neutral

    No single AI model is best at everything, and pricing and capability change monthly. Brandywine Consulting Partners designs multi-model routing layers that sit between your applications and model providers. Each request is classified and sent to the model that meets its quality bar at the lowest cost and latency. PHI-bearing traffic goes only to models covered by a BAA or hosted privately, and requests fall back automatically when a provider degrades. We pair the router with evaluation harnesses that score every candidate model on your own tasks, along with observability for cost, latency, and quality drift. Your organization can adopt new models in days instead of re-platforming each time the market moves.

    What We Do

    Service overview and the core capabilities BCP brings to every multi-model ai routing engagement.

    AI gateway architecture: single API, provider abstraction, and centralized keys and quotas
    Policy-based routing by task type, complexity, latency budget, and cost ceiling
    PHI / PII-aware routing to BAA-covered or privately hosted models
    Cascades and fallbacks: small model first, escalate on low confidence or failure
    Task-specific evaluation harnesses and golden datasets for model selection
    Semantic caching, prompt compression, and token-spend controls
    Observability: per-route cost, latency, quality, and drift dashboards
    Model onboarding playbooks for adopting new providers safely

    Key Benefits

    Lower Cost Per Request

    Routine tasks run on small, inexpensive models, and frontier models handle only the requests that need them.

    Resilience

    Automatic failover across providers and regions keeps AI features up when a single vendor has an outage or rate-limits you.

    Freedom to Adopt

    Applications call one gateway, so new models can be evaluated and switched in without code changes.

    Requests passing through scoring gates and branching to different AI models with fallback paths
    Every request, the right model

    Lower cost, PHI-safe policy, and automatic failover.

    Why BCP for Multi-Model AI Routing

    Vendor-neutral: we design routing across Azure OpenAI, Claude, Gemini, and private open-weight models
    PHI-aware policies that prove which models processed sensitive data
    Evidence-based model selection from golden datasets built on your real tasks
    Cost controls: cascades, semantic caching, budgets, and chargeback
    Resilience through automatic multi-provider failover
    Fast adoption of new models without application rewrites

    Who We Serve

    The audiences this service is built for, with the specifics that matter to each.

    Health Systems

    One governed entry point for every AI use case across departments.

    • Enterprise AI gateway
    • PHI-aware routing
    • Departmental chargeback

    Payers

    High-volume document and claims AI at sustainable unit cost.

    • Small-model-first cascades
    • Batch vs real-time routing
    • Quality gates for member-facing output

    Health Tech Vendors

    Multi-provider resilience and margin control for AI product features.

    • Provider failover
    • Per-tenant quotas
    • Model A/B evaluation

    Life Sciences

    Right model per research task, with data-residency controls.

    • Private model hosting
    • Long-context vs fast-model routing
    • Evaluation on domain corpora

    Typical Triggers

    If any of these sound familiar, you're in the window where this service delivers the most value.

    AI spend climbing

    Token costs grow faster than value because every request hits the most expensive model.

    Key sprawl

    Teams hold their own provider keys with no central policy, logging, or budget.

    PHI uncertainty

    No one can prove which model saw which patient data.

    Provider outage

    A single-vendor outage or rate limit took a production feature down.

    New model every month

    You want to adopt better models quickly without rewriting applications.

    No evaluation evidence

    Model choices were made by demo, not by measured performance on your tasks.

    Service Deliverables

    Three engagement models, same engineering rigor — choose the operating boundary that fits your team.

    BCP Hosted

    Fully managed by BCP

    • BCP-operated AI gateway with routing policies, failover, and spend dashboards
    • Managed evaluation runs when new models are released
    • Monthly cost and quality optimization reviews

    Client Hosted

    Delivered into client tenant

    • AI gateway and router deployed in your tenant (API Management or LiteLLM-based)
    • Routing policy set, golden datasets, and evaluation harness
    • Observability stack for cost, latency, quality, and drift

    BCP-Managed, Client Hosted

    BCP operates inside your tenant

    • BCP operates routing policy and model onboarding inside your environment
    • Quarterly model-market review and re-benchmarking
    • Budget enforcement and chargeback administration

    Service Timeline

    BCP's framework-driven methodology: Discover → Design → Build → Validate → Launch → Operate. Durations are typical and right-sized to scope.

