// Automate
AI that operates inside your business processes
LLM integrations, AI decision systems, intelligent data processing, and autonomous agents. We design AI workflows that work reliably at production scale — with observability, guardrails, and human oversight where the stakes demand it.
// Services
AI workflow services
- RAG for private business knowledge
- LLM-powered document processing
- AI quality classifiers and routers
- Custom agent tool development
- LLM observability and cost tracking
AI Process Integration
Embed AI capabilities into existing business workflows.
AI Decision Systems
Automated decisions with human-in-the-loop review patterns.
LLM & GPT Integrations
GPT-4, Claude, Gemini — API integrations for your product.
AI Data Processing Workflows
Extract, classify, and transform data with AI models.
Custom AI Agents
Autonomous agents that operate within defined task boundaries.
// Standards
AI engineering standards
Task-appropriate models
Using GPT-4o for simple classification wastes money and adds latency. We select the smallest, fastest model capable of the task — and benchmark before deciding.
Guardrails and validation
Output validation, JSON schema enforcement, toxicity filtering, and PII detection on every AI output that reaches users or downstream systems.
Retrieval-augmented generation
RAG reduces hallucination risk for knowledge-intensive tasks. We design chunking strategies, embedding models, and retrieval pipelines specific to your data.
LLM observability
Every LLM call traced with LangSmith or Langfuse — prompt versions, token costs, latency, and output quality tracked across model versions.
PII and data handling
AI workflows that process personal data include PII detection and masking before sending to third-party model APIs, with clear data processing agreements.
Human review loops
Low-confidence AI outputs route to human review queues. We never design fully autonomous AI systems for consequential decisions without human oversight mechanisms.
// Technology
AI workflow technology stack
LLM APIs
Orchestration
Vector Stores
Backend
Data Processing
Monitoring
// Process
From use case to monitored AI workflow
Use Case Discovery
2–3 daysIdentify high-value AI integration points in your workflows. Map data availability, quality, volume, and latency requirements that will constrain model selection.
// FAQ
Common questions about AI workflow integration
Which AI models do you work with?+
OpenAI GPT-4o and GPT-4o-mini, Anthropic Claude 3.5 Sonnet and Haiku, Google Gemini 1.5, Mistral, and open-source models via HuggingFace or Ollama for on-premise deployments. Model selection is based on the specific task requirements.
Can you build AI features that work on our private data?+
Yes — through RAG (Retrieval-Augmented Generation) we index your private documents, databases, or knowledge bases into vector stores, enabling the LLM to answer questions about your specific data without fine-tuning.
What are AI agents and when do they make sense?+
AI agents are LLMs with tool use — they can call APIs, query databases, send messages, and make sequences of decisions autonomously within defined boundaries. They make sense for multi-step tasks where the workflow is complex and variable.
How do you prevent AI hallucinations in production?+
Structured output schemas (JSON mode), retrieval augmentation for factual tasks, confidence thresholds that trigger human review, citation requirements for knowledge-based responses, and comprehensive output validation before downstream use.
How do you handle data privacy when using OpenAI or Anthropic APIs?+
We can configure API settings to opt out of model training on your data. For sensitive use cases, we implement PII masking before sending data to third-party APIs, or recommend on-premise open-source alternatives like Llama 3.