RAG Pipelines
Document ingestion, chunking, embedding, and retrieval designed around your source material.
AI Integration
Production AI systems.
Not experiments. Not demos.
Most AI demos fall apart in production. We design the system around retrieval, fallbacks, observability, and a product experience your team can maintain.
Capabilities
Production AI, end to end.
Each system starts with the constraints that decide whether it can be operated after launch.
Multi-Agent Systems
Orchestrated agents for research, synthesis, and multi-step workflows with explicit handoffs.
LLM Routing
Model routing based on task complexity, cost, and latency, with a defined fallback path.
Streaming and Caching
Streaming responses and caching designed for a faster product experience and controlled API usage.
Production Error Handling
Fallbacks, retries, rate-limit handling, and monitoring designed to make failures visible.
Vector Databases
Storage and retrieval choices evaluated against your data volume, latency needs, and constraints.
Implementation proof
The work is designed to be inspectable.
Source-aware answers
Retrieval and citations are planned with the source data, rather than layered on after a prototype.
Known failure paths
Timeouts, provider errors, and uncertain answers have an explicit product response.
Operating signals
Latency, errors, and cost are surfaced so the team can make informed changes after launch.
FAQ
AI Integration, answered directly.
What AI integrations do you build?
RAG pipelines, multi-agent systems, LLM routers, AI chatbots, document processing pipelines, real-time AI streaming interfaces, and custom AI-powered features for SaaS products. All built to production scale, not just demos.
What AI models do you work with?
OpenAI GPT-4 and o-series, Anthropic Claude, Google Gemini, and open-source models via Ollama or Hugging Face. Model selection depends on latency, cost, and accuracy requirements for your specific use case.
How do you achieve high AI accuracy in production?
Systematic prompt engineering, retrieval-augmented generation with well-structured knowledge bases, evaluation frameworks, fallback chains, and continuous monitoring.
How do you prevent AI hallucinations?
RAG with verified knowledge bases, structured output validation, confidence scoring, source citation, and human-in-the-loop checkpoints for high-stakes responses. No AI system ships without an evaluation suite.
How much does AI integration cost?
AI integration is included in Launch Build ($30,000 fixed) and Product Build ($60,000 fixed). Standalone AI integrations into existing products are scoped per project. Book a free discovery call to discuss your specific requirements.
Next step
Start with the production boundary.
Bring the data source, product workflow, and known constraints. We will identify the smallest responsible path.