RAG & knowledge systems
Retrieval, chunking, embeddings, reranking, citations and permission-aware access to private business knowledge.
AI development · RAG · agents · automation
AI features that connect to the real product and workflow.
We build production AI capabilities into web applications: retrieval systems, agents, copilots, semantic search, document processing and workflow automation connected to the data and tools the business already uses.
The model is only one component. Useful AI products also need retrieval, permissions, APIs, evaluation, fallbacks, observability, cost controls and a clear path for human review.
RAGretrieve · rank · cite
Agentstools · actions · workflows
Evalsquality · regression · safety
Operatetrace · cost · monitor
Production AI engineering
A good AI demo answers a prompt. A good AI product survives real users.
We design the surrounding system as carefully as the prompt: data access, model routing, retrieval, tool permissions, failure handling, evaluation and operational visibility.
AI agents & copilots
Tool-enabled assistants that can retrieve context, execute bounded actions and hand control back to a person when required.
ExploreAI workflow automation
Document processing, classification, extraction, routing and multi-step business workflows connected to existing applications.
ExploreEvaluation & observability
Quality tests, regression datasets, traces, token and latency monitoring, fallbacks and cost controls for production operation.
Explore
Integrated execution
AI works best as part of the product architecture, not as a disconnected chatbot.
Laravel or another application layer can own users, permissions, billing and workflows while Python-based AI services handle retrieval, inference, agents and evaluation through explicit interfaces.
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AI use-case and workflow architecture
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LLM and model integration
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RAG and semantic retrieval systems
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Agent and tool orchestration
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Document and data processing pipelines
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Evaluation datasets and quality checks
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Logging, tracing and cost monitoring
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Human-review and fallback workflows
Go deeper
Related capabilities.
Questions
What clients usually want to know.
Do you build more than AI chatbots?
Yes. AI can power search, document processing, workflow automation, copilots, recommendations, classification, extraction and tool-enabled agents.
What is the difference between a prototype and production AI?
Production systems need permissions, evaluation, monitoring, cost controls, failure handling, security and integration with the product’s real data and workflows.
Can AI be added to an existing Laravel or PHP product?
Yes. The existing application can remain responsible for the core product while Python-based AI services are introduced only where they add value.
Start with the business problem
Have an AI use case that needs to become a real product feature?
Bring us the workflow, data sources and actions the system should support. We will map the architecture around measurable product behavior rather than an AI demo.