Intelligent Systems That
Actually Work
We build AI that operates in production, not just demos. Multi-layer orchestration, domain-specific intelligence, and quality gates that prevent failures before they happen.
Built With Enterprise-Grade Infrastructure
Why Most Enterprise AI Fails
The "Chatbot" Approach
Single model, generic prompts, hope for the best.
The "Fine-Tune Everything" Approach
Months of training, massive compute, still misses edge cases.
Production Architecture
Every query flows through five coordinated layers. Each layer adds intelligence, reduces risk, and creates the audit trail your compliance team needs.
flowchart LR
subgraph Input
Q[Query]
end
subgraph L1[Layer 1: Intent Analysis]
Intent[Intent
Classification]
Route[Smart
Routing]
end
subgraph L2[Layer 2: Domain Detection]
Domain[Domain
Classifier]
Expert[Expert
Selection]
end
subgraph L3[Layer 3: Knowledge]
Retrieve[Document
Retrieval]
Reason[Domain
Reasoning]
end
subgraph L4[Layer 4: Quality]
Confidence[Confidence
Scoring]
Validate[Response
Validation]
end
subgraph L5[Layer 5: Output]
Response[Grounded
Response]
Escalate[Human
Escalation]
end
Q --> Intent --> Route
Route --> Domain --> Expert
Expert --> Retrieve
Expert --> Reason
Retrieve --> Confidence
Reason --> Confidence
Confidence --> Validate
Validate -->|Pass| Response
Validate -->|Fail| Escalate
Intent Analysis
Before any expensive operation, we classify intent. Conversational queries get canned responses. Technical queries get routed to the right pipeline. Navigation queries go directly to apps.
Domain Detection
Insurance claims need different handling than underwriting questions. Our domain classifier routes to specialized knowledge bases and expert models trained on your specific terminology.
Knowledge Layer
Hybrid retrieval combines semantic search across your documents with domain-specific reasoning models. Facts come from your data. Interpretation comes from fine-tuned experts.
Quality Gates
Every response gets a confidence score. High confidence? Deliver with citations. Medium? Add verification disclaimer. Low? Escalate to human before sending garbage to users.
Output Control
Grounded responses include source citations. Full audit trail for compliance. Graceful escalation paths when AI shouldn't answer. Your team stays in control.
MLOps Layer
Production isn't deployment. It's monitoring, drift detection, A/B testing, feedback loops, and continuous improvement. We build systems that get better over time.
Industry Solutions
We don't do generic. Each solution is architected for the specific terminology, compliance requirements, and workflows of your industry.
Automotive
Predictive maintenance, quality inspection, engineering knowledge systems, supply chain intelligence.
View solutions →Manufacturing
Process optimization, defect detection, operator assistance, inventory forecasting.
View solutions →Insurance
Claims processing, underwriting automation, member services, fraud detection.
View solutions →Healthcare
Clinical decision support, documentation AI, protocol guidance, administrative automation.
View solutions →Sales & Service
Support automation, knowledge retrieval, lead intelligence, personalized engagement.
View solutions →Other Industries
Finance, logistics, energy, government. If you have complex operations, we can help.
Let's talk →Engineering Stack
Orchestration & Routing
- Custom steering agents with deterministic routing rules
- Multi-model orchestration (GPT-4, Claude, Llama, Mistral)
- Intent classification with ML + pattern matching hybrid
- Domain detection with hierarchical classifiers
- Confidence-based decision trees
Knowledge & Retrieval
- Vector search: FAISS, Pinecone, Weaviate, pgvector
- Embeddings: BGE, OpenAI, Cohere, custom fine-tuned
- Reranking: Cross-encoders, ColBERT
- Hybrid search: Dense + sparse retrieval fusion
- Multi-source: Documents, APIs, databases, real-time feeds
Domain Models
- LoRA/QLoRA fine-tuning on your data
- Domain-specific adapters (claims, underwriting, etc.)
- Hot-swappable expert selection at inference
- Continuous training pipelines with feedback loops
- Model evaluation and A/B testing frameworks
Quality & Safety
- Multi-signal confidence scoring
- Hallucination detection with retrieval grounding
- Out-of-scope detection and graceful escalation
- Bias monitoring and fairness metrics
- Complete audit trails for compliance
Infrastructure
- Cloud: Azure, AWS, GCP (your choice)
- Serving: vLLM, TGI, Triton Inference Server
- Orchestration: Kubernetes, Docker, Terraform
- APIs: FastAPI, gRPC, WebSockets
- Security: OAuth2, SSO, SAML, encryption at rest
MLOps
- Experiment tracking: MLflow, Weights & Biases
- Model registry with versioning and rollback
- Monitoring: latency, throughput, error rates, drift
- Alerting and automated retraining triggers
- Shadow deployments for safe model updates
Proven Results
Real outcomes from production deployments. Numbers our clients have shared.
Deployed RAG-powered engineering assistant across PLM, specifications, and technical documentation. Engineers now get answers in seconds instead of hours.
Read case study →Multi-layer orchestration routes claims to the right handler with pre-populated context. Quality gates ensure accuracy before customer-facing responses.
Read case study →AI-predicted maintenance windows reduced unplanned downtime by 41%. Sensor telemetry analysis identifies failures 2-3 weeks before they occur.
Read case study →The 5-Layer Architecture for Hallucination-Free Enterprise AI
Learn the production patterns that prevent AI failures. How intent routing, domain detection, confidence scoring, and human escalation work together to create systems that know when they don't know.
Ready for AI That Works?
Skip the hype. Let's talk about your actual operations, your actual data, and what's actually possible. Free consultation, no sales pitch.