AI TOOLS & ENTERPRISE AI INTEGRATION
Bring advanced AI intelligence into the applications, data and workflows your teams already depend on.
Business systems hold valuable information. But turning that information into a clear decision still takes effort: reviewing documents, testing exceptions, interpreting trends and finding the right allocation.
DataDevice uses Microsoft Foundry and a flexible, multi-model AI architecture to bring intelligent analysis and AI-assisted processing into existing business applications. We connect suitable AI models, including large language models (LLMs), with structured data, operational context and defined controls so organisations can move beyond standalone chatbots towards practical, governed AI solutions.
From financial document and receipt processing to dashboards, management reporting, compliance checks and intelligent allocations, the objective is straightforward: make information more useful without losing control of the decisions that matter.
The difficulty is rarely a shortage of information. It is the work required to interpret it. A financial controller may have access to every transaction yet still spend hours explaining an unexpected variance. An AP team may hold the supplier invoice and purchase order but still need to untangle conflicting references. A manager may receive a report that describes yesterday but gives little indication of what needs attention today.
AI can help interpret documents, surface patterns and suggest the next step. What turns that intelligence into business value is the surrounding workflow: the records it can rely on, the rules it must respect, and the people authorised to approve the result.
Our approach is to bring those elements together around the applications and business processes already in place.
A powerful model is only one component of an enterprise solution. It must connect to the right information, operate within the right permissions and produce results that can be reviewed, measured and used by existing systems.
DataDevice uses Microsoft Foundry (formerly Azure AI Foundry) as an enterprise AI platform for model integration, development and operational governance, including solutions that use large language models (LLMs). Together with application-level connectors and orchestration logic, this enables AI-assisted services to be incorporated into business workflows rather than isolated in a separate chat experience.
The best model is the one that meets the task. A high-volume classification process may need speed and a predictable cost. A complicated exception may need more detailed reasoning. A management report may need careful interpretation of lengthy, structured source material.
Our architecture is designed to support integration with leading model providers, subject to model availability, access, hosting requirements and technical fit. Rather than tying the customer to a single model, we assess the provider and model route that best supports the business outcome.
A large language model (LLM) can interpret natural-language content and, where the selected model supports it, visual document inputs. In business workflows, an LLM can help understand varied supplier descriptions, extract information from non-standard documents, classify transactions, draft structured summaries and suggest candidate allocations using authorised context.
LLM outputs are recommendations or proposed structured data, not automatic financial authority. Deterministic validation, reference-data checks, configured permissions and the appropriate approvals remain responsible for accounting entries, compliance results and posting decisions.
Suitable for general-purpose language understanding, structured extraction, summarisation and reasoning-intensive analysis. Model selection is actively maintained against the current API catalogue to ensure long-term lifecycle support.
An option for detailed document interpretation, extended analysis, knowledge work and structured business communication. Different Claude tiers can be assessed for complex reasoning, balanced workloads or high-throughput processing. Technical note: A direct Anthropic API connection and a supported native-cloud deployment can differ in regional data residency, data-processing terms and tenant-isolation parameters. The actual route and contractual conditions must be confirmed before customer information is processed.
An option for multimodal information understanding, complex analysis and responsive processing. Where appropriate, Gemini models can be integrated using supported provider APIs and application connectors. Technical note: A direct Google API connection and any separately confirmed native-cloud hosting option can differ in regional data residency, data-processing terms and tenant-isolation parameters. Availability and the exact route must be verified before customer information is processed.
Important: An available model is not necessarily a native Microsoft Foundry deployment. Provider API integration, Foundry-hosted partner models and Azure-hosted arrangements can have different regional data residency, data-processing terms, tenant-isolation parameters and technical requirements.
AI becomes useful when its output can be trusted in context. DataDevice designs workflows that connect authorised business information with the right analysis and the controls required to act on it.
Bring together the documents, relevant reference records and structured information needed for the task. Access is limited to the information the solution is authorised to use.
A selected model, including a suitable LLM where appropriate, supports interpretation, classification, summarisation, anomaly explanation or recommendation. The choice of model depends on accuracy, latency, complexity and cost requirements.
