use cases
From goal to proof.
Real Reply engagements run on Symbiosis. For each: the business goal, what stood in the way and what we did, the measures that moved, and delivery far faster and cheaper than a traditional build.
01
Offshore energy · Legacy replacement
A legacy estate the business can change again.
The business goal
Run and change their own applications again, without the legacy platform or scarce specialists.
1 of 8
legacy applications meeting the maintainability bar. Target: all 8 by 2027.
What stood in the way, and what we did
"We cannot maintain it ourselves anymore."
AI agents rebuilt every business rule in plain Python, with automated tests and written documentation.
Maintainability checks passed
0 → 6 of 6
✓ Solved
No rebuilt application had ever reached production.
Building the path to production with the client's IT team, step by step.
Maintainability checks passed
3 of 5 gates
On track
Cleared: testing, logging, monitoring. Next: sign-off, release.
8x
Faster delivery
3 months vs a typical 2 years
12x
Lower cost
versus a traditional rebuild
Every week
Measured, then decided
Continue, change or stop, on evidence
02
Healthcare · Public health provider
More patients stay in care. Clinicians get their time back.
The business goal
Equitable, verified access to community prehabilitation services across the catchment.
25 → 10
patients in 100 who drop out of the prehab pathway. Target reached.
What stood in the way, and what we did
Manual admin work took clinician time.
Referrals read automatically, support matched by AI, check-ins sent in one click.
Clinician time spent on admin
50% → 20%
✓ Solved
Built on synthetic data, outside the provider's live systems.
Built to national health data and identity standards from day one, then connected.
Live systems connected
0 → 3 of 3
✓ Solved
Patient records (FHIR), verified patient identity, secure clinical messaging.
6x
Faster into production
8 weeks vs a typical 1 year
10x
Lower build cost
versus a traditional build
Every week
Measured, then decided
Continue, change or stop, on evidence
03
Industrial machinery · Agentic bug-fixing
Bugs read, fixed and validated by an AI agent.
The business goal
Ship reliable PLC software to customer projects faster, without a developer hand-fixing every bug ticket.
6 → 19
bugs resolved per week, 3× faster. Measured in a one-week trial, one engineer with the AI agent.
What stood in the way, and what we did
Bugs were fixed by hand, one ticket at a time.
An AI agent reads the bug ticket from Azure DevOps, proposes a fix to the team's coding standards, and posts it back for a developer to review and integrate.
Bugs auto-triaged per project
400 → 100-150
✓ Solvev
The same known bug had to be re-checked across every customer project.
A validation agent flags which known bugs still apply to each project, as a ready-to-work checklist.
Fixes validated in parallel
1 → 10-15
On track
Cleared: testing, logging, monitoring. Next: sign-off, release.
3x
Faster bug resolution
19 vs 6 bugs per week
One-week trial
One engineer + AI agent
not a large team
Every week
Measured, then decided
Continue, change or stop, on evidence
Figures from delivered Reply engagements. Where a measure is still moving, its current status is shown. Sectors named; individual client names added on sign-off.