AI consultancy & LLM integration
Practical AI integration that holds up in production — model selection, agent design, and LLM features built by senior engineers.
Last updated:AI consultancy is the practical integration of large language models into real products — selecting the right model, designing agents, and shipping LLM features that hold up in production rather than just in a demo. Revenant Systems builds AI features with Anthropic, OpenAI, and Google models, and self-hosted models via LMStudio and llama.cpp.
What is practical AI integration?
Practical AI integration means building LLM features that work reliably for real users and real data, not just in a prototype. Practical AI integration covers model selection, prompt and agent design, evaluation, and the guardrails that keep model outputs correct, fast, and safe once they reach production.
Revenant Systems selects models per task across Anthropic, OpenAI, Google, and OpenRouter, and runs local models with LMStudio and llama.cpp where data residency or cost demands it.
- Model selection and evaluation
- Agent and tool design
- Prompt engineering and guardrails
- Production monitoring
Can AI agents drive your existing application?
Yes, through tool exposure: an existing application's search, actions, and workflows are wrapped as typed tools an AI agent can call. Revenant Systems exposes existing applications to LLMs as tools agents can drive, automating multi-step workflows that previously required a person.
- Typed tool and function definitions
- Agent orchestration
- Workflow automation
What do teams use AI integration for?
The recurring use cases are operational rather than novelty: assistants that search and act on existing systems, document workflows that extract and validate structured data, and assessments of whether a workload can run on local models. Each one pairs a model with the tools, guardrails, and evaluation that make it dependable.
| Use case | Why it fits |
|---|---|
| Internal support assistant | Searches and acts on your existing systems through typed tools |
| Quoting assistant | Calls your pricing logic as a tool rather than guessing at it |
| Document review workflow | Extracts and validates structured data with typed, checked outputs |
| Operations assistant | Drives multi-step workflows in an existing application |
| Local-model assessment | Establishes whether data-residency or cost constraints can be met on-premise |
Which models and frameworks does Revenant use?
Revenant Systems works across hosted and local models. Hosted models come from Anthropic, OpenAI, Google, and OpenRouter; local and self-hosted models run through LMStudio and llama.cpp. Application code is typically Python with Pydantic for typed, validated model inputs and outputs.
- Anthropic
- OpenAI
- OpenRouter
- Python
- Pydantic
- LMStudio
- llama.cpp
What's included
- Model selection and evaluation
- Agent and LLM feature design
- Tool / function interfaces for your app
- Guardrails and production monitoring
Stack Anthropic · OpenAI · Google · OpenRouter · Python · Pydantic · LMStudio · llama.cpp
Frequently asked questions
Our AI feature works in the demo — will it survive production?
That is exactly the question the AI production-readiness audit answers: a fixed-scope assessment of evaluation coverage, guardrails, prompt and tool design, model choice with its cost and latency profile, and the failure modes that surface once real users arrive — delivered as a severity-ranked report with a hardening roadmap.
Can our data stay on our own infrastructure?
Yes. Where data residency or cost demands it, Revenant Systems runs local and self-hosted models through LMStudio and llama.cpp rather than hosted APIs. Model selection is per task — hosted models from Anthropic, OpenAI, Google, and OpenRouter where they fit, local models where they don't.
How do you stop an LLM feature giving wrong answers?
You can't eliminate model error, but you can measure and contain it. Practical AI integration pairs evaluation — so quality is measured and regressions are caught when models or prompts change — with the guardrails and production monitoring that keep outputs correct, fast, and safe in front of real users.
Which model should we use?
The one the evaluation picks. Revenant Systems selects models per task across Anthropic, OpenAI, Google, and OpenRouter — weighing quality against cost and latency for your actual workload — and revisits the choice as models change, since evaluation coverage makes switching safe.
Do you use AI to build software yourselves?
Openly, and with care. AI speeds up the work, but every output is read, tested, and owned by a senior engineer before it ships. We don't outsource our thinking to a model — only our typing; judgement, architecture, and correctness stay human.
Does an AI feature need a data pipeline behind it?
Usually. Model quality rests on data quality — an LLM feature is only as reliable as the data that reaches it, so AI work often pairs with data engineering: the pipelines that feed features dependably sit underneath the model.
Our AI project has stalled — can you rescue it?
Often, yes — and the starting point is evidence rather than a rewrite. The AI production-readiness audit assesses where the feature actually fails — evaluation coverage, guardrails, prompt and tool design, model choice, cost — and delivers a severity-ranked report with a hardening roadmap. Sometimes the fix is a redesign; more often it is measurement and guardrails the project never had.
Every engagement follows the same process — see how we work.