AI · Automation · Integration
Same team.
Less friction.
More output.
Real software, real operational understanding, real years building businesses from the inside. LLM-agnostic by design. Supported for the long term.
What we bring
In this order.
Business understanding first.
Engineering discipline second.
AI where it earns its place.
Twenty-seven years building and running businesses. Python since 2020. Shipping AI tools since 2025. Systems designed to survive the change in models, vendors, and business needs — and supported for the long term.
How we differ
Four ways to buy AI. We are the fourth.
Big consultancies
Templated playbooks. Partners sell, junior staff deliver. Premium rates for graduates learning on your time. Slow.
Product vendors
One tool, one capability. They sell features; you absorb the integration cost and own the gaps between systems.
Prompt shops
Confident with a prompt, less confident when the system has to run unattended. Demos that look good in the meeting, edge cases that bite in production.
WildSun
An operator who has built businesses since 1999. Real software, not prompts. LLM-agnostic by design. Supported for the long term.
How we work
Six phases. Real software. Modular by design.
A repeatable framework that takes businesses from first conversation to working AI without the stalled-pilot trap.
Discovery
We listen, and we ask a lot of questions. We identify the first bottleneck whose removal will deliver visible benefit fastest, and build a plan to solve it. Step by step, anchored to your overall objective.
Specification
Before we build, we write down exactly what the system will do. You review it, we discuss it, we agree it. No invoice for the build until the finished system does what the spec says.
Data Foundation
Convert what you have today — spreadsheets, legacy databases, mixed-format documents — into a foundation an integrated system can run on. The unsexy step that makes everything else work.
Working Prototype
The smallest working version that proves the spec. Rough around the edges, running on real data in your environment, so you can see it work before we invest in the full build.
Full Implementation
Production-grade build of the signed-off prototype. Properly engineered and integrated with the rest of your operation.
Long-term Solutions
Ongoing evaluation through model changes, supplier changes, and business changes. We stay engaged on terms that match how you are using the system.
Understanding the difference
Automation vs. AI — when to use which.
Not everything needs AI. The systems we build use deterministic automation where rules are stable and AI only where judgment is required.
| Traditional Automation | AI & Agentic Systems | |
|---|---|---|
| Logic | If this, then that. Deterministic. | Probabilistic. Context-aware. |
| Best for | Repetitive, rule-based, high-volume tasks | Judgment, exceptions, dynamic environments |
| Consistency | Run it 1,000 times, get 1,000 identical results | Results vary — by design. Requires evaluation. |
| Evolution | Requires manual updates | Learns and improves from data |
| Our default | Use first. Automate everything that can be automated. | Use sparingly. Only where automation cannot do the job. |
Read the full piece: Most AI projects should have been automation projects
Why us
We have built business systems since 1999.
We did not arrive at AI from a slide deck. We have spent 27 years building the kind of operational systems we now build for clients — stock control, automated estimating, accounting integrations, advertising tools, customer workflows — in our own businesses, under our own pressure. We understand business needs first, then translate them into systems that work. That operator's discipline shapes everything we deliver.
Book a discovery call.
An online conversation about your business. No fixed agenda, no cookie-cutter discovery. We focus on your ultimate objective and the most pressing thing in front of you right now — then look at the route between them.