AI that works inside your systems, through the doors they already have.
A model on its own produces text. Value appears when that output reaches the CRM record, the ERP order, the ticket or the database row where work actually happens — and when information from those systems reaches the model.
Integration is ordinary engineering with a few AI-specific risks. We connect through approved interfaces with scoped credentials, and treat every write-back as something to validate, log and, where it matters, approve.
01Verified or custom?
A connection counts as verified only once it has been built and tested against your system.
Ramerion does not publish a catalogue of ready-made connectors, and no product is assumed to be compatible until discovery confirms it. What varies is how much discovery and custom engineering each kind of interface needs.
| Interface | What it means | How it is verified | Typical effort |
|---|---|---|---|
| Documented, supported API | The vendor publishes and supports an interface your licence includes | Built and tested against your instance, version and permission set | Lowest — but still tested per environment |
| Events and webhooks | The system announces changes as they happen | Delivery, retries and duplicate handling exercised | Moderate |
| Database access | Reading from a replica, view or export rather than the live application | Queries checked for load, correctness and permissions | Moderate; schema changes need watching |
| File exchange | Scheduled exports and imports (CSV, XML, SFTP) | Format validation and reconciliation of totals | Low to moderate; slower to react |
| Legacy or undocumented | Unofficial endpoints, screen automation or custom protocols | Discovery plus the system owner's permission; fragile by nature | Highest — custom engineering and ongoing maintenance |
02How it works
One AI step, surrounded by careful plumbing.
- InputTriggerEvent, schedule or request
- ToolRead from systemsScoped, read-only where possible
- ModelAI stepClassify, extract or draft
- CheckValidateSchema, rules, duplicates
- PersonApproveWhere the action matters↳ rejected: back with a reason
- ToolWrite backIdempotent↳ failure: retry queue, then alert
- StoreAudit logInputs, decision, outcome
- Input
- Tool
- Model
- Check
- Person
- Store
Map the systems
List each system, its version, the interfaces your licence covers, its rate limits, and which system is the source of truth for each piece of data.Scope the credentials
Dedicated service accounts with least privilege — read-only wherever writing is not needed — held in a secrets manager, rotated, and never placed in prompts or browser code.Define contracts
Typed inputs and outputs between each system and the AI step, so malformed or unexpected output is stopped at the boundary instead of travelling on.Design for failure
Timeouts, retries with backoff, dead-letter queues and idempotent writes: an outage should delay work, never lose or duplicate it.Decide who approves what
For each action, choose whether it runs automatically, waits for a person, or is only ever offered as a suggestion.Log and reconcile
Each action is logged with its inputs and outcome, and periodic reconciliation confirms the connected systems still agree.
03Where it helps
Hand-offs people currently re-key or copy and paste.
Potential use case Examples to assess against your own systems.
Inbound email to CRM
Classify incoming enquiries, extract contact and request details, and create or update the CRM record — with uncertain cases queued for review.
Order and invoice matching
Compare purchase orders, deliveries and invoices across the ERP and incoming documents, flagging mismatches for the finance team.
Ticket enrichment
Add a summary, a category and links to related records to each service ticket before anyone picks it up.
Report preparation
Pull figures from several systems into a draft report that a person checks before it is shared.
04Limits and security
Integration inherits the strengths — and the flaws — of the systems it joins.
Limitations
- Inconsistent master data stays inconsistent until it is fixed at the source; AI does not repair it.
- Vendor API changes, rate limits and licence terms can change what is possible over time.
- Real-time behaviour may be impossible where a system only offers batch exports.
Security implications
- Model output is untrusted input to the next system: it is validated and encoded before use, never executed directly.
- Each credential's reach is limited by its scope, so one compromised integration cannot touch unrelated data.
- Only the fields an AI step needs leave the source system.
- Logs record what changed and why, without copying more personal data than necessary.
05How success is measured
Correct actions, fewer re-keyed fields, fast recovery.
The AI step itself is evaluated like any other — see evaluation and governance. The integration adds measures of its own.
- Correctness
- Share of automated actions that were right, judged on reviewed samples.
- Exception rate
- How much work still needs a person, and the reasons why.
- Reliability
- Failed, retried and duplicated operations, and time to recover from an outage.
- Cycle time
- Time from trigger to completed action, compared with the current process.
06Related
Related services.
Next step
Map one hand-off between two systems.
Tell us which systems are involved, what moves between them and who checks it today. That defines the first integration worth building — or shows that a simpler fix exists.