Custom AI agents. Built around your work.
For teams ready to move beyond fixed workflows. We design agents that use your systems, handle multi-step work and know when to ask for approval.
Context-aware execution
Connected tools
Human approvals
Measured performance
Your systems are connected. The work still needs you.
Custom AI Agents
When the next step depends on the situation.
Your team already works digitally. Yet someone still has to read the context, check several systems, decide what is missing and move the task forward. A fixed sequence cannot anticipate every variation.
A custom AI agent can choose its next permitted action based on what it finds. We define that responsibility with your team, prepare the information it needs and build it into the way you already work. Your people retain accountability and control.
Defined sequence
A workflow follows a route.
A signed contract arrives. The same checks run, a CRM record updates and a project is created. When the rules are stable, this is often the simplest and most dependable solution.
Context-led execution
An agent chooses the next step.
A client review is due. The agent checks the CRM, reads project updates and looks for missing information. It chooses which approved tool to use next, then prepares a brief with sources.
It operates inside defined permissions, budgets and stopping rules. Anything outside that scope returns to a person.
Is it the right fit?
Start with one task worth delegating.
A good candidate is frequent, time-consuming and variable, with accessible information and a clear definition of a useful result. Examples include preparing account reviews, coordinating document requests or investigating operational exceptions.
We map the task with its owner and compare a conventional workflow, an existing AI product and a custom agent. If a simpler option solves the problem, that is what we recommend. Poor source data and unclear ownership need fixing first.
ILLUSTRATIVE EXAMPLE · CLIENT REVIEW PREPARATION
“Prepare tomorrow’s client review.”
01 · READ
Gather the evidence
Retrieve the approved account record, project updates and relevant documents. Keep a link to each source and respect the user’s access rights.
02 · CHECK & ADAPT
Resolve the gaps
Compare dates and commitments. If information is missing, query another permitted source. If it remains unclear, flag it rather than inventing an answer.
03 · HAND OVER
Brief the account owner
Produce a sourced review brief, open questions and proposed next actions. The owner approves any customer message or consequential system change.
WHAT THE ACCOUNT OWNER RECEIVES
A review brief, not another chat to interpret.
Confirmed: current project status, with source links. Unresolved: a delivery date differs between the CRM and the latest update. Proposed: confirm the date with the project lead before drafting the client follow-up. Nothing is sent automatically.
UNDER THE HOOD
A controlled loop. Not unrestricted access.
The model proposes the next step. Your application enforces which information and actions are allowed. This boundary matters more than the number of agents.
Your information
CRM records, project tools and approved documents. Retrieval adds relevant context, with source references and access checks.
Agent runtime
Read context. Select a permitted tool. Inspect the result. Continue only within task, time and cost limits. Missing evidence triggers a handoff.
Useful output
A sourced brief, a completed check or a proposed action. Validate the result against business rules before it reaches the next system.
People set the authority. The system enforces it.
Reading approved data and drafting can run within scope. Sending externally, changing sensitive records or making commitments requires the agreed approval. Payments and accountable business decisions remain with authorised people.
Per-tool permissions · Validated inputs and outputs · Run logs · Bounded retries · Stop and escalation rules
What makes it dependable in practice?
The same principles, different responsibilities.
Illustrative use cases. Scope, feasibility and expected value are assessed against your actual systems and data.
From pilot to operation
A working capability, not a chatbot handover.
We establish a baseline, build a bounded pilot and test it against representative tasks, including missing data and failures. Together we review output quality, completion rate, review time and cost per task before expanding its responsibility.
The agreed scope covers integration, permissions, evaluation tests, run logs, documentation and team training. Ongoing monitoring, maintenance and changes are defined separately, with named owners and a clear way to pause or roll back. Model and provider costs are made explicit.
Bring one task your team keeps chasing.
Tell us which team owns it, where the information lives, how often it happens and what a good result looks like. We will help you decide whether an agent is justified, which foundations are missing and what a useful pilot should prove.
