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Practical guides, tools and checklists for putting AI and custom software to work in your business.
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Three in-depth guides, one for each service.
AI workflow automation: a practical guide for operations teams
AI workflow automation puts AI models inside a defined business process to read, classify and prepare work, while fixed rules, integrations and named people handle the steps that must be predictable. It suits repetitive, document-heavy or request-heavy work such as intake, routing, document checks and approvals. Start with one measurable workflow, keep consequential approvals with an accountable person, and expand only once the numbers show it works.
Read the guideRunning AI in production on AWS and AzureRunning AI in production on AWS or Azure means giving a model the same controls as any business system: identity, secrets, logging, cost limits, fallbacks and a named owner for every operating task. Managed services such as Amazon Bedrock and Azure OpenAI in Microsoft Foundry host the models, but the architecture, data boundaries and day-to-day operations remain your decisions to make and assign.Read Build, buy or integrate: deciding on custom business softwareBuy standard software when the process is common and you can adapt to the product, integrate when your existing systems already hold the data and only the connections are missing, and build custom software only where the process sets you apart or no product fits its core steps. Most growing companies land on a hybrid: buy the core system of record, then integrate or build the edges around it. Compare the options on 3 to 5 year total cost of ownership, not on the first invoice.Read
Library
Guides, articles and checklists for each part of the work.
AI Integration & Automation
Automate the routine work between your systems, with people kept in the decisions.
All 35 on AI Integration & AutomationDistributor order validation: checking orders before they reach your ERPOrder validation automation reads each distributor or retailer order, whether it arrives by email, PDF, EDI or portal, and checks it against the product master, price list, trade terms, active promotions and credit position before it is entered in the ERP. Clean orders pass straight through. Exceptions go to the sales operations owner with the evidence attached, and credit, pricing and promotion decisions stay with a named person.Read Shipping document verification: automating the field-by-field checksShipping document verification means comparing the commercial invoice, packing list, bill of lading or air waybill and purchase order for the same shipment, field by field, before the freight moves or the entry is filed. AI can read the documents, line up the fields and route each mismatch to the operator who owns that shipment. Customs classification, valuation decisions and the filing itself stay with a licensed broker and your own people.Read Client document collection for accounting, legal and advisory firms: from request to ready fileClient document collection automation sends each client a tailored request list, tracks what arrives, checks that each document is the right one and complete, chases what is missing and hands the professional a ready file. It cuts the administrative back and forth, while the professional reviews the documents, decides what they mean and owns the advice.Read
AI & Cloud Infrastructure
The cloud, data and model foundations an AI workload needs to run in production.
All 12 on AI & Cloud InfrastructureMonitoring AI in production: what to log, measure and alert onMonitoring AI in production means watching four things that ordinary uptime checks miss: whether the answers are still good, what each task costs, how the system behaves when inputs change, and whether the human approval step is keeping up. Log every model call and tool action with sensitive data protected, sample real outputs for review, alert on thresholds a named owner agreed in advance, and write down how to roll back.Read RAG, fine-tuning or prompting: how to give AI your company's knowledgeUse retrieval (RAG) when the AI must answer from facts that change and show where each answer came from. Use fine-tuning when you need the model to behave consistently, for example a fixed output format or a narrow classification. Start with prompting, because a small, stable body of context often needs neither.Read How to write an AI acceptable use policy for employeesAn AI acceptable use policy tells employees which AI tools they may use, what data may go into them, how closely to check the output, and who to ask when a new tool shows up. It matters now because most employees already use AI on their own initiative, often through personal accounts the company cannot see. A workable policy fits on two pages, matches its rules to how sensitive the data is, and names one owner who keeps it current.Read
Custom Solutions
When to build, buy or integrate, and how to get internal tools and portals right.
All 8 on Custom SolutionsWhat drives the cost of custom softwareThe cost of custom software is set mostly by scope: how many user roles the software serves, how many systems it connects to, how much existing data has to move, and what the security and compliance requirements are. Technology choices and hourly rates matter far less than most buyers expect. Budget for the years after launch as well, because hosting, support and maintenance continue for as long as the business depends on the software.Read Internal tools and customer portals: planning the first releaseThe first release of an internal tool or customer portal should cover one workflow for one group of users, end to end, rather than a thin version of everything. Decide roles, permissions and how people sign in before design starts, because those choices are expensive to retrofit. Then measure adoption against the work actually moving through the tool, not against logins.Read Off-the-shelf, no-code or custom software: when to use eachBuy an off-the-shelf product when the process is standard and a vendor already handles it well. Use a no-code or low-code platform for internal tools and team workflows where speed matters more than scale, and govern those apps the same way you govern any other business system. Build custom software when the process is specific to your business, when data volume or integration depth exceeds what a platform can carry, or when the app must live inside a system of record you already own.Read
Case studies
Tools and checklists
Work out what is worth doing, what needs to be ready, and how to move forward.
AI readiness self-assessment
Answer 12 questions to see what is ready and what to work on first.
Check your readinessAI project brief builder
Turn a project idea into a clear brief you can share with a partner.
Build your briefBrowse by industry
Where AI and automation help in your sector.
RetailOrders, stock requests and product updates that move without anyone re-keying them.
LogisticsShipping documents checked and every exception routed to the person who owns it.
TelecommunicationsActivations, field visits and support requests that reach the right team first time.
Real estateInquiries answered, applications complete and maintenance moving before anyone chases.
FMCGDistributor orders, promotions and launches coordinated from brief to sign-off.
Professional servicesClient intake handled, so your people spend their time on judgment.AI and software terms, explained
Plain definitions for the words that come up in every AI project.
Agentic workflowAI acceptable use policyAI agentAI gatewayAI governanceAI guardrailsAI inferenceAI observabilityAI readiness assessmentAI workflow automationAPI (application programming interface)Approval workflow
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