"KIJO didn't just build what we asked for, they challenged our assumptions, simplified the scope, and shipped something better than we imagined. The AI automation has saved us 10 hours a week."
AI that works in production.
Getting an LLM to produce impressive outputs in a demo is easy. Making it reliable, fast, and safe in production at scale — that's where the real work happens.
4wks
To first
working demo
Day1
CI/CD & observability
configured
30d
Post-launch
support included
£25k
Projects
from
What you get
Production code.
Not just a prototype.
Working Software
Production-ready code with full test coverage, typed interfaces, and documentation — built to last beyond the engagement. Not a prototype, not a proof of concept.
01
Impressive in a demo.
Impressive in a demo.
Gone in six months.
Most AI builds fall apart in production. The demo was clean, the outputs looked great — then reality hit. Slow responses, hallucinations at scale, no monitoring, and a codebase nobody wants to touch.
02
Engineering discipline
Engineering discipline
applied to AI.
We treat AI development the way it deserves — typed interfaces, comprehensive test coverage, cost modelling, and observability from day one. No magic, no black boxes.
03
Built for your team
Built for your team
to own.
Every line of code is written to be maintained, extended, and understood by your engineers — not just ours. Clean architecture, full documentation, and a proper handover. Not a dependency, a foundation.
04
From brief
From brief
to production.
Iterative sprints with weekly demos and real feedback loops. You always know where things stand, every sprint ships something real, and nothing lands in production without you seeing it first.
How it works
From brief to
production.
Phase one
Scope
We define exactly what gets built, why, and what success looks like — before writing a line of code. No ambiguity, no scope creep, no nasty surprises.
- Requirements & acceptance criteria
- Technical feasibility review
- Stack & architecture decisions
- Fixed-price or sprint scoping
Phase two
Architect
System design documented end-to-end — data flows, model choices, integration patterns, fallback strategies, and cost modelling. Built to survive contact with reality.
- System architecture diagram
- Model selection & evaluation
- Data pipeline design
- Cost & latency modelling
Phase three
Build
Iterative sprints with weekly demos. Real code, real tests, real feedback loops — shipped incrementally so you always know exactly where things stand.
- Weekly sprint demos
- CI/CD from day one
- Evaluation & regression tests
- Async standup access
Phase four
Launch
Production deployment, observability setup, and a handover that actually sticks. Your team leaves knowing how to own, maintain, and extend what we built.
- Production deployment
- Monitoring & alerting setup
- Code walkthrough sessions
- 30-day post-launch support
What clients say
Sarah Chen
CPO, Atlas Intelligence
Questions
Common questions.
Answer
We start with a short quiz that maps your business processes and goals. From there, we run a structured assessment session — usually 45–60 minutes — to dig into the detail. You receive a written report with prioritised AI opportunities, quick wins you can act on immediately, and a recommended roadmap built around your situation.
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