Bring the right context in
Retrieve the records, documents, states, and permissions a useful response depends on—without flattening the access rules around them.
- System to AI
- Identity · mapping · retrieval
Intelligence, connected to the work.
We connect AI capabilities with the CRMs, ERPs, support platforms, databases, APIs, and internal tools your organisation already relies on.
Customer records
Support platform
Product database
Internal workflow
Retrieve permitted context
Apply the defined AI capability
Return a bounded update or action
AI appears inside the service flow—not as another disconnected destination.
Useful AI needs a controlled route into—and back out of—your systems.
Most integrations combine three jobs. Keeping them explicit makes permissions, quality, and operational ownership easier to design.
Apply intelligence in context
Use the selected AI capability to classify, extract, explain, recommend, or prepare a decision inside a defined service boundary.
- Context through AI
- Instructions · evaluation · limits
Return a useful next state
Send a structured result, prepared action, or bounded update back to the system that owns the work, with validation and a clear failure path.
- AI to system
- Validation · audit · recovery
We design the connection around the systems that own the work, the records that may move, and the action that must return.
Bring account, relationship, and case context into a service flow; return notes, classifications, or prepared updates.
Identity and field-level access
Read permitted product, order, inventory, finance, or fulfilment states without bypassing the system of record.
Transactional boundaries
Enrich tickets, route enquiries, retrieve context, draft responses, and return structured service notes.
Agent review and handoff
Retrieve governed records and company knowledge through controlled queries and owned indexes.
Source ownership and freshness
Expose a narrow set of product capabilities or internal actions to an AI-enabled experience.
Contract and versioning
Place AI-assisted classification, extraction, review, or preparation inside the tools teams already use.
Role, approval, and audit
Specific connections depend on the interfaces, access model, data quality, commercial terms, and technical limits of the systems involved. The categories above do not imply formal partnerships.
A connection is not complete
until failure is designed too.
The interface contract defines what the integration may see, what it may change, how each response is validated, and what happens when a dependency is unavailable or unclear.
Who is asking?
What is needed?
What may cross?
What may return?
What changed?
Who owns the next state?
Does the request carry the right actor and role?
- Authentication context
- Entitlements and access
- Delegation rules
Is the minimum permitted context crossing the boundary?
- Field selection
- Transformation and redaction
- Freshness and provenance
Is the returned change valid and appropriately authorised?
- Schema validation
- Confirmation or approval
- Idempotency and limits
Can the service stop safely and recover visibly?
- Timeout and retry
- Fallback route
- Owner and alert
This contract sits alongside the software connection. It gives product, security, data, and operations teams a shared description of the live service.
Different systems. Different access. One clear responsibility for each connection.
AI inside a product
Add classification, extraction, explanation, generation, or recommendation to an existing product journey without sending the user to a separate tool.
- User request · product state · permitted records
- Structured response · product-ready output
- The product remains the owner of the journey
AI inside service operations
Bring account and case context into support work, prepare a response or route, and return the useful result to the service platform.
- Case · account · knowledge · service rules
- Prepared answer · classification · next route
- A person owns exceptions and sensitive decisions
AI over governed knowledge
Connect an assistant or internal tool to owned company sources with permissions, source context, and update responsibility intact.
- Question · identity · selected sources
- Grounded explanation · source context
- Missing or conflicting sources narrow the answer
AI across a workflow
Let an agent or automation retrieve context and prepare bounded steps across several systems while each system keeps its own authority.
- Trigger · workflow state · permitted tools
- Prepared action · status · audit record
- High-impact or irreversible actions require approval
Five operating layers keep the connection useful after the first successful request.
The route between AI and operations.
Each layer has an owner, a test, and an observable state.
Permissions
Carry the right identity and expose only the fields and actions that role may use.
Mapping
Translate records and outputs into stable, documented structures the service understands.
Validation
Check inputs and returned actions before they cross into the next system.
Observability
Record requests, decisions, failures, and service-level signals without logging unnecessary sensitive data.
Recovery
Define retries, safe stops, fallbacks, reconciliation, and the person or team that owns an exception.
From interface audit to an integration your team can operate.
Scope is shaped by the number of systems, access requirements, data condition, action risk, and the environments needed for testing and launch.
Understand the current service, systems of record, available interfaces, access model, and operational ownership.
- System map
- Interface inventory
- Risk and dependency notes
Define what crosses each boundary, which system remains authoritative, and where validation or human approval sits.
- Data and action contract
- Identity model
- Failure routes
Shape the records and sources the AI capability can use while preserving ownership, freshness, and permissions.
