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LLM pipelines and end-to-end workflow automation, decided properly.

Where to integrate language models, where to automate end-to-end, and the contracts, evaluation harnesses and rollback plans that make it survive contact with operations.

Dynamis Advisory — Integration provides decision-grade counsel on LLM pipelines and workflow automation: model selection (OpenAI, Anthropic, Gemini, Llama, Mistral, Qwen), retrieval-augmented generation, agentic workflow design, evaluation harnesses (Promptfoo, OpenAI Evals), and the rollback contracts that keep an LLM in production. Implementation runs through Dynamis Digital — Integrate; the strategy lives here.

  1. Vision
  2. Integration
  3. Architecture
  4. Governance

Workstreams

Where Integration counsel earns its place.

The decisions before you wire an LLM into anything that matters. We engage with the questions — selection, isolation, evaluation, rollback — that vendors won’t answer because the answer is sometimes "don’t".

LLM selection & isolation

Where to run language models, which ones, and what data ever sees them. We help pick between hosted APIs (OpenAI, Anthropic, Gemini), private deployments (Llama, Mistral, Qwen) and the right tenancy model for the data you actually handle.

Retrieval-augmented generation pipelines

When a model needs to answer with your knowledge, not the open internet. Vector store choice, chunking strategy, freshness, citations, evaluation harness, and the boundary between "the model knows" and "the model retrieved".

Agentic workflow design

Multi-step automations that take a brief and finish a job — reviewing documents, drafting responses, syncing systems, escalating to a human when uncertain. We design the orchestration, the guardrails, and the audit trail.

Evaluation, rollback & cost contracts

The unsexy part: golden datasets, regression suites, drift alarms, kill-switches, and a written budget contract for tokens spent. Without these, an LLM pipeline is a demo, not a system.

The implementation arm — private LLM hosting, voice agents, ERP automation, agentic plumbing — lives at Dynamis Digital — Integrate. Advisory is where it’s decided; Digital is where it gets wired up.

Engagement shape (Briefing, Review, Fractional) lives on the Advisory overview.

Common questions

FAQs

Here are some of our most frequently asked questions. Can't find what you're looking for? Reach out to our support team.

When should I run a hosted LLM versus a private deployment?
Hosted (OpenAI, Anthropic, Gemini) for general-purpose tasks where the data is already public or the convenience-and-quality trade is worth it. Private (Llama, Mistral, Qwen running in your tenancy) for confidential documents, regulated workloads, or where vendor lock-in carries unacceptable risk. The decision is workload-by-workload, not blanket policy.
How do you evaluate an LLM pipeline?
Golden datasets that match the actual task, regression suites run on every change, drift detectors comparing live outputs against historical baselines, and human-in-the-loop scoring for quality gates that automation cannot capture. Evaluation harnesses are tools (Promptfoo, OpenAI Evals, custom) plus a written methodology — without both, an LLM pipeline is a demo, not a system.
Where does the implementation actually happen?
At Dynamis Digital — Integrate. Advisory writes the decisions: which model, which architecture, which evaluation regime, which rollback. Digital wires it up: hosted infrastructure (Cloudflare Workers AI, AWS Bedrock, on-premise GPU), pipeline code, monitoring, and the agentic plumbing into your existing systems (Slack, Microsoft 365, Salesforce, custom databases).
What is retrieval-augmented generation (RAG)?
A pattern where a language model answers using documents retrieved from your own knowledge base — vector store (Pinecone, Weaviate, pgvector), chunking strategy, freshness pipeline, and citation-back-to-source. RAG is the boundary between "the model knows" (parametric) and "the model retrieved" (grounded). Most enterprise LLM deployments need RAG; very few do it well.
What is an evaluation contract and a kill-switch?
An evaluation contract is the written threshold below which the pipeline must not run — accuracy floor, latency cap, cost ceiling. A kill-switch is the operational mechanism (feature flag, traffic shaper, runtime guard) that takes the pipeline offline when the contract is breached. Both are non-negotiable for production-grade LLM deployment.

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