LLM Orchestration
- Multi-agent workflow design
- Tool-calling guardrails
- Evaluation and regression suites
Private AI workflows, retrieval systems, agentic tools, and automation controls for high-stakes operations.
We build AI systems that can be inspected and governed. Instead of opaque prompts scattered through operations, we design retrieval, evaluation, tool use, review checkpoints, and observability into the workflow from the beginning.
Applied AI and automation engineering is the discipline of deploying large language models, retrieval systems, and agentic workflows in production environments where outputs carry business consequences. Unlike consumer AI where variability is a feature, institutional AI requires auditability, determinism, and governance. At Axionbay Technologies, we build AI systems designed for inspection and control. Our LLM orchestration practice implements multi-agent workflow architectures with tool-calling guardrails, evaluation and regression suites, and structured output enforcement. Retrieval-Augmented Generation (RAG) pipelines are architected with private knowledge retrieval, vector database architecture (Pinecone, Weaviate, pgvector), and grounded response generation with source attribution. We implement deterministic controls including constrained generation, human review checkpoints at consequential decision points, and output quality monitoring with drift detection. Data sovereignty is foundational: we deploy on private infrastructure with access-control boundaries and audit-ready observability so every AI output can be traced to its source documents and model version. Our deliverables include AI workflow architecture, retrieval and automation pipelines, evaluation harnesses with quality metrics, and governance documentation. We build AI systems for document-heavy operations, support and compliance workflows, and knowledge-intensive processes where outputs must be auditable and improvable—not black-box guesses.
Consumer AI tools like ChatGPT are not suitable for institutional use—they lack data sovereignty, audit trails, and output consistency. Our AI systems run on private infrastructure with access controls, retrieval from your proprietary knowledge base (not public training data), structured output enforcement, and full logging of every model decision. You own the data, the models are pinned to specific versions, and every output can be traced to its source documents.
Agentic AI systems go beyond simple prompt-response interactions. They dynamically decompose high-level goals into subtasks, invoke external tools (APIs, databases, code interpreters), and revise plans based on observations. For example, a regulatory compliance agent can autonomously audit contracts across multiple jurisdictions, flag anomalies, and generate structured reports—with human review checkpoints at consequential decision points. We build these systems with circuit breakers, idempotency-keyed tool calls, and output anomaly detection so they fail loudly rather than silently.
We implement four layers of reliability engineering: (1) pinned model versions with no silent updates, (2) temperature set to 0 for extraction and classification tasks, (3) structured output schemas enforced via constrained generation, and (4) post-generation validation against a factual consistency model. For recurring tasks, we deploy output similarity detection that flags anomalous generations before they reach end users.
Share the current product, architecture, or operational bottleneck. We will map the right delivery shape and identify what should be designed before the first implementation sprint.