01
Start with the responsibility gap
Before comparing model features, ask what your team is missing. Is it help writing and understanding code, a way to coordinate AI-assisted work across tickets and repositories, or additional engineering delivery capacity? These are related needs, but they lead to different buying decisions.
A coding assistant can be useful inside a capable engineering team. A platform can organise more of the workflow. A managed service adds people and operating responsibilities defined by an engagement. Buying one does not automatically supply the responsibilities of the others.
02
Compare the three operating models
| Decision | Coding tools | Fusion Standard | Fusion Autonomous |
|---|---|---|---|
| Who operates it? | Your developers, according to the tool’s capabilities | Your engineering team | JadeX manages the specialist AI squad |
| Who reviews delivery? | Your organisation must assign responsibility | Your team; JadeX engineering services are separate | JadeX review, oversight and escalation within agreed scope |
| AI provider setup | Depends on the product and plan | Your own AI provider accounts | JadeX-managed, self-hosted AI infrastructure |
| Release authority | Set through your engineering controls | Your team retains release control | Your team retains release control |
This describes Fusion’s service boundaries. Individual coding products differ; check their current capabilities and terms rather than assuming all assistants work alike.
03
Choose a platform when your team will run delivery
If your engineers can define work, assess changes and manage approvals, an AI-assisted software development platform may fit. Fusion Standard connects repository analysis, ticket assignment, implementation, review and validation while your team runs the platform.
Include the operating effort in the decision. Someone still needs to resolve ambiguous tickets, maintain useful repository context, manage access and decide when a proposed change is ready. Standard uses your own AI provider accounts; engineering services from JadeX are arranged separately.
04
Choose managed capacity when you need an operating partner
If the gap includes managing the AI squad and supervising delivery, assess a managed AI software development service. Fusion Autonomous includes JadeX review, oversight and escalation within the agreed service scope.
The illustrative squad covers technical leadership, front end, back end, integration, QA and code review. Agree the role mix, workloads, concurrency and review capacity for your engagement. “Managed” should be explained through concrete responsibilities, not treated as a guarantee that every engineering task is included.
05
Evaluate a scoped piece of work
Compare approaches using a representative ticket with agreed acceptance criteria. Ask what context was needed, what changed, which tests actually ran, how much review was required and what rework remained. Measure accepted delivery and reviewer effort rather than counting generated lines of code.
Discuss ownership, processing locations, provider terms, access revocation and release permissions before connecting repositories. For a practical example, explore the illustrative category-filter walkthrough. It explains the evidence to assess without claiming measured customer results.
06
Questions to take into a buying conversation
- Who turns an unclear requirement into an agreed task?
- Who investigates failures and handles rework?
- What evidence accompanies a proposed change?
- Which reviews and integrations are included in scope?
- Who can approve, merge and release code?
- What can we retain and export when the engagement ends?
Use these answers to choose an operating model before comparing headline capacity or subscription prices.