For teams commissioning AI development
AI development you can put into production
A convincing demo is a useful start. A system your team can own needs a defined problem, engineering decisions, tests, security checks, and a release plan. SUIKI uses AI to accelerate the work while a human engineer remains accountable for what ships.
Discuss your project →What if we need more than a prototype?
AI-generated code can make a first version fast. The questions for a buyer are whether it solves the right workflow, fits an existing system, handles data and permissions safely, and can be maintained after the first release. We answer those questions with agreed acceptance criteria and reviewable delivery evidence, rather than asking you to trust a prompt or a demo.
Can you review a "vibe-coded" prototype we already have?
Yes, as a scoped first step. We would establish what it is meant to do, review its code and dependencies, identify data and permission risks, and test the behaviour that matters to users. The outcome may be a plan to harden it, replace a bounded part, or stop before further spend. We would agree the review depth and deliverable before starting.
What does forward-deployed engineering add?
A forward-deployed engineer (FDE) works close to the people and systems that will use the software. The work joins discovery to delivery: understand the real workflow, turn it into a scoped technical plan, build with the client team, observe what works, and feed that evidence into the next decision. For a SUIKI engagement, we agree the lead, working rhythm, decision rights, and handover in the proposal.
- See the real workflow. Talk to users and owners, inspect the current product and constraints, and find the smallest useful outcome.
- Make the work testable. Define user stories, acceptance criteria, data boundaries, risks, and what must stay under human control.
- Build in context. Integrate with your code, people, and release process; review AI-assisted changes as engineering work.
- Prove and transfer. Check behaviour against the agreed criteria, release through the agreed controls, and hand over code and operating knowledge.
This approach can cover a bounded AI feature alongside your developers as well as a larger application build. The scope and on-site or remote involvement are agreed for each project.
Can you take on only part of an AI project?
Decide where AI helps
Review a workflow, compare a small AI-assisted change with non-AI alternatives, and define a first experiment.
AI adoption and scoping →Add a feature or integration
Build a contained service, interface, or agent workflow within an existing product, with clear data and permission boundaries.
Full-stack development →Modernise what is already live
Map usage and dependencies before changing an inherited system; keep current users and operations in view.
Legacy modernisation →How do we make the result reviewable?
Correct problem
We write down the users, current workflow, success criteria, and exclusions before committing to a build. This limits the risk of producing polished software for the wrong task.
Accountable implementation
A human owns architecture and code review. Requirements become acceptance criteria and tests; AI output is checked against both. Our delivery process explains the sequence.
Security and model risk
We agree the data, permission, and release checks the project needs. For systems that use an LLM in production, prompt injection, unsafe outputs, and unintended external actions are explicit design questions. NIST's AI Risk Management Framework and the OWASP LLM Top 10 are useful references; neither is a certification of SUIKI or a substitute for project-specific review.
Release and ownership
We agree how changes reach staging and production, what evidence is needed to release, and who can approve external effects. Deliverables and handover are written into the proposal.
What work can you inspect?
CommentNow
A news-publishing application taken from initial build to production. The public case study records 30+ delivered features and nine security hardening measures. It shows the application, infrastructure, and testing work behind an AI-assisted delivery.
Profile renewal
For a long-running subscription platform, more than 200 existing features were checked against production usage logs before the rebuild scope was set. This is evidence-led discovery for a live system, where building the wrong replacement would be costly.
Suiki Agent
Our own operations agent investigates production signals and prepares proposals. External writes remain subject to human review. It demonstrates a concrete boundary for agent autonomy, not a claim that an AI agent should run without oversight.
What would you receive?
The deliverables depend on the engagement. A scoping phase may produce a workflow map, options, risks, and a recommended first increment. A build may also include acceptance criteria, architecture decisions, reviewed code, tests, deployment checks, and operating notes. We specify these in the proposal so that completion can be checked against agreed evidence.
Before a release, ask us to show the agreed acceptance checks, test results, security decisions, and the release and rollback plan. After handover, your team should know where the code runs, who owns access, and how faults are investigated. The exact evidence set follows the risk and scope of the work.
We do not promise defect-free software or a fixed result from an unspecified AI brief. We do promise to make responsibility, scope, checks, and handover explicit before work begins.
How long will it take, and what will it cost?
The first 20-minute conversation is free. An introductory AI advisory session is listed at £100 per hour for the first session. A code review, integration, or build needs a scoped proposal: we agree the first useful increment, milestones, deliverables, timeline, and price after seeing the system and constraints. We will not attach a fixed build quote to an unexamined brief.