AI output is checked against deterministic rules, source records and configured tolerances. Uncertain results or policy exceptions can be directed for further verification.
The result becomes a structured document record, a dashboard insight, a report narrative, a proposed allocation, an exception item or another workflow outcome, subject to the applicable approvals and implementation scope.
Business confidence matters as much as model capability. An AI solution needs to work within established security, operational and governance expectations, particularly where financial and supplier information is involved.
Depending on the design, safeguards may include identity and role-based access, authorised data retrieval, secure provider connections, minimisation of sensitive information, evaluation against representative examples, activity logging, exception queues and human review.
Microsoft Foundry provides capabilities for managing models, evaluations, monitoring and access. Additional controls may be required in the connected application and orchestration layer. Regional data residency, data-processing terms, tenant-isolation parameters and retention must be reviewed for the specific model and deployment route, including any direct API or native-cloud hosting option.
The objective is controlled assistance, not unreviewed automation.
We develop AI-driven solutions that bring interpretation and decision support closer to the finance and operational processes they serve. The following capabilities are solution areas; the precise scope, connected systems and stage of delivery are defined with each customer.
AI CAPABILITY 01
What it does: AI-assisted document recognition and suitable multimodal large language models (LLMs) can interpret unstructured or semi-structured business documents - including PDF supplier invoices, scanned or photographed receipts, purchase orders and legible handwritten dockets - and organise relevant text and values into proposed ledger-ready records, structured database fields or spreadsheet columns.
Real-world SME use: An SME using Xero or a document-capture product such as Dext may still receive unusual supplier bills, handwritten delivery dockets or invoices with job-level line-item charges. A tailored LLM-assisted connector (sometimes called an LLM wrapper) could help interpret these formats, propose clean supplier and cost information, and prepare line-item costs for a job-costing spreadsheet or a supported finance-system import. This is an illustrative extension, not a claim of a prebuilt integration or universal document compatibility.
Control principle: Extracted amounts, GST, supplier identities, purchase order and job references, and duplicates must be checked against source documents and configured business rules. Uncertain handwriting, missing fields and inconsistencies are flagged for review; records should not be posted to a ledger solely because an LLM has extracted them. Handwritten dockets and unstructured receipts must always enter the exception queue for manual validation by an authorised human reviewer, regardless of model confidence. They must never bypass the exception queue for automated posting.
A small builder receives PDF supplier bills, a photographed receipt and a handwritten delivery docket. AI-assisted extraction and a capable LLM structure the supplier, line-item and amount information; validation checks totals, tax treatment and available job codes. An AP reviewer must manually validate the photographed receipt and handwritten docket through the exception queue, as well as resolve unclear values, before approved information is exported to the job-costing spreadsheet or connected system using the agreed method.
AI CAPABILITY 02
A dashboard should explain what matters, not simply display another set of numbers. AI can help turn validated financial and operational information into relevant summaries, anomaly prompts and questions for management investigation.
Potential applications include budget and expenditure intelligence, invoice and procurement trends, operational exceptions, natural-language questions over authorised data and management commentary tied to defined metrics.
Control principle: Calculations and underlying figures come from governed business data. AI assists with interpretation and explanation; it must not fabricate values or silently change the underlying metrics. All AI-generated interpretation summaries and analytical narratives must be logged with immutable audit trails for compliance tracking.
A Finance Manager opens an expenditure dashboard and asks what drove an increase across three divisions. The application retrieves authorised transaction summaries, calculates the variance using defined measures, and AI proposes an explanation supported by the relevant records. The manager can inspect the source figures before acting.
AI CAPABILITY 03
Reports should help decision-makers understand the movement behind the numbers. We design reporting workflows that can bring structured data, approved measures and AI-assisted narrative together in a consistent management format.
Examples include weekly operational packs, monthly management reporting, executive summaries, financial and budget analysis, procurement reporting, exceptions and matters requiring management attention.
Control principle: Reports should distinguish sourced facts, calculated results and AI-generated commentary. Review and distribution rules should be defined before reports are issued.
Approved month-end data feeds a management report. AI drafts commentary on expenditure movements, delayed approvals and notable exceptions. A responsible reviewer checks the narrative against source measures and approves the pack for circulation.
