Rivac AI LabsLLM & Foundation Model Selection
With new frontier and open-weight models shipping constantly, picking the wrong one locks you into avoidable cost, latency, or compliance exposure for the life of a product. This service runs a structured, side-by-side evaluation of candidate models (OpenAI, Anthropic, Google, and relevant open-weight options) against your actual accuracy, cost, latency, and compliance requirements, using your own representative tasks rather than public benchmarks alone. We test the failure modes specific to your use case, not just headline capability scores, because the model that wins a leaderboard often isn't the one that survives your edge cases. You walk away with a defensible model choice and the evaluation harness to re-run the comparison as new models ship.
This is a capability we deliver for you as a service, not a role we’re hiring for.
How We’d Approach This
A clear, staged plan, not a black box
These are the service-specific steps inside our six-step process.
- 1
Define the accuracy, latency, cost, and compliance requirements specific to your use case and data sensitivity
- 2
Build a representative evaluation set from your own tasks and edge cases, not generic public benchmarks
- 3
Run candidate models (proprietary and open-weight) head-to-head against that evaluation set and score the results
- 4
Deliver a recommendation with the reusable evaluation harness so you can re-test as new models release
What You Get
Deliverables from this engagement
- Side-by-side model comparison scored against your own tasks
- Reusable evaluation harness for re-testing future model releases
- Cost and latency projections at your expected usage volume
- Written recommendation with trade-off rationale
Six Ways We Could Architect This
Different engagement, different build, pick the shape that fits
There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.
Read all 6 architectures as text
Lean Fast-Start Architecture
Setup
- Representative Task Set Defined
- Candidate Models Shortlisted
- Evaluation Harness Scaffolded
Testing
- OpenAI Model Test Run
- Anthropic Model Test Run
- Open Weight Model Test Run
Scoring
- Accuracy Cost and Latency Compared
- Failure Mode Analysis
Delivery
- Stakeholder Review Session
- Defensible Model Recommendation
- Reusable Evaluation Harness Delivered
- Handoff to Engineering Team
Cloud-Native / Managed Services Architecture
Managed Model Access Layer
- OpenAI API Endpoint
- Anthropic API Endpoint
- Google Model API Endpoint
- Hosted Open Weight Model Endpoint
Evaluation Operations
- Cloud Evaluation Harness Service
- Test Case Dataset Store
- Automated Scoring Pipeline
Synthesis Layer
- Cost and Latency Tracking Service
- Comparison Report Generator
Delivery Layer
- Cloud Hosted Comparison Dashboard
- Reusable Harness Repository
- Stakeholder Notification Service
Event-Driven & Automation-First Architecture
Triggers
- New Model Version Released
- Auto Trigger Re Evaluation
- New Representative Task Added
- Auto Run Against All Models
Automation Layer
- Automated Harness Execution
- Auto Score Accuracy Cost and Latency
- Auto Detect Regression vs Baseline
Review Gate
- New Model Outperforms Current Choice
- Auto Flag for Migration Review
- Auto Retain Current Recommendation
- Auto Update Comparison Report
Delivery
- Notification to Engineering Lead
- Published Updated Model Ranking
Enterprise-Grade & Compliance-Ready Architecture
Governance Layer
- Data Residency and Compliance Policy
- Vendor Risk Assessment Standard
- Access Control for Model Credentials
Secure Evaluation
- Vaulted API Key Management
- Role Based Evaluator Access
- Versioned Test Dataset Repository
Auditable Testing
- Compliance and Safety Test Battery
- Independent Security Review
- Failure Mode and Bias Log
Compliance Delivery
- Redundant Evaluation Evidence Archive
- Audit Trail Export
- Signed Off Model Selection
- Retention Schedule for Evaluation Data
AI-Augmented, Human-in-the-Loop Architecture
Data Ingestion
- Representative Client Tasks
- Historical Production Failure Cases
AI Analysis Layer
- AI Judge Model Scoring Engine
- AI Failure Mode Classifier
- Draft Model Ranking and Recommendation
Human in the Loop
- ML Engineer Review Session
- Ranking Holds Up on Edge Cases
- Flag Corrections Back to AI Judge
- Human Approved Model Choice
Delivery
- Defensible Model Recommendation
- Reusable Evaluation Harness
- Production Integration Handoff
Integration-Heavy / Ecosystem-Connected Architecture
Client Systems
- Client Application Codebase
- Existing Observability Stack
- Data Warehouse of Production Logs
Model Selection Core
- Model Evaluation Harness
- Accuracy Cost Compliance Model
- Model Recommendation
Two Way Sync Layer
- LLM Gateway Configuration Sync
- Observability Dashboard Sync
- Application Inference Layer
- Cost Monitoring Platform
Shared Governance
- Joint Review with Engineering and Security
- Published Model Selection Decision
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- Proof-of-Concept Scoping & Rapid Prototyping Sprints
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- Responsible AI & Governance Framework Design
Bias, explainability, and transparency frameworks aligned to ISO/IEC 42001, the NIST AI RMF, and the EU AI Act.
Ready to get started?
Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start, scoped small enough to prove itself before you commit to anything bigger.
Talk to us about LLM & Foundation Model SelectionMost engagements like this start as a $500–$2,500 pilot, see full pricing.