    011–2 weeks

    Discover

    • Inventory AI use cases, providers, keys, and spend
    • Classify workloads by sensitivity, latency, and quality bar
    • Baseline cost and quality
    022–3 weeks

    Evaluate

    • Build golden datasets per task
    • Benchmark candidate models
    • Define routing and fallback policy
    033–5 weeks

    Build

    • Deploy gateway, router, and semantic cache
    • PHI policy enforcement and logging
    • Observability and budgets
    042–4 weeks

    Migrate

    • Move applications onto the gateway
    • Shadow-route and compare
    • Cut over with rollback
    05Ongoing

    Operate

    • Continuous evaluation
    • New-model onboarding
    • Cost and quality tuning

    Service Stack

    The BCP-preferred technology stack for this service, plus the common client stacks we support and operate.

    BCP Technology Stack

    Gateway

    Azure API Management AI GatewayLiteLLMKong AI Gateway

    Models

    Azure OpenAIAnthropic ClaudeGoogle GeminiLlama / Mistral / Phi via vLLM

    Evaluation

    MLflow EvaluatePromptfooCustom golden sets

    Observability

    OpenTelemetryLangfuseAzure Monitor

    Common Client Stacks We Support

    Azure-native

    API ManagementAzure AI FoundryAzure OpenAI

    AWS

    BedrockAPI GatewayCloudWatch

    Private / on-prem

    vLLMKubernetes GPU poolsRedis

    Hybrid

    Gateway in front of hosted and private models

    Representative Use Cases

    • Consolidating scattered AI provider keys into a governed enterprise gateway
    • Routing PHI workloads to private or BAA-covered models and generic tasks to cheaper public models
    • Cutting generative AI spend with small-model-first cascades
    • Multi-provider failover for patient- and member-facing AI features
    • Continuous evaluation to choose models per task with evidence
    • Chargeback and budget enforcement for AI usage by department

    Compliance

    The standards we engineer to — and how BCP ensures the controls are real, evidenced, and audit-ready.

    HIPAA

    Routing policy guarantees PHI reaches only BAA-covered or private models; every request is logged without storing PHI payloads.

    NIST AI RMF

    Documented model selection evidence and continuous measurement per route.

    SOC 2

    Central key management, access control, and change history for routing policy.

    EU AI Act

    Model provenance and technical documentation per routed use case.

    Service Proof Points

    Representative engagements with the technical challenge, BCP solution, measured outcomes, and the trust assets we deliver alongside the work. Client identifiers anonymized; details available under NDA.

    Medicare Advantage plan, 200K members

    Clinical NLP for HCC suspect coding

    Challenge

    Coder team reviewing 100% of charts manually; suspect identification slow and inconsistent.

    BCP Solution

    • Fine-tuned clinical-BERT model to surface HCC suspects from notes
    • Coder-in-the-loop UI for confirmation and feedback
    • Eval pack with subgroup fairness metrics

    Measured Outcomes

    +3.2×
    Coder review throughput
    0.88
    Suspect precision
    +17%
    Capture of true HCCs

    Stack

    Azure MLPyTorchHugging FaceMLflow

    Trust Assets

    • Model card with subgroup metrics
    • Independent clinical validation
    • HIPAA BAA
    Series C remote patient monitoring platform, 60+ hospital customers

    HL7 → FHIR pass-through broker for a digital health vendor

    Challenge

    Enterprise hospital customers required the vendor to broker HL7 v2 ADT/ORU feeds into a downstream analytics partner without the vendor ever storing the messages — a hard contractual constraint blocking new logo signings.

    BCP Solution

    • Built a multi-tenant, stateless HL7 → FHIR R4 conversion gateway on Azure Container Apps
    • Per-tenant cryptographic isolation with customer-managed keys in Key Vault
    • Event Hubs configured with minimum-viable retention for in-flight replay only
    • Summary-only telemetry shared with each hospital's SIEM
    • BAA + zero-retention rider made part of standard MSA

    Measured Outcomes

    11 in 9 months
    Enterprise deals unblocked
    180 ms
    Average HL7 → FHIR conversion latency
    0
    Payload bytes persisted by BCP
    −55%
    Customer security-review cycle time

    Stack

    Azure Container AppsEvent HubsFHIR R4Mirth Connect (stateless mode)Key Vault

    Trust Assets

    • HIPAA BAA + zero-retention rider
    • HITRUST inheritable controls
    • Penetration test report
    • Per-tenant isolation architecture brief

    Frequently Asked Questions

    01

    What is multi-model AI routing?

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    02

    How does routing protect PHI?

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    03

    How much can routing reduce AI costs?

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    04

    Do we have to rewrite our applications?

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    05

    How do you decide which model handles which task?

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    06

    Which gateways and models do you support?

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    Request a Multi-Model AI Routing Proposal

    Share the specifics so we can scope, price, and stand up the right team. Most proposals back within 3–5 business days.

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