Data & Reporting Pipelines
Connect your data sources, normalise the outputs, and deliver live dashboards and exception reports — replacing manual consolidation entirely. No more Friday afternoon report building; no more spreadsheet errors.
Wizeb — Workflow Automation
Eliminate the repetitive work. Keep the people for the decisions.
Most businesses are running expensive, error-prone manual processes that software could handle reliably and cheaply. Wizeb builds automation pipelines that identify exactly where human time is being wasted on predictable work, and replace that work with reliable, auditable AI systems — without disrupting the workflows your team already knows.
What we automate
Every project starts with your specific problem
These are the most common workflow automation systems we build. Most projects combine elements from several areas.
Document Processing & Extraction
AI that reads contracts, invoices, applications, and forms — extracting structured data, flagging anomalies, and populating downstream systems automatically. Handles the documents your team currently processes by hand.
Communication & Follow-Up Automation
Automated workflows that send confirmations, chase approvals, notify stakeholders, and escalate exceptions — triggered by events in your systems. No more manual follow-up chains or things falling through the cracks.
Built for Usage-Based Efficiency
All our workflow automation 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 OptimizationHow 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
Process audit
We map every manual step in the target workflow — who does it, how long it takes, where errors happen, and what the inputs and outputs are. This is where most of the value is found.
02
Automation design
We design the automated version: which steps get replaced entirely, which get human-in-the-loop checkpoints, and where AI judgement is needed versus rule-based logic.
03
Build with your real data
We build against your actual systems and data — not synthetic test cases. Edge cases are handled before they hit production. You review each stage before it goes live.
04
Handover and ongoing support
We document everything, train your team, and provide ongoing support. Most clients find their automated workflows require less maintenance than the manual processes they replaced.
Case Study
What this looks like in practice
A real project, real results. No client name — that's deliberate.
Client Type
Logistics & Distribution Company
Shipped & live
“3,200 hours of manual work eliminated — and a 28% cut in operational costs”
The Challenge
"Our ops team spent most of the week pulling data from different systems, building reports, and chasing status updates. It was expensive, error-prone, and completely unsustainable at our growth rate."
The Solution
A multi-step AI automation pipeline was built to connect all operational data sources, extract and normalise information automatically, generate live dashboards and exception reports, and flag problems before they escalated — replacing a process that previously required a dedicated team of three people working full time.
Key Results
- 3,200hManual hours eliminated per year
- 28%Reduction in operational overhead costs
- 99.2%Reporting accuracy (previously 71%)
Technologies We Use
Model-agnostic. Stack-agnostic.
We pick what's right for the problem — not the most impressive-sounding name.
n8nAutomation
Make (Integromat)Automation
ZapierAutomation
PythonCustom code
OpenAIAI extraction
ClaudeAI extraction
PostgreSQLDatabase
Webhooks / APIsIntegration
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.