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Software outcomes without scaling your engineering team
AI Delivery Agents take scoped software work, features, fixes, automation, testing and documentation, and ship it back to your team ready to review. More output. Same workflow. No new headcount.
The software industry has already moved
of Microsoft's code is now AI-generated on some projects.
Satya Nadella, April 2025.
of all new Google code is AI-generated and approved by engineers.
Sundar Pichai, April 2026.
faster task completion in GitHub-controlled developer studies.
Peng et al., GitHub research.
projected enterprise AI agent adoption by 2027, from 25% to 50%.
Deloitte Global TMT 2025 Predictions.
The model is no longer "more engineers means more output". It is engineers overseeing, agents delivering. Businesses that act early in this cycle protect their margins. Those that wait absorb the cost.
A working AI teammate, inside your delivery process
Most AI coding tools speed up one developer inside their own editor. An AI Delivery Agent is different. It sits inside your managed delivery process, so work is allocated, tracked, discussed and reviewed through the tools your team already uses, against the codebase you already have, to the standards you already work to.
Your engineers stay in control. The agent absorbs the repeatable, time-consuming work that slows them down.
Defined work, delivered
Features, fixes, code generation, workflow automation, QA, testing, documentation, reporting. If it can be scoped, it can be delivered.
Inside your tools
Project management, source control, repositories, test environments. The agent works where your team works. Ready integrations: GitHub, Azure DevOps, CI/CD workflows and Linear. Jira, Bitbucket and others are scoped during onboarding.
Reviewed and reported back
Every change is reviewable. Every test is logged. Nothing ships without your team's approval.
More of your team can move software forward
By turning requirements, tickets and product intent into structured, agent assisted work, commercially capable people who are not deep engineers can take part in delivery. You depend less on finding a highly specific technical profile for every need, and can build a delivery team around product thinking, operations, QA and customer insight.
Engineering skill is not removed, it is refocused. Senior people spend more time on architecture, review, standards, security and release quality, while the agent moves well defined tasks through the system, with technical governance kept firmly in place.
Cyber rarely lives on its own
Cyber Security work runs alongside cloud migrations, application development, web development, integration, automation and modernisation, the wider digital transformation programmes most large organisations are already inside.
Those programmes carry the same delivery pressure your security work does. Tight timelines. Stretched engineering teams. A backlog that keeps growing.
An AI Delivery Agent gives those programmes capacity without scaling headcount, working alongside your engineers, inside your existing workflow. If we are already embedded with you on the cyber side, we can extend that into delivery capacity for the wider programme.
Why this is a no-brainer
A team of ten experienced software engineers in the UK is a seven-figure annual commitment once salary, employer costs, recruitment, equipment and management are loaded in.
An AI Delivery Agent, scoped, built and embedded in your delivery workflow, comes in at a fraction of that against the same volume of scoped delivery work.
You keep your engineers focused on the work only engineers can do. The agent absorbs the rest.
Not doing this is the more expensive option.
| Ten software engineers | One AI Delivery Agent |
|---|---|
| Seven-figure annual cost once salary, employer costs, recruitment, equipment and management are loaded in. | A fraction of the cost against the same volume of scoped delivery work. |
| Months to recruit, onboard and ramp. | Scoped, built and live in weeks. |
| Capacity limited to working hours. | Runs against your backlog when you need it. |
| Payroll, retention, replacement risk on your books. | None of it. |
A controlled Delivery Actor, not an unsupervised Developer
The agent is deployed inside your existing access control model. Repository, API and environment access run through scoped service accounts, role based permissions and least privilege, limited by repository, branch, environment, workflow or task type. It only gets the access the agreed work needs.
Full audit trail.
Pull requests, commits, comments, task updates and workflow actions stay visible and attributable, so you can see what was accessed, what changed and what needs human review before release. Audit depth is agreed with you.
Secrets stay out.
API keys and production credentials are never placed in prompts or task instructions. Access to protected systems runs through approved secret managers and customer controlled integrations.
Your code, your release.
You own all code and outputs. Nothing reaches production without your normal review, testing and approval. The agent is never an autonomous release authority.
No training on your code.
Where paid API based models are used in line with provider terms, your code and data are not used to train models. Task context is sent to the model for inference only, which is different from training.
Where your code lives.
By default code stays in your own source control, such as GitHub or Azure DevOps. For sensitive deployments the orchestration layer can run inside your infrastructure, and for the strictest needs, local inference can be used so the bulk of model activity stays in your environment.
Data residency.
Default infrastructure is UK or European Azure based. Strict UK or EU only residency is designed and contractually controlled as part of the deployment, not assumed.
Our delivery partner's certifications: Cyber Essentials in place, ISO 27001 in progress, with the deployment model adaptable for stronger governance, security and audit requirements.
Built with our specialist software delivery partner
invitise partners with a specialist software delivery team to bring AI Delivery Agents to our clients. They scope, build, test and support your agent. We make the connection and stay alongside you throughout.
Our partner has been building production AI agents since long before the current AI cycle began. The model is not fixed to one provider, it is selected for the complexity of the work and the balance of quality and cost, and reviewed regularly so the agent uses the most appropriate model available. You do not manage any of that.
Transparent on cost from the start
The base deployment includes the agent capability, the delivery workflow, the orchestration layer and the integration to connect into your process. Model usage is configured through your own API key, or as a transparent pass through cost in rare cases. It is not hidden inside the base price.
There is no pretend ticket number. Throughput depends on your codebase, ticket quality and complexity, so we benchmark the agent against a representative set of your real tickets during onboarding and agree realistic expectations from there. The agent is designed to get more efficient over time as it builds working knowledge of your stack.
Tell us what is sitting in your backlog
Send us the shape of the work. invitise will connect you with our delivery partner, and a short technical call is usually enough to know if an AI Delivery Agent is the right fit. If it is not, they will tell you.
What happens next
- 1.invitise acknowledges your enquiry within one working day.
- 2.We connect you with our specialist software delivery partner.
- 3.A short technical call together with our partner. If it fits, you get a scoped proposal. If not, we say so.
