AI integration services for the systems you already run
ComplxAI provides AI integration services across Australia. A model earns nothing until its output lands where your team works: the CRM, the ledger, the helpdesk. We connect LLMs and agents to those systems through APIs, events and queues, with a person approving any write that carries consequences.
- Senior engineers do the work themselves
- Adapters and infrastructure code are yours from day one of any paid stage
- Written price for each stage, exit at any boundary
Integrating AI with the CRM, the ledger and the inbox
You may not need new systems; the ones you have need to talk to a model and to each other. AI integration is that work: read from Xero, MYOB, HubSpot, Salesforce, email, document stores and internal databases, run the right model, write the result back where a person can check it.
If the application does not exist yet, start with custom AI development. If the goal is a whole process running end to end, see agentic workflow automation. This page is the plumbing beneath both.
A good fit if…
- Data lives in three or more systems and people re-key it between them
- Finance will not accept unreviewed writes to the ledger or the CRM
- IT wants an integration it can read, monitor and switch off
Five ways to integrate AI into existing systems
Which pattern fits depends on what a system exposes and how fast the result is needed. We choose per integration and say why; if your system is not named here, only the adapter changes.
| Pattern | Use it when | What we build | Watch for |
|---|---|---|---|
| API-first | A documented API covers the objects you need. | Typed clients, scoped auth, pagination, rate-limit budgets, contract tests against the sandbox. | Undocumented limits; version deprecations. |
| Event-driven | Results must land seconds after a trigger: a new email, a paid invoice, a closed deal. | Webhook receivers behind API Gateway, EventBridge routing, SQS queues, idempotent consumers, dead-letter handling. | Duplicate deliveries; events out of order. |
| Scheduled sync | Minutes of delay are fine and volume is bounded: nightly enrichment, hourly reconciliation. | Step Functions or scheduled Lambda jobs, watermark tracking, backfill and replay. | Drift between runs; records that never sync. |
| Middleware and adapters | Several systems share one canonical model, or a legacy system needs shielding from change. | One adapter per system behind a single internal contract, with explicit schema mapping. | The adapter layer becoming a second product. |
| RPA and browser automation | No API, export or webhook, and the vendor will not add one. A last resort, treated as temporary. | Sandboxed browser sessions with a screenshot per step, change alerts and a manual fallback. | Breaks on UI changes; vendor terms-of-service and audit questions. |
The integration layer between your systems and the model
Four working layers and a control plane. The model never talks to a system of record directly.
- Systems of recordwhere truth livesXeroMYOBHubSpotSalesforceEmail and calendarDocument storesInternal databasesHelpdeskJob system
- Integration and event layercontracts, queues, retriesAPI GatewayWebhook receiversEventBridgeSQS queuesAdapter per systemIdempotency keysDead-letter queues
- AI servicesextraction, classification, retrievalModel routerStructured outputsDocument extractionClassification and triageRAG over wiki and CRMEvaluation harness
- Human approvalwhere consequences sitApproval queueConfidence thresholdsReviewer interfaceEdit or rejectEscalation pathDecision log
- Controlsidentity, audit, operationsService accountsOAuth scopesLeast-privilege IAMSecrets managementAudit log per writeCloudWatch alarms
We build this on AWS, serverless where volume allows, and we design every write to trace back to the event, the model call and the person behind it. The platform side is our AWS consulting; retrieval, routing and evaluation are production AI engineering; and when the AI layer acts rather than drafts, it becomes AI agent development.
What separates an integration from a script
A script works on the happy path. An integration keeps working when the API is down, the record is malformed and the token has expired.
Identity and permissions
Each integration runs as its own service account or OAuth app with the narrowest scopes the task allows. Credentials sit in a secrets manager in your cloud account, never in a prompt or a log.
Data mapping and quality
A company in HubSpot, a contact in Xero and a row in your database are three shapes for one thing. We define the canonical model, write each mapping explicitly, validate on the way in and reject rather than guess.
Idempotency, retries and dead letters
Every write carries an idempotency key, so a retried event cannot create a second invoice. Retries back off inside each vendor's rate budget. Permanent failures land in a dead-letter queue with the original payload and raise an alarm; ambiguous ones go to a reviewer with a plain-language explanation.
Audit log, sandbox testing and rollout
Every read, model call, approval and write is logged: who, what, when, which prompt and model version. We test against the vendor's sandbox on recorded events before production is touched, then roll out per team or record type behind a kill switch, with dashboards for throughput, errors and cost per record.
How an integration project runs
Integration work follows the five stages in how we work. Inside an implementation block the order is fixed, because the order keeps production safe.
- MapSystems, objects, owners, volumes, and the writes that must wait for a human.