AI CAPABILITY 04
Interpretation helps find the issue. Rules determine the result. AI can support the understanding of varied documents and transaction context, while deterministic controls assess configured conditions such as PO limits, approval thresholds, supplier checks or required supporting information.
Potential applications include invoice and financial document validation, purchase order compliance, policy checks, supplier verification, document consistency review and exception escalation.
Control principle: Policy rules, audit evidence and approval responsibilities remain explicit. AI assistance should not be presented as legal assurance or as an autonomous compliance sign-off.
An invoice amount differs from the approved PO. AI assists with interpreting supplier descriptions and identifying possible explanations, while deterministic checks calculate the variance and determine the configured exception route. An authorised user resolves the case.
AI CAPABILITY 05
A transaction may be valid but still need the right destination. AI can use document clues, available reference data and relevant history to suggest the most likely Job Code, Cost Centre, GL Account, supplier or Purchase Order for a particular transaction.
Potential applications include AI-assisted invoice coding, financial transaction classification, supplier identification, purchase order matching and cost allocation recommendations.
Control principle: Recommendations are evaluated against permitted values, entity constraints and configured rules. An automated allocation recommendation may progress only when it meets a strict, pre-configured confidence threshold. Every transaction scoring below that threshold must be routed to an exception queue for manual approval. Meeting the threshold does not, by itself, authorise posting; ambiguous or high-consequence allocations still require the relevant approvals.
A subcontractor invoice omits the Job Code. The solution uses the invoice description, supplier context and approved job records to generate a candidate allocation. Validation confirms the job is available for the relevant entity; any remaining ambiguity is routed to the appropriate reviewer. A candidate below the pre-configured confidence threshold must go to the exception queue for manual approval.
The value of enterprise AI is measured in the work it helps resolve: less manual document re-keying, fewer repetitive checks, earlier identification of exceptions, clearer management information and more consistent decisions.
Our focus is integration. Conversational interfaces may be useful, but we also design AI-supported document and receipt processing, dashboards, reports, compliance workflows and allocation processes that connect with existing business applications and controls.
Our architecture can support integration with these model providers where technically and commercially feasible. Exact model access, endpoint, region, hosting and usage rights are confirmed during technical assessment. For Claude and Gemini, the chosen direct API or confirmed native-cloud deployment route can affect regional data residency, data-processing terms and tenant-isolation parameters.
No. Some models may be available through Foundry deployments, while others require an external provider API or another approved integration route. Data-handling and governance requirements are assessed for the selected approach. For Claude and Gemini, specific provider routes must be assessed for regional data residency, data-processing terms and tenant isolation before use.
AI may recommend or interpret, but governed reference records, validation rules and approval controls determine whether information can progress. Transactions requiring judgement can be escalated for review. Automated allocation recommendations must meet the configured confidence threshold; results below it require exception-queue review and manual approval.
Where the selected model and document quality allow, LLM-assisted processing can help interpret unusual supplier layouts, receipts and legible handwriting, then propose structured fields or line items. Results are checked against original documents and business rules, with uncertain data reviewed by an authorised person before any accounting handoff. Handwritten dockets and unstructured receipts always require manual validation by an authorised reviewer through the exception queue and cannot proceed directly to automated posting.
Potentially yes. We assess the application interfaces, authorised data access, workflow requirements and integration effort before agreeing the solution.
AI can help describe and interpret approved measures, but important financial figures should be calculated and reconciled from source systems. Commentary can be reviewed, traced and corrected before release. All AI-generated interpretation summaries and analytical narratives must be logged with immutable audit trails for compliance tracking.
The design is assessed against the chosen hosting and provider route, customer access requirements and appropriate security arrangements. We do not assume all models share the same residency, retention or contractual terms. Claude and Gemini hosting and API options may have different regional residency, data-processing terms and tenant-isolation parameters.
Your next AI initiative should start with a business decision or process - not a model name. Whether you want cleaner financial document processing, clearer dashboards, decision-ready reporting, structured compliance reviews or more reliable allocations, DataDevice can help assess where AI belongs and how it should connect with your existing systems.
Tell us what information your teams work with, where the manual effort sits and which controls must remain in place. We will discuss the options for a practical, governed implementation.
