CRM, inbox and internal tools
Connect model calls to Salesforce, HubSpot or a custom API. Match records by stable identifiers, handle expired access and keep changes visible in the tools your team already checks.
AI integration services for the tools you already run
AI integration services connect a model to the records and actions that make it useful. dplyz works inside your existing systems, so answers reflect your data and changes follow your access rules.
Discuss a project
Your data has rules. The integration needs them too.
A demo can answer from a folder of sample documents. A working integration has to know which version is current, who may read it and which system owns the final record. We map those details before giving a model access.
Scope of work
Knowledge with sources
Make policies, product information and internal documents searchable. Carry source references into the answer and preserve document permissions when information is retrieved.
Writes you can trace
Define a small set of actions with explicit inputs. Validate them before execution, handle retries without duplicate updates and record the result for the person responsible.
A working example
A service rep asks about a delayed order
An illustrative integration across customer records, orders and delivery information.
- 01
Identify
Use the signed-in rep’s access to find the customer and order.
- 02
Retrieve
Read the order system and the latest shipment update, with timestamps.
- 03
Respond
Prepare a reply with the source information and any missing details called out.
- 04
Update
After approval, log the reply and next task on the correct CRM record.
Built to hand over
What you receive
AI integration extends the tools you already run. If those applications need replacing, an agentic rebuild covers the custom software, shared architecture and migration into a new stack.
- System and data ownership map
- Tested API connections and permission checks
- Source-linked answers and logged actions
- Retry, timeout and failure-handling documentation
About AI integration
Usually the first step is to work through its existing APIs or approved data exports. We check the access available and explain any limitations before agreeing the integration scope.
No. Model selection depends on the task, data requirements, latency and cost. The integration keeps business rules in your application so a model change does not mean rebuilding the process.
Let's talk about AI integration
Tell us what you want to change, what you run today and who will use the result.