AI agent development for enterprises
Agents that do work inside your systems rather than only answer questions, with clear limits on what they may act on alone and a complete record of everything they did.
An agent that can act is a different risk from a chatbot.
A chatbot that is wrong wastes someone's time. An agent that is wrong updates a record, sends a message or moves money. That difference is the whole engineering problem: what it may do alone, what needs approval, and how you find out afterwards exactly what happened.
What our agents are built with
Permissions with a boundary
Each agent gets the narrowest access that lets it do its job. What it may read, what it may change, and what it must ask about, defined explicitly.
An audit trail that holds up
Every action logged with what it saw, what it decided and why. When somebody asks what happened on the fourteenth, there is an answer.
Approval where it matters
Actions above a threshold you set wait for a person. The threshold moves as the agent earns trust, rather than being all or nothing on day one.
Evaluated before it goes live
Tested against real scenarios including the awkward ones, with results you see before it touches production. This is the step almost everyone skips.
Connected to real systems
Your ERP, CRM, database and internal tools. An agent that cannot reach your systems is a demo.
Watched after launch
Monitoring on what it does, how often it escalates and what it costs to run, so drift shows up as a chart rather than a complaint.
How we build agents
Discovery, build, certify, scale.
A senior-led delivery model built for revenue-critical commerce: predictable and transparent.
Discovery
We map the workflow, the constraints, and the compliance surface before a line of code.
Build
Senior engineers ship in two-week sprints. You see working software, not status decks.
Verify
Security and compliance are tested as we go (ADA/WCAG, PCI DSS, SOC 2 controls), never bolted on at the end.
Scale
We harden, instrument, and hand over, or stay on as your embedded product team.
Questions buyers ask us first
A chatbot answers. An agent takes actions in your systems: updating a record, creating an order, sending a message, escalating a case. That makes it more useful and considerably more dangerous, which is why permissions and audit matter as much as the model.
Narrow permissions, approval thresholds you control, evaluation before go-live, and read-only operation in production until it has proved itself. Every action is logged and reversible.
That is usually most of the project. We integrate with your ERP, CRM, databases and internal tools, and the difficulty is almost always there rather than in the model.
It depends on volume and model choice, and we size it during design rather than surprising you. Caching, smaller models for simple steps and hard limits are all part of the build.
Describe a decision your team makes fifty times a day.
With the rules, the exceptions and what happens when it goes wrong. That is the shape of a good first agent, and a senior consultant will tell you whether yours qualifies.