AI Solutions - SABA AI

Agentic Orchestration

The difference between a demo and production is routing. Our steering agents classify every query before expensive operations, determining the optimal path through your AI infrastructure.

Intent detection with hybrid ML + pattern matching
Cost optimization: simple queries skip LLM entirely
Deterministic routing rules you can audit and modify
Multi-model support with automatic fallback

flowchart TB
    Query[Incoming Query]
    
    Query --> Classify{Intent?}
    
    Classify -->|Greeting| Canned[Canned Response]
    Classify -->|Navigation| App[App Redirect]
    Classify -->|Factual| Retrieval[Retrieval Path]
    Classify -->|Reasoning| Expert[Expert Model Path]
    Classify -->|Complex| Hybrid[Hybrid Processing]
    
    Retrieval --> QA[Quality Check]
    Expert --> QA
    Hybrid --> QA
    
    QA -->|Pass| Response[Deliver Response]
    QA -->|Fail| Human[Human Escalation]

Domain-Specific Intelligence

Generic models fail on domain terminology. We train specialized adapters on your data using efficient fine-tuning (LoRA/QLoRA) that can be hot-swapped at inference time.

Domain classifiers route to specialized experts
LoRA adapters: 10-100x more efficient than full fine-tuning
Your terminology, your decision patterns, your institutional knowledge
Continuous retraining as your data evolves

flowchart TB
    Query[Routed Query]
    
    Query --> Detect{Domain?}
    
    Detect -->|Claims| Claims[Claims Expert]
    Detect -->|Underwriting| UW[Underwriting Expert]
    Detect -->|Service| Service[Service Expert]
    Detect -->|Compliance| Comp[Compliance Expert]
    
    subgraph Base[Base Model + Adapters]
        Claims
        UW
        Service
        Comp
    end
    
    Claims --> Inference[Expert Response]
    UW --> Inference
    Service --> Inference
    Comp --> Inference

Knowledge Retrieval

Your AI is only as good as its access to accurate information. We build retrieval systems that search across documents, databases, and APIs with semantic understanding.

Semantic search with state-of-the-art embeddings
Hybrid retrieval: dense vectors + sparse keyword matching
Cross-encoder reranking for precision
Multi-source: PDFs, wikis, databases, real-time APIs
Every response grounded with source citations

flowchart LR
    subgraph Ingest[Document Processing]
        Docs[Documents]
        Chunk[Chunking]
        Embed[Embedding]
        Index[Vector Index]
        Docs --> Chunk --> Embed --> Index
    end
    
    subgraph Search[Query Time]
        Q[Query]
        QEmbed[Query Embed]
        Retrieve[Top-K Search]
        Rerank[Reranking]
        Q --> QEmbed --> Retrieve --> Rerank
    end
    
    Index --> Retrieve
    Rerank --> Context[Retrieved Context]

Quality Gates

The most critical component. Every response is evaluated before delivery. Low confidence triggers graceful degradation, not hallucinated answers.

flowchart LR
    Response[Generated Response]
    
    Response --> Score{Confidence?}
    
    Score -->|High| Deliver[Deliver with Citations]
    Score -->|Medium| Caveat[Add Verification Note]
    Score -->|Low| Clarify[Request Clarification]
    Score -->|Very Low| Escalate[Human Escalation]
    
    Response --> OOS{Out of Scope?}
    OOS -->|Yes| Escalate

Confidence Level	Threshold	Action	Example Response
High	≥ 0.85	Full response with source citation	"Per Policy 4.2, coverage includes..."
Medium	0.70 - 0.84	Response with verification note	"Based on available information... recommend confirming with..."
Low	0.50 - 0.69	Request clarification	"Could you provide more details about...?"
Very Low	< 0.50	Human escalation	"Connecting you with a specialist who can help..."

Production Operations

Deployment is the beginning, not the end. Our systems include monitoring, feedback loops, and continuous improvement from day one.

flowchart LR
    subgraph Train[Training]
        Data[Training Data]
        Model[Model Training]
        Eval[Evaluation]
    end
    
    subgraph Deploy[Deployment]
        Registry[Model Registry]
        Stage[Staging]
        AB[A/B Test]
        Prod[Production]
    end
    
    subgraph Monitor[Monitoring]
        Metrics[Performance Metrics]
        Drift[Drift Detection]
        Feedback[User Feedback]
        Alert[Alerting]
    end
    
    Data --> Model --> Eval --> Registry
    Registry --> Stage --> AB --> Prod
    Prod --> Metrics --> Alert
    Prod --> Drift --> Alert
    Prod --> Feedback
    Feedback --> Data
    Alert --> Data

Observability

Real-time dashboards tracking latency, throughput, error rates, and confidence distributions. Know exactly how your AI is performing.

Drift Detection

Automated monitoring for input distribution shifts and model performance degradation. Get alerts before users notice problems.

Feedback Integration

Structured capture of user corrections and edge cases. Feedback flows directly into retraining pipelines for continuous improvement.

See It In Action

Technical deep-dive with our engineering team. We'll walk through architecture decisions and how they map to your specific use cases.

Schedule Technical Call

How We BuildProduction AI