Useful
Designed around a real user and business decision
AI APPLICATIONS AND INTEGRATION
Put AI inside the systems where work already happens.
We connect language models, agents and intelligent features to the applications, data and business tools required to produce a useful action.
Production-ready
Engineered beyond the prototype
Connected
Integrated with the tools you already use
Measurable
Tracked against adoption and business value
What this solves
What you get, in plain language.
Start with the business problem. We define and deliver the product, workflow or transformation needed to solve it.
At a glance
An isolated AI demo creates interest. An integrated application creates value.
Tangible designs the application layer, APIs, data flow, permissions and human controls around the AI capability. The result can be a new interface, an intelligent feature inside an existing product or an agent working across approved systems.
Integration patterns
- Add an LLM feature to an existing web or mobile product
- Connect agents to CRM, ERP, helpdesk or collaboration tools
- Link AI workflows to databases, document stores and internal APIs
- Create a secure orchestration layer across multiple services
- Stream real-time events into classification, recommendation or action flows
- Expose an existing AI capability through a usable business application
What this changes for you
- Avoid another standalone tool that teams must manually update.
- Give AI access to current, relevant business context.
- Turn model outputs into controlled actions inside existing workflows.
- Preserve system ownership, permissions and auditability.
- Modernise specific experiences without replacing the complete stack.
- Create reusable APIs and integration patterns for future AI use cases.
What we deliver
- Integration and application architecture
- API design, middleware and orchestration services
- LLM and model-provider integration
- CRM, ERP, helpdesk and business-tool connections
- Database, document-store and real-time data integration
- Model Context Protocol integration where valid and useful
- Authentication, role and permission design
- Structured outputs, validation and business-rule layers
- User interfaces and embedded AI experiences
- Monitoring, failure handling and operational documentation
Integration process
1. Map the systems and action
Define where the user works, what information AI needs, which action should follow and which system remains the source of truth.
2. Assess interfaces and constraints
Review APIs, data quality, identity, permissions, rate limits, latency and ownership. Confirm what can be read, written or triggered.
3. Design the control layer
Separate model reasoning from validation and transaction logic. Use structured outputs, business rules and human approval where actions have consequences.
4. Build and test the complete path
Test authentication, stale data, unavailable services, duplicate actions, conflicting records and rollback behaviour, not only successful API calls.
5. Release and observe
Monitor integration failures, AI quality, latency, cost and downstream outcome. Document ownership and recovery procedures.
How it connects
How we work
Small-team speed. Enterprise-level discipline.
You always know what is being decided, built and measured.
Frame
Define the outcome, user and riskiest assumption.
Prototype
Make the value tangible before committing to the full build.
Build
Design, develop and integrate the production system.
Improve
Measure adoption and strengthen what performs.
Built for reality
Connected. Governed. Ready to operate.
The new system should fit the business you already run, not create another isolated tool.
SystemsCRM · ERP · data · APIs
ControlsAccess · review · audit
PeopleRoles · handoffs · adoption
Relevant exampleClient engagementRead the case study ↗
Make every workout plan more personal.
Conversational onboarding, adaptive routines and connected product analytics helped CloudFit turn a generic fitness journey into a more useful, measurable experience.
+45%sign-ups
+34%subscriptions
FAQ
Questions about AI Applications and Platform Integration
Straight answers before you decide what to do next.
Can you add an LLM to an existing application without rebuilding it?
Often, yes. We first assess the application's interfaces, data, identity and user experience. The AI feature can then be introduced through APIs, backend services or a focused interface change.
Can AI agents update CRM or ERP records?
Yes, where suitable APIs and permissions exist. Consequential writes should include structured validation, explicit rules, idempotency and human approval where appropriate.
What happens if a connected system is unavailable?
The workflow should detect the failure, prevent duplicate or incorrect actions, inform the user appropriately and queue, retry or escalate according to the business requirement.
Do you work with OpenAI, Claude and Gemini?
We can design model-flexible architectures and integrate relevant providers where capability and project conditions fit. Specific technology support is confirmed during discovery rather than treated as a blanket claim.
Is integration different from workflow automation?
AI integration connects models or agents to existing applications and data. RAG grounds answers in an approved knowledge base. A solution may use both.
How long does a typical engagement take?
A focused diagnostic or prototype can take a few weeks. A production build or wider transformation is phased around complexity, integrations, risk and the evidence required at each gate.
What determines the cost?
Scope, workflow complexity, data readiness, integrations, security requirements and the level of production support determine investment. We define the smallest credible first phase before proposing a wider programme.
Can this work with our existing systems?
Usually, yes. We design around your current CRM, ERP, data, communication and operational tools, then recommend replacement only when an existing constraint genuinely blocks the outcome.
How do you handle security and human oversight?
We define access, approval, escalation, audit and monitoring requirements with the workflow. High-impact decisions retain the right human checkpoints and visible accountability.
Have a problem worth fixing?
Tell us what needs to change.
We will help you find the clearest route from business need to working system.
Talk to our team