Custom AI Agents
An agent that handles your process has to know your process. We build agents against the workflow the audit mapped — your rules, your systems, your escalation paths — rather than adapting a template and hoping the gaps do not matter. That includes deciding what the agent should refuse to do on its own, which is the part templates never cover.
What’s included
- An agent built to your workflowthe steps, the decision rules, and the point at which it hands to a human, all matching how the work is actually done.
- Retrieval over your own knowledgeRAG against your documentation, databases and internal sources, so answers come from your material rather than from the model’s general training.
- Tool access, scoped deliberatelythe agent can act in your systems — and only in the systems and operations you granted it.
- Defined escalationthe cases the agent must not decide alone, routed to the right person with the context already gathered.
How it runs
- 01
Model chosen per job, not per vendor
We are deliberately model-agnostic — OpenAI GPT, Anthropic Claude, Google Gemini or an open-source LLM, picked on what the task needs. A build locked to one vendor inherits that vendor’s pricing and outages.
- 02
Grounded in your data before it is trusted with it
Retrieval first, so the agent answers from your knowledge base. An agent that improvises confidently is worse than no agent, because the errors are plausible.
- 03
Tested against the real cases, including the bad ones
The edge cases your team already knows about are the test set. An agent that handles the happy path is a demo, not a deployment.
What you get out of it
- An agent that handles your process, not a generic approximation of it
- Answers grounded in your own documentation and data
- Clear boundaries on what it may do alone and what it escalates
- No lock-in to a single model vendor
Questions about custom ai agents
Which AI models do you build on?
We are deliberately model-agnostic. We choose between OpenAI GPT, Anthropic Claude, Google Gemini and open-source LLMs per task, so your automation stays fast, reliable and future-proof rather than locked to one vendor. If a better model ships for a given job, the agent can move to it.
How do you stop the agent from making things up?
Retrieval-augmented generation against your own sources, so the agent answers from your material rather than from general training. Beyond that, the scope of what it is allowed to decide alone is defined explicitly, and anything outside it escalates to a person with the context already gathered.
Who owns the agent once it is built?
You do. It is built for your process, against your data, in your systems. Book a strategy call and we will walk exactly what the handover covers for the workflow you have in mind.