- Field mapping
- Source preparation
- Transformation rules
Build and test the interfaces, retrieval, transformations, actions, and service-side logic needed for the live route.
- API and event connections
- Read and write paths
- Test environments
Exercise realistic records, permissions, edge cases, outages, retries, and incorrect AI outputs before launch.
- Integration tests
- Failure testing
- Acceptance scenarios
Introduce the connection carefully and establish the monitoring, change, incident, and ownership model around it.
- Release plan
- Runbook and alerts
- Handover
We begin with the service and its system boundaries, then prove the connection on realistic records and failure conditions before widening it.
01MapTrace the service, system owners, records, actions, and pain points around the proposed connection.
02ContractDefine identity, data, action, validation, ownership, and failure behaviour for each boundary.
03ProveConnect the smallest useful path with representative records and observable test conditions.The smallest useful path first.
04HardenExercise permissions, malformed data, dependency failures, retries, and inappropriate outputs.
05OperateLaunch with monitoring, alerts, runbooks, change ownership, and a route for improvement.
How a connected service moves from record to useful action.
Receive the request, retrieve permitted context, apply the capability, then return an owned next state. The lime station marks the applied step.
- Support-platform integration
- Identity, customer records, product data, governed knowledge, and support operations.
- Classification, grounded response preparation, and route recommendation inside defined boundaries.
- Own exceptions, sensitive decisions, final service judgement, and operational improvement.
- A structured note and next route in the platform that owns the case.
- Permission checks, source ownership, validation, logs, safe failure, and an accountable service owner.
Illustrative integration pattern, not a client case study or a performance claim.
The connection should be observable, reversible, and owned.
Four safeguard groups agreed with your security, engineering, and operations teams—design decisions, not guarantees.
Least privilegeUse the narrowest identity, fields, and actions needed for the designed service.
Data minimisationMove and retain only the context the integration requires.
System authorityKeep the owning platform authoritative for records and decisions.
ValidationCheck structured outputs and proposed actions before they enter another system.
ConfirmationRequire review or approval for sensitive, high-impact, or irreversible steps.
IdempotencyPrevent retries from duplicating a write or applying the same change twice.
Timeout and retryBound waiting and retry behaviour so a failing dependency does not stall the whole service.
Safe degradationNarrow or stop the capability when required context is unavailable.
ReconciliationIdentify and repair partial or inconsistent states after a failure.
ObservabilityMonitor requests, latency, failures, and service outcomes at the level the team can act on.
Change controlReview model, prompt, mapping, schema, permission, and dependency changes before release.
OwnershipName who receives alerts, resolves exceptions, and decides how the live connection evolves.
Working answers. Scope, access, timing, and commitments are agreed per engagement, in writing.
An AI integration connects a defined capability—such as classification, extraction, generation, retrieval, recommendation, or agent action—to an existing product, data source, business system, or workflow. The integration includes the interfaces, identity, data mapping, validation, failure handling, and operating model around that capability.
Often, when the system exposes suitable interfaces and the required access can be granted under its existing rules. We review the specific platform, records, permissions, actions, rate limits, environments, and commercial constraints before confirming scope.
Usually not. This service is specifically about placing useful AI capabilities inside or alongside the systems that already own the work. Replacement may be considered only when an existing dependency cannot support the required service safely or reliably.
Potentially. We define exactly which actions are allowed, how identity and authorisation are carried, what validation occurs, whether a person must confirm, how duplicate writes are prevented, and what happens if the action only partly succeeds.
By minimising the fields that cross each boundary, carrying the appropriate identity and permissions, using the security controls available in the connected systems and infrastructure, and defining retention, logging, redaction, and access with the relevant security and legal owners.
The architecture can be designed so the service is not needlessly tied to a single model interface, where the use case and available infrastructure support that choice. Actual portability depends on capability differences, evaluation results, contracts, data handling, and operational requirements.
With representative records, identities, permissions, malformed inputs, incorrect or unsafe AI outputs, timeouts, dependency failures, retries, duplicate events, and reconciliation scenarios. Acceptance criteria are tied to the service behaviour, not only a successful technical connection.
Someone in your organisation needs to own the live service, its system access, source content, alerts, incidents, changes, and performance. We define that responsibility and the supporting runbook during the engagement, and can scope ongoing support separately.
Where does the intelligence
need to connect?
Show us the systems involved, the records that matter, and the action the service should support. We will help map the smallest useful integration and the controls it needs.
studio@quirkydock.com · working internationally · CET