Build · Custom AI Applications
Build the AI capability the workflow actually needs.
Purpose-built internal or customer-facing applications for workflows where no off-the-shelf tool fits without damaging workarounds.
Systems of record
- CRM
- ERP / finance
- Service desk
- Document store
- HR / ATS
Integration layer
- Authentication
- Permissions
- Data contracts
- Retries and errors
- Audit log
Workflow and AI-assisted steps
- Intake
- Retrieval
- Drafting
- Routing
- Human decision gates
Measurement layer
- Cycle time
- Quality and rework
- Exception rate
- Adoption
- Cost per run
Implementation blueprint
How this is actually built and run
01 · Trigger
A user action inside the application, or an integration event from a connected system.
02 · Context and data
Application data and connected systems, governed by the application's own role and permission model.
03 · AI task
The specific bounded capability the application is built around, with defined inputs and outputs.
04 · Human control
Roles, approval steps, and audit trails designed into the application rather than added afterwards.
05 · System action
Persists results, triggers downstream integrations, and records who approved what and when.
06 · Evaluation
Functional and scenario testing before release, plus quality testing of the AI-assisted steps against agreed criteria.
07 · Monitoring
Application logging, error rates, usage by role, and AI step quality reviewed on a schedule.
08 · Ownership
A product owner on your side, with documented handover so your team can operate and extend it.
The problem
What this solves
Sometimes the workflow is the differentiator, and forcing it into a generic product costs more than building the right thing. A custom application is justified when the process is specific, durable, and central to how the business competes.
When this solution fits
- The process is a genuine differentiator, not a commodity
- Existing tools force workarounds that create risk or rework
- Several point tools need consolidating into one workflow
- Access rules or data models cannot be expressed in an off-the-shelf product
- The workflow is stable enough to justify a build
Example workflow
A review-and-approve application for a specialised assessment process
01
Submission
Cases enter through a structured form or an integration from an upstream system.
02
Assisted analysis
AI drafts a structured summary and highlights the evidence behind each point.
03
Reviewer workspace
A specialist reviews, edits, and records their reasoning against each item.
04
Approval states
Cases move through defined states with role-based permissions at each transition.
05
Output and audit
The approved output is issued and the full decision trail is retained.
What is implemented
- Requirements and workflow design with the people who will use it
- Application build with roles, review states, and logging
- AI capability embedded where it helps, not everywhere
- Integration with core systems and your identity provider
- Testing, release plan, and post-launch support model
- Technical documentation and handover
Systems and data inputs
- Workflow definition and business rules
- Data model and existing systems of record
- Identity provider for authentication and roles
- Hosting, environment, and deployment constraints
- Representative case data for testing
Human control
How oversight is designed in
- AI output is presented as a draft attached to evidence, never as a final decision
- Role-based permissions on every state transition
- Complete audit trail of who changed what and when
- Manual override available at every assisted step
- Feature flags so AI assistance can be disabled without breaking the workflow
Tangible outputs
- A deployed application in your environment
- Role and permission model documentation
- Audit and reporting views
- Test results and release notes
- Support and maintenance plan
Measurement model
How we know it is working
| Measure | How it is tracked |
|---|---|
| Cycle time per case | End-to-end duration compared with the baseline process |
| Reviewer effort | Time per case spent on assisted versus manual steps |
| Quality | Sampled review of decisions and correction rate after issue |
| Adoption | Share of eligible cases processed in the application |
| Defect and incident rate | Issues raised per release, tracked over time |
Boundaries
What this solution does not promise
- We do not recommend a custom build where a configured product would do the job
- Delivery timelines depend on scope, integration complexity, and your review cycles
- A custom application requires ongoing ownership and maintenance
- We do not promise capability beyond what your data and systems can support
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Find the AI opportunities worth implementing in your business.
Start with a structured assessment of your workflows, systems, and data, and leave with a prioritized view of where AI can create real value.