You do not have a Cognite-scale data team. You do not need one. The procurement leak, the AP backlog, the contract queue, the expense audit gap. These are the four places enterprise AI pays for itself in 90 days. We build the systems. You own the source code. Eight to fourteen weeks to production.
Five inputs. Forty-five seconds. A defensible savings figure based on how enterprise AI actually delivers ROI in procurement, accounts payable, and expense audit.
Not vision decks. Not transformation roadmaps. Specific cost centres where AI replaces manual work in 90 days, and where the savings hit the P&L within the first quarter.
Your AP team spends six hours of every working day on invoice keying, three-way matching, exception handling, and approval routing. AI agents read invoices regardless of format, match to PO and goods receipt, route exceptions intelligently, and flag duplicates and fraud.
RFQ generation, supplier matching, quote comparison, PO drafting, contract clause review. Every step in the procurement lifecycle has an AI counterpart that runs in seconds, not hours. Your procurement team negotiates better terms because they finally have the time and the data.
Vendor MSAs, NDAs, SOWs, framework contracts. AI extracts obligations, payment terms, liability caps, renewal dates, and red-flag clauses in seconds. Your legal counsel reviews the AI's findings, not the entire document. Compliance risk falls. Cycle time falls further.
Manual expense audit covers 5% of submitted reports on average. AI audits 100%. It catches duplicate submissions, policy violations, suspicious vendor patterns, and aggregated misuse that is invisible to a human reviewer. Industry benchmark: 1.5%–3% of total T&E spend, year one. Recurring annually.
No twelve-month transformation programmes. No per-seat licensing. No black-box SaaS. We build, you own, and the system pays for itself before the project closes.
Every AI system we ship is built for your data, your ERP, your workflow. You own the source code at the end of the engagement. No subscription, no per-seat pricing, no vendor lock-in.
Most AI agencies talk about pilots that take a quarter to scope. We discover in week one, build through week ten, and have your AP team using the system before the calendar year closes.
We do not sign engagements we cannot deliver. The savings model in our proposal becomes the success criteria of the project. If the system underdelivers, we extend the engagement until it does, at no additional cost.
Each of these is a production-grade reference architecture. We tune to your data and your stack. We deploy to your cloud or ours. You own everything we build.
No proprietary lock-in. No black-box models. Every component of the stack is industry-standard, auditable, and replaceable.
OpenAI GPT-4 (via Azure OpenAI for enterprise data residency). Anthropic Claude (API or AWS Bedrock). Open-source models (Llama, Mistral) for on-premise. Selection driven by use case, cost, and data residency.
LangChain / LlamaIndex for orchestration. Vector databases (Pinecone, Weaviate, pgvector). Agentic frameworks for multi-step automation. Function calling and tool integration patterns.
SAP (S/4HANA, ECC, Ariba), Salesforce, Oracle (NetSuite, EBS, Fusion), Microsoft Dynamics 365, Workday, ServiceNow, Coupa, Concur. Custom REST/GraphQL APIs and SFTP feeds.
Cloud: Azure, AWS, GCP, your account, your data, your rules. On-premise: containerised for regulated environments. Compliance: SOC 2, GDPR, NORSOK-aligned. Full audit logs, model decision tracing, cost monitoring.
We are model-agnostic and cloud-agnostic. The stack adapts to your environment.
The same four reference solutions land in different ways across industries. Here is how we tune them.
Don't see your sector? The four reference solutions adapt to any industry where documents flow, vendors are paid, and contracts govern operations.
Our engineers have shipped enterprise AI systems at scale before joining WAIOZ, across consumer intelligence platforms, financial services, and global SaaS providers. They have built the AP automation systems, the contract intelligence engines, and the expense audit platforms that the Fortune 500 now relies on.
European buyers do not want six-month discovery phases. Neither do we. Here is how we move from a thirty-minute call to a system in production.
A two-week paid discovery (or fixed-fee free for qualified opportunities). Our team embeds with your AP, procurement, and finance leads. We map data flows, ERP integration points, exception patterns, and compliance constraints. Output: a fixed-scope build proposal with success criteria, milestone payments, and timeline.
Custom build by a dedicated WAIOZ squad of 4–6 engineers. Bi-weekly demos. End of week 8: a working pilot processing real invoices, real contracts, real expense reports, in your environment, with your data. Pilot scope is intentionally narrow: one ERP, one entity, one workflow.
Pilot moves to production. Additional entities, ERPs, and workflows are added in parallel. Team training. Documentation handover. Source code transfer. Optional managed support contract from month four onwards (capacity-based, not per-seat).
The fully customised system, the source code, the model configurations, the integration code, the deployment infrastructure documentation, and the operational playbooks. No subscription. No vendor lock-in. No per-seat pricing.
The calculator on this page is conservative. The savings figures we model in the discovery phase are usually higher. If the number you see is meaningful, the next step is a thirty-minute call. No scoping document, no procurement RFP. We map your back office, you tell us where it bleeds, and we tell you in writing whether AI fixes it.
Wondering what AI is actually worth to your P&L? Model your enterprise AI ROI in 60 seconds.
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