Application integration
AI features inside the screens your users already work in, under existing permissions.
Into the systems you run
AI/ML Integration
AI capability added to the applications, databases and APIs you already depend on.
What this is
Most AI value is unlocked inside existing software, not beside it. We add AI capability to the systems your teams already use, reusing their authentication, permissions and workflows rather than creating another place to log in.
The problem
What this service addresses
If more than one of these is familiar, there is usually a case worth scoping.
- AI tools sit outside the workflow, so staff have to leave their system to use them.
- A legacy platform has no API, and vendors say integration is impossible.
- AI features were added as a separate app and adoption never happened.
- Each AI pilot re-implements authentication, permissions and logging from scratch.
Scope
What we deliver
Every item below is a deliverable you receive, not an activity we perform.
- AI capability embedded in your existing application interfaces
- Integration layer over your APIs, databases, webhooks or event streams
- Bridges for systems without APIs, using supported extension points
- Reuse of your existing authentication and permission model
- Asynchronous processing with queues so AI latency never blocks the interface
- Monitoring, cost attribution and rollback paths per integrated feature
Capabilities
What the system does
API and webhook integration
Event-driven processing triggered by changes in your systems, with idempotent handling.
Legacy system bridges
Database-level, file-based or scripted integration where no API is available.
Data access layer
Safe, read-scoped access to the records a feature needs, without broad exposure.
Asynchronous processing
Queued jobs, retries and status updates so slow model calls never block a request.
Feature flagging and rollback
AI features deployable to a subset of users and removable without a release.
Applied
Where it is used
Each of these is a bounded job with a measurable result, which is how we prefer to scope a first build.
01
CRM enrichment
Summarises account history and drafts next steps directly on the record the salesperson is viewing.
02
Helpdesk assist
Suggests the approved answer and source inside the ticket the agent already has open.
03
ERP document handling
Extracts and validates invoice data, then posts it through your existing finance workflow.
04
In-app search and summarisation
Natural-language search and summaries added to your own product.
05
Legacy modernisation step
Adds AI capability to an old platform without a rewrite, buying time for a proper migration.
Architecture
How it is put together
An indicative architecture. The final design follows your systems, data boundary and constraints.
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01
Host systems
Your application, CRM, ERP, helpdesk or internal tools as the surface users touch.
-
02
Integration layer
APIs, webhooks, queues, database access or file bridges depending on what the system supports.
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03
AI service layer
A separate, independently deployable service holding model calls, retrieval and prompt logic.
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04
Identity and permissions
Your existing authentication and authorisation reused, not duplicated.
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05
Async processing
Queues, workers, retries and status callbacks for anything slower than a page load.
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06
Operations
Per-feature monitoring, cost attribution, feature flags and rollback.
Security & governance
Controls we build in
Access, privacy and oversight are part of the build rather than a later hardening phase.
- Least-privilege data access: a feature reads only the fields it needs.
- Existing permission model enforced; AI features cannot widen a user’s effective access.
- Sensitive fields masked or excluded before reaching a model provider where required.
- No changes to your database schema or business logic without explicit agreement.
- Feature flags so an AI feature can be withdrawn immediately if it misbehaves.
Delivery
Our approach
How we run the engagement
Discovery
Process walkthroughs, data review, constraints and a written scope you sign off before build.
Architecture
Data model, integration boundaries, hosting and the security model, agreed up front.
Proof of concept
A narrow build on your real data against an agreed quality bar, so the go-ahead is evidence-based.
Development
Two-week increments, each ending in a working deployment and a review.
Testing
Automated coverage on critical paths, evaluation sets for AI behaviour, acceptance testing with your users.
Deployment
Staged rollout into your infrastructure with monitoring and a tested rollback path.
Monitoring & support
Quality, cost and latency tracking after launch, with handover documentation or an ongoing retainer.
Proven work
Products we have built and sold
We are product builders as well as a services team. These are our own commercial platforms, not client references.
Digital sales & support agent
SIZH Virtual Agent
A virtual digital agent for sales, support and reception — trained on your business, live on WhatsApp, web, voice and email.
View product Mobility platformRideOn
A ride-hailing and fleet management platform, now with agentic dispatch, driver support and demand intelligence.
View product Multivendor marketplaceUniBooker
A configurable multivendor service-booking marketplace with agentic onboarding, matching and support.
View product
Questions
AI/ML Integration — FAQs
The questions we are asked most often about this service.
Our main system has no API. Is integration possible?
Usually, through a supported extension point: scheduled database reads, file exchange, scripting hooks or screen-level automation as a last resort. We assess what is available and tell you honestly if the only safe path is a vendor change.
Will this slow our application down?
No, because model calls run asynchronously through queues with status updates. The user interface never waits on an AI response.
Do you need write access to our database?
Only where the feature requires it, and scoped to specific tables with an audit trail. Many integrations are read-only with writes going through your existing APIs.
Can we remove it later?
Yes. The AI service is independently deployable and features sit behind flags, so a feature can be switched off without touching your core system.
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