Customer Support Agent
Our conversational assistant is handling live customer traffic on shopfronts today. We’re now rebuilding the deployment as a full agent — the first release on the platform described here.
MagBot.ai builds and operates AI agents for the operational work businesses can’t afford to get wrong. Every action reviewable, every decision traceable, done inside your own environment.
Demos are simple. That’s where projects stall, putting an agent to work that has a customer, regulator or contract behind it. Three things break each time.
A chat window is not a history. If someone comes back six months later and says, ‘Why did you make this decision?’ then ‘the model said so’ is not a good answer.
Customer files, patient files, batch files, contracts. For most teams, submitting it to a third-party API means the evaluation is done before it begins.
Generic tooling gives you a framework and says, “Good luck.” And when the agent gets it wrong in production it’s your problem and yours alone.
All MagBot agents are built the same way, no matter what industry they are in. Things work themselves out. The judgement stays where it belongs.
The agent reads from the sources your team already uses — documents, inboxes, tickets and the systems you run on. No migration, no new place to work.
It does the repetitive part: reading, extracting, checking, matching, drafting. The time-lagging, non-expert-leveraging parts of your team's day.
Results are presented as a ranked queue with reasoning and sources attached. Your people approve, edit or reject. Low confidence cases escalate themselves.
Human checkpointApproved work is written back to your systems with a complete record of what happened, what was used and who signed off.
Fully traceableNo, not a marketplace. All of our agents are built in-house by our own team, on our own platform, with our name on the result. We publish build status openly — you should know exactly what carries traffic today, what we’re building now, what we’d only start with a design partner beside us.
Our conversational assistant is handling live customer traffic on shopfronts today. We’re now rebuilding the deployment as a full agent — the first release on the platform described here.
Creates a structured, deduped draft with a proposed assessment from inbound case material, routed to a reviewer queue. Never submits by itself.
Screens scheduled searches against your inclusion criteria, flags what is important and records the reasoning behind every include and exclude decision.
Manages order status, changes and returns right through. Checks policy and stock before committing, and escalates anything outside the rules.
Reviews drafts and verifies clinical and administrative documentation against your templates, leaving the clinical judgement to the clinician.
Reviews production and quality records for exceptions and missing entries. Brings any deviations to the attention of a qualified reviewer, rather than closing them.
Same everywhere, high volume procedural work, done by people whose skills are wasted on it, where a mistake is more expensive than the task. We never build speculatively, we design-build with a partner in each sector.
Safety and compliance operations run on high volume, fixed deadlines and work that is mostly manual transcription — done by people qualified to do something far more valuable. This is the next investment and we are building it with the teams that do the work.
Inbound material has been turned into a structured draft, with each field traceable to its source
Candidate matches with existing cases, with evidence explaining the match
Scheduled searches against your inclusion criteria with reasons recorded
Structured data, consistent drafts, fully traceable, your house style
The first suite being rebuilt on the platform, and where we operate today. Accurately answered high volume conversations with clean escalation as soon as something should not be handled automatically.
Resolution and handoff between stores, logged and reviewable every interaction
Policy and stock checked before anything is committed on behalf of the customer
Anything that is outside the rules is sent to a human with full context attached
Administrative burden that takes clinicians away from patients — documentation, authorisation and review work that follows rules but still requires a trained eye on the outcome.
Drafted and checked against your template, with clinical judgement left to the clinician
Evidence collected and compiled ready for submission, ready for review
Discrepancies and gaps surfaced to a qualified reviewer instead of being silently corrected
Quality and compliance work is document and exception driven, where it’s a lot more valuable to find the problem early than to move the paperwork faster.
Check records for exceptions and missing entries before sign off
Deviations with context for a qualified reviewer — never auto-closed
Documents received against your requirements, gaps highlighted at receipt
Most AI vendors want you to send your data to their cloud. We do not. MagBot deploys into infrastructure you already own and have already evaluated — typically the difference between a pilot that launches and one that dies in vendor review.
Agents recommend. It’s your people’s call. That is not a configuration choice, it is baked into how the platform works where an action has real consequence.
SteppingEdge, the product engineering team behind MagBot.ai, builds and operates production systems for enterprise customers — not a lab, not a prototype shop.
It is more important than it sounds. Most agentic AI never survives contact with the real environment, the integrations, the permissions, the review workflow, the change control. We build the boring stuff. Because the boring stuff is the product in work that matters.
A senior team builds agents directly, not remotely coordinating delivery.
Systems that operate every day under real load, with real users and real consequences.
Comfortable inside your environment. Your cloud. Your VPS. Your controls.
You talk to the people working on it. No intermediate account layer.
Thirty minutes with the people building it. Not a demo — we want to understand the workflow, where the hours go, and whether an agent belongs anywhere near it. If it doesn't, we'll say so.