Wizeb — Custom AI Agents
An AI agent is more than a chatbot. It queries your CRM, triggers workflows, checks availability, drafts documents, calls APIs, and escalates to the right person — all within a single conversation. Wizeb builds agents that are deeply integrated with your systems, trained on your context, and built to handle real business volume.
These are the most common custom ai agents systems we build. Most projects combine elements from several areas.
Responds to every inbound enquiry within minutes — 24 hours a day. Asks the right scoping questions, scores by intent and budget, books meetings for warm leads, and routes cold ones to nurture sequences. Your team only touches prospects who are ready.
Handles FAQs, order status, returns, appointment booking, and account queries — resolving 60–80% of tickets without human involvement. Escalates to your team with full context when a human is genuinely needed.
Automates repetitive internal workflows triggered by people or systems — drafting reports, extracting data from documents, populating CRMs, generating briefings, and surfacing the right information at the right time.
All our custom ai agents solutions include token optimization as standard — reducing your ongoing API costs by 40-70% through smart routing, caching, and efficient architecture.
Learn about AI Cost OptimizationNo slide decks, no vague roadmaps. Here's exactly how a project runs from first call to live deployment.
We identify exactly where the agent adds value — which conversations, which decisions, which handoffs. We define what success looks like in measurable terms before writing a line of code.
We pick the model, tools, memory strategy, and integration points that fit the use case. All integrations are MCP-native by default — reusable across AI providers, with enterprise authorization and audit logging built in. Simple where simple works. Complex where it has to be.
We develop in short cycles using your actual data and edge cases. You see working software at every stage — not slide decks. Each iteration is tested against real-world inputs before it ships.
We ship to production with scoped permissions, audit trails, and automated evaluation pipelines in place — not as afterthoughts. Escalation paths, rollback procedures, and human-in-the-loop checkpoints are designed before go-live. We review real conversation logs and escalation rates, and iterate. Most agents improve significantly in the first four weeks of live traffic.
A real project, real results. No client name — that's deliberate.
Client Type
Professional Services Firm
The Challenge
"Every inbound lead meant hours of back-and-forth before we even knew if they were a fit. By the time we sent a proposal, half had already gone with someone faster."
The Solution
An AI qualification agent was deployed to respond to every inbound enquiry within minutes — 24 hours a day — asking the right scoping questions, scoring each lead, and generating a first-draft proposal. The sales team now only touches pre-qualified prospects who are ready to close.
Key Results
We pick what's right for the problem — not the most impressive-sounding name.
An agent that passes every test case once still isn't proven — it needs to succeed on the same task run many times, on inputs that look like your real volume, not a curated demo. We call this the pass¹ vs passᵏ gap, and we test for it before anything goes live.
We pull a test set from your actual historical data — messy scans, ambiguous phrasing, edge cases included — not the clean handful of examples a demo is built around.
Every task in the sample runs several independent times. A single pass tells you the agent is capable; repeated passes tell you how often it actually succeeds at the volume you'll really see.
We don't average failures away into one accuracy score. We group them by input type, so a predictable weak spot becomes a specific, fixable gap instead of an invisible risk.
The agent surfaces its own uncertainty. Low-confidence cases route to a human in seconds; everything else ships automatically — full speed where it's earned, a checkpoint where it isn't.
Tell us about the problem. We'll come back with a realistic picture of what's possible, what it costs, and how fast it can be running.
