The intelligence is available but the data is scattered
An AI system that cannot read from your records or write back to them is a demonstration. Integration is what makes it a business system.
The system object
one version of each fact, across systems
01
Systems
ERP · CRM · finance · support
02
Event
a record changes somewhere
03
Source
which system is authoritative
04
Mapping
field to field, validated
05
Sync
written once, correctly
06
State
visible, reconciled
07 · Exception
Failure
nothing silently written
Where it fits
If people are the integration layer, connect the systems.
Customer history sits in the CRM, order and invoice history in the ERP, correspondence in email, and the reasons behind decisions in documents and chat. Any AI system asked a useful question needs several of those sources at once, and any useful output needs to go back into the system where the team will act on it.
Without integration, teams end up copying information into AI tools and copying answers back out, which is slow, error-prone and hard to govern. The systems were bought separately; connecting them is the work that makes AI practical.
- AI pilots that stall because they cannot access live business data01
- Staff pasting information between systems and AI tools by hand02
- CRM and ERP that disagree about the same customer or order03
- Reporting that requires manual consolidation from several sources04
- Messaging channels used for business conversations that never reach the system of record05
The approach
We integrate for reliability first and intelligence second
Integration work is unglamorous and decisive. We start by understanding each system's data model, API capabilities, permissions and rate limits, then design the data flows so that AI components receive accurate context and their outputs are written back with the right validation. Security, data residency and access control are decided at this stage, not retrofitted.
1. System and data discovery
An inventory of the systems involved, what each one holds, how it can be accessed and which records are authoritative when sources disagree.
2. Integration architecture
The pattern that fits your situation: direct API integration, an integration layer, event-driven synchronisation or a combination, chosen for maintainability.
3. AI service design
How AI components receive context, which model or service is used, how outputs are validated, and how failures are handled without corrupting data.
4. Security and compliance controls
Credential management, least-privilege access, data-handling rules and logging designed with your IT and compliance requirements.
What we build
Integrations we deliver
Illustrative of what a system in this area can include. Every build is designed from your workflow, not from a catalogue.
- 01CRM integrationAI-generated research, summaries, next actions and data hygiene written directly into accounts, contacts and opportunities.
- 02ERP integrationDocument extraction, matching and validation connected to purchasing, sales orders, inventory and finance modules.
- 03Email and collaboration integrationInbound messages classified, routed and drafted for reply from within the tools people already use.
- 04Accounting and finance integrationInvoice, expense and reconciliation workflows connected to your ledger with controlled write-back.
- 05Customer-support integrationTicket classification, suggested responses and knowledge retrieval inside your support platform.
- 06APIs, data and knowledge sourcesSecure connections to internal databases, data warehouses, document stores and third-party services, including messaging channels where compliant.
Writing to a system of record is a decision, not a side effect
Human authority · consequential writes confirmed by a person
Reading data is low risk. Writing it back changes what the business believes to be true. We treat every write path as a controlled action with validation, a named owner and, where the record matters, a human confirmation. Integrations fail safely: if a source is unavailable or an output does not validate, nothing is silently written.
- Explicit rules on which fields an AI component may update and under what conditions
- Validation and confirmation steps before consequential records are changed
- Audit logs showing what was read, what was written and by which component
- Monitoring and alerts for integration failures, with a defined manual fallback
Systems we commonly connect
We work with supported APIs and integration capabilities wherever they exist, and with export, database or file-based approaches where they do not. Product families below are indicative; the relevant question is always what your specific version and configuration allows.
- CRM (Salesforce, HubSpot, Microsoft Dynamics 365, Zoho CRM, Pipedrive)
- ERP (SAP, Oracle NetSuite, Microsoft Dynamics, Odoo, Tally)
- Accounting (Xero, QuickBooks, Zoho Books, Sage)
- Email and collaboration (Microsoft 365, Google Workspace, Slack, Teams)
- Customer support (Zendesk, Freshdesk, Intercom, HubSpot Service)
- Messaging platforms via official business APIs, where policy and regulation allow
- Databases, data warehouses, document stores and internal or partner APIs
How an engagement runs
From scattered systems to a connected one
1. AI Leverage Call
We discuss which systems hold the data that matters, what you want AI to do with it, and whether integration is the blocker.
2. AI Opportunity Blueprint
System and data discovery, integration architecture, AI service design and the control framework for reads and writes.
3. Integration Sprint
The first integration built and running: real data in, validated output back, with monitoring from day one.
4. Production intelligent system
The integration hardened, secured and documented, with alerting and a fallback path for failures.
5. Ongoing optimisation
Adding sources and destinations, adapting to system upgrades and expanding what AI can do with the connected data.
Scope grows only as value is shown. The next workflow is chosen, not assumed.
Tell us which systems need to talk
If you know what AI should do but your data is spread across tools that do not connect, integration is the first problem to solve. List the systems involved and what should flow between them. Discuss an AI opportunity with us and we will outline the safest way to connect them.
Solutions this service supports
Related reading
Questions about this service
Will you need administrator access to our systems?
We ask for the minimum access the integration needs, typically a dedicated integration user with scoped permissions. Credentials are managed through your own secret store or ours under agreed controls, never shared informally.
Can AI write directly into our ERP or CRM?
It can, within limits you set. We usually begin with AI proposing updates that a person confirms, then allow direct writes for low-risk fields once accuracy is established. Every write is validated and logged.
Our ERP is heavily customised. Is that a problem?
Customisation is normal and is why discovery comes first. We map your actual data model and workflows rather than assuming a standard configuration, and design integrations that respect your customisations.
Where is our data processed?
That is decided with you during design. Model and hosting choices can keep processing in a specified region or environment, and data-handling rules are documented so your compliance function can review them.
Do you replace our existing systems?
No. Integration work exists precisely so you can keep the systems that serve you and add intelligence around them. If a system is genuinely holding the business back, we will say so, but replacement is a separate decision.
Where is capability hiding in your business?
Bring one workflow that costs your team more time than it should. We will tell you honestly whether AI, automation or a better system belongs there.