- AccessService accounts, OAuth scopes, sandbox tenants and secrets, provisioned in your accounts.
- ContractCanonical data model, field mappings, event schemas and error classes agreed in writing.
- BuildAdapters, queues, model services and the reviewer interface, in vertical slices.
- RehearseSandbox runs on recorded events, load tests, failure injection, reconciliation checks.
- Roll outPer team or record type, alarms live, runbooks and documentation handed over.
Where integrated AI earns its keep
Five examples, each pairing a model with a system of record.
CRM records enriched from documents and email
A model reads proposals, contracts and email threads, extracts key fields and updates the HubSpot or Salesforce record with the source attached. The record stays authoritative.
Invoice and receipt extraction into accounting
Supplier invoices arrive by email or upload, are parsed into line items, matched to purchase orders or jobs and pushed to Xero or MYOB as drafts. Exceptions surface daily, not at month end.
Ticket triage into the helpdesk
Inbound requests are classified, prioritised and routed into the helpdesk with a suggested reply and the relevant knowledge article. Agents accept, edit or reject, and each decision feeds the evaluation set.
Knowledge assistant over the wiki and CRM
One assistant answers staff questions from the wiki, policy documents and CRM history. Retrieval is permission-aware, so people see only what they could already open, and every answer cites its source.
Quotes generated from the inbox into the job system
Enquiries land in a shared inbox, a model drafts a quote from your price book and job history, and once approved the contact, job and quote are created in the job system in one step.
Integrations we have shipped
Two engagements where integration carried most of the outcome. The figures are the clients’ own reports; more detail is on the case studies page.
DisabilityAssessments
End-to-end invoice processing for providers and plan managers, automated reconciliations, matching and alerting, on secure patient-data storage. The client reports 90% of operations automated, running on 10% human resource.
Read the DisabilityAssessments case studyLuxpip
A custom B2B API and agentic tooling for internal operations, delivered alongside the client's web and mobile apps and admin panel. The client reports manual workload down 75% and a 40% scale-up.
Read the Luxpip case studyWhy teams bring integration work to us
One team, no hand-off
The engineers who scope the integration write it, deploy it and hand it over. Remote-first across Australia, onsite when a whiteboard beats a call.
Code and credentials stay yours
Adapters, infrastructure code, mappings and runbooks live in your repositories and cloud accounts from day one of any paid stage. Take it in-house whenever you choose.
Written pricing, clean exits
Two free conversations, then Discovery, Strategy and 12-week Implementation blocks, each priced before it starts. Exit at any boundary; no retainer.
Built to be operated
Dashboards, alarms and runbooks are designed in from the first block, not bolted on at the end. The same approach applies across our custom software development work.
Services on either side of the integration layer
AI development
Applications and LLM systems built around your data.
AI development servicesAI agent development
Agents that act in your systems under approval, with a full trail.
Custom AI agentsAI automation
Whole processes run end to end by agentic workflows.
Agentic workflow automationCustom software development
Platforms and internal tools with the integration layer designed in.
Software built around your systemsAWS consulting
The serverless, event-driven foundation the layer runs on.
AWS architectureAI engineering
Retrieval, routing, evaluation and observability for the model side.
Production AI engineeringQuestions buyers ask before integrating
The specific ones about your systems are best answered on a call, and the first one is free.
Book a free intro callDo we need to replace our existing systems?
No. Xero, HubSpot or your database stays the system of record and we add a layer beside it. We recommend replacing a system only when Discovery shows it is the bottleneck, and we put that in writing.
What if one of our systems has no API?
We look for an export, a database view, a file drop or a vendor-supported webhook first. Browser automation is the last option: it breaks when a screen changes, so it gets screenshots, alerts, a manual fallback and a retirement plan from the start.
How do you keep our data safe during integration?
Each integration has its own least-privilege credentials in a secrets manager in your cloud account. Data is validated and, where needed, redacted before it reaches a model; every read and write is logged. Storage and processing are deployed in the region you need; for Australian data residency that is the AWS Sydney region. Where a model API is only served offshore, we say so upfront.
What happens when something fails?
Transient failures retry with backoff; idempotency keys stop retries creating duplicates. Permanent failures go to a dead-letter queue with the full payload and raise an alarm. Anything ambiguous waits for a person. Nothing is dropped silently, and every failed case can be replayed.
How long does an AI integration take?
It depends on how many systems are involved and how many writes need approval. Discovery takes two to four weeks and produces a prioritised use-case map; Strategy turns that into a priced implementation plan; Implementation runs in 12-week blocks with working software every couple of weeks.
Tell us which systems need to talk
Bring the list of systems and the process that spans them. Two free conversations come first; after that, each stage arrives with a written price.