Wizeb — Custom AI Agents

AI agents that work inside your business — not beside it.

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.

What we build

Every project starts with your specific problem

These are the most common custom ai agents systems we build. Most projects combine elements from several areas.

Lead Qualification Agents

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.

Customer-Facing Support Agents

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.

Internal Process Agents

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.

Built for Usage-Based Efficiency

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 Optimization
How We Work

From problem to production

No slide decks, no vague roadmaps. Here's exactly how a project runs from first call to live deployment.

01

Map the problem

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.

02

Choose the right architecture

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.

03

Build and test against real data

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.

04

Deploy with governance, monitor, and improve

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.

Case Study

What this looks like in practice

A real project, real results. No client name — that's deliberate.

Client Type

Professional Services Firm

Shipped & live

41% higher close rate and £290K in new pipeline after AI took over lead qualification

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

  • 41%Improvement in proposal-to-client conversion
  • <30mLead response time (was 2 days)
  • £290KNew pipeline added in first quarter
Technologies We Use

Model-agnostic. Stack-agnostic.

We pick what's right for the problem — not the most impressive-sounding name.

Claude (Anthropic)Model
GPT-4oModel
Gemini FlashModel
MCPIntegration Standard
ElevenLabsVoice
LangChainFramework
n8nAutomation
Netlify FunctionsInfra
SupabaseDatabase
Common Questions

Things people ask before starting

Work with Wizeb

Ready to build something?

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.