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Platform
Connect systems and AI models — without new lock-in
The layer that lets assistants and agents work across ERP, CRM, documents and email, with the freedom to change models as the market moves.
Last reviewed: 29 September 2026
End-to-end
workflows across systems, not within one tool
Model choice
kept open, with cost and quality tracked per model
One place
for logging, credentials and evaluation
Every vendor’s AI works best inside its own walls
Valuable work rarely stays in one system. An order touches email, ERP and CRM; a service request touches knowledge bases, tickets and billing. Each platform’s AI helps within its own product, point-to-point integrations break when anything changes, and model decisions made today may look expensive or outdated in a year.
- AI features that stop at each product’s boundary
- Point integrations that break on change
- Model choices locked in too early
What the integration layer makes possible
| Use case | Business KPI | Maturity |
|---|---|---|
| Workflow orchestration across ERP, CRM and email | End-to-end cycle time | Proven |
| Document and email intake pipelines | Hours per item, error rate | Proven |
| Retrieval over enterprise knowledge with source citations | Answer accuracy, time to answer | Proven |
| Standard connector protocols that let agents use approved tools | Integration effort per use case | Emerging |
| Model routing and fallback | Cost per task, availability | Emerging |
| Cost and quality monitoring per model and use case | Cost per task, accuracy | Emerging |
How we build it
1
Map
The systems, data and handoffs in each priority workflow.
2
Build
Orchestrated workflows, connectors and retrieval, with human approval points.
3
Operate
Monitoring, evaluation on model changes and cost control.
Central control for distributed AI
- Secrets and credentials managed centrally, never embedded in workflows
- Separate credentials and least-privilege access for each agent
- Central logging of prompts, actions and outcomes, with retention rules
- Evaluation gates before any model or prompt change reaches production
Have a workflow that crosses three systems? That’s where we start.
See it in practice
Client Zero
Signal-based GTM automation
How we run multi-step AI workflows across data, enrichment, email and CRM tools, with human approval points and cost control.
Read the story
Also in your estate
We also integrate AI workflows with Oracle, Workday, Zoho and other business systems through their standard interfaces. Dedicated pages for these platforms will follow as our delivery experience grows.
Programs that start here
Enterprise AI Transformation
AI embedded across priority workflows.
AI-Ready Data & Knowledge
The data and knowledge the integration layer draws on.
AI Operations & Managed Services
Keep integrated AI reliable and cost-effective.
Questions about AI integration
Why not just use each platform’s built-in AI?
Use it wherever the work stays inside that platform — it is usually the simplest option. The integration layer is for workflows that cross systems, or where you need a model or capability the platform does not offer.
How do you avoid lock-in to one AI model?
We separate workflows from model choice, test alternative models against the same evaluation sets, and route tasks based on cost and quality. Switching models becomes a tested change, not a rebuild.
Is an orchestration layer another system to maintain?
Yes, which is why we keep it small, well documented and monitored, and why we recommend it only where cross-system value justifies it.
Connect the workflow that matters most
- 30 minutes with a senior lead
- No sales script — a working conversation
- A clear view of where AI can pay back first