Solutions
LLM & Agent Systems
Turning a language model into a dependable business application takes more than a good prompt — it takes retrieval, orchestration, evaluation, and guardrails.
We design and build LLM-powered applications and agent workflows that are grounded in your data and accountable to your operational requirements.
What it is
LLM application development covers building products and internal tools on top of large language models: assistants, copilots, document and knowledge workflows, and multi-step agents that plan and take action within defined boundaries.
It also includes modernizing existing rule-based or scripted workflows where a language-model-based approach can handle more variation and context than the original system was designed for.
Where it helps
This work applies where unstructured information, natural-language interaction, or variable-input decision support are part of a workflow — customer and employee support, document review, research and drafting assistance, and structured task automation with human oversight.
What PacificTahoe does
We design the application architecture — retrieval strategy, prompt and context management, tool access, and evaluation approach — before writing production code, and we select models and frameworks based on the requirements of the task rather than a default preference.
For agentic workflows, we define the scope of autonomy deliberately: what an agent can decide versus what requires human review, and how actions are logged and reversible.
- Retrieval-augmented generation (RAG) and knowledge-grounded assistants
- Multi-step agent workflows with defined tool access and guardrails
- Evaluation harnesses to measure quality, safety, and regression over time
- Modernization of scripted or rules-based workflows using LLM components
Integration considerations
- Where source knowledge lives and how current it needs to be
- Latency, cost, and throughput requirements for the target workflow
- Human-in-the-loop checkpoints for higher-risk actions
Governance & security considerations
- Evaluation and testing before and after deployment, not only at launch
- Clear logging of model inputs, outputs, and agent actions for review
- Defined fallback behavior when the model is uncertain or unavailable
Building or rethinking an LLM-powered workflow?
We'll help you separate what's promising from what's production-ready.