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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. This guide is for operations leaders at companies of roughly 20 to 200 people, mainly in the US and Canada: 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 Azure: architecture, security, cost and operationsRunning 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. This guide is for IT and operations leaders at companies of roughly 20 to 200 people.Read Build, buy or integrate: deciding on custom business softwareBuy software for standard processes, integrate when your existing systems already hold the data, and build only where the process sets you apart. Companies of 20 to 200 people usually end up with 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 52 on AI Integration & AutomationUsing Claude and ChatGPT for security audits and red teaming: what worksClaude and ChatGPT are useful first-pass reviewers of your own code, configurations and AI features, and they are weak as a final authority. Use them to find common flaws, explain findings, draft threat models and triage scanner output, keep Semgrep or CodeQL running beside them, and have a person confirm every finding. For regulated systems, an AI pass does not replace an independent penetration test.Read The history of large language models: a dated timeline and what it means for buyersLarge language models grew out of statistical word prediction, gained their modern shape with the transformer in 2017, and reached the public with ChatGPT in November 2022. The history teaches four things to a business buyer: capability per dollar changes fast, model rankings keep reshuffling, training method matters as much as size, and no single vendor should be a permanent dependency. This guide is for leaders at companies of roughly 20 to 200 people.Read How do LLMs work? A plain-English guide for people who buy and run themA large language model (LLM) reads text as tokens, turns them into numbers, and uses attention to predict the next token, over and over, until a reply is complete. It generates likely text instead of looking facts up, which is why you test it against your own work, ground it in your documents, and keep a person approving consequential actions. This guide is for operations and IT leaders at companies of roughly 20 to 200 people.Read
AI & Cloud Infrastructure
The cloud, data and model foundations an AI workload needs to run in production.
All 19 on AI & Cloud InfrastructureThe encryption techniques that matter, and what the industry uses by defaultThe types of encryption that matter in practice are AES-256-GCM for stored data, TLS 1.3 for traffic, envelope encryption with a cloud key service for data at rest, Argon2id hashing for passwords, and hybrid post-quantum key exchange, which browsers already use. This guide is for operations, IT and finance leaders who need to know the current default for each job and the questions to put to a vendor. Never invent your own cryptography.Read How to build a system that handles hundreds of millions of data pointsA high volume data pipeline works in stages: a durable ingestion buffer, stream processing, raw data kept cheaply in object storage as the source of truth, and pre-aggregated rollups that answer the questions people actually ask. The design choice that matters most is deciding those questions first, because they set what you aggregate and how long you keep detail. This guide is for operations and IT leaders at companies of roughly 20 to 200 people who collect sensor, image or event data at volume.Read How Meta handles billions of requests, and what your company should copyMeta (formerly Facebook) serves its social graph through layers: load balancers, a very large cache, and a sharded MySQL tier behind it. A company of 20 to 200 people should copy the order of operations, which is to measure, index, cache, add read replicas, partition, and shard last, because most never need sharding. This guide explains each technique and when it starts paying off.Read
Custom Solutions
When to build, buy or integrate, and how to get internal tools and portals right.
All 18 on Custom SolutionsRust vs TypeScript: when to use eachTypeScript is the right default for web applications, APIs and business software because of its hiring pool, speed of change and ecosystem. Rust earns its higher cost in CPU-heavy paths, latency-critical services, embedded and edge work, and anywhere memory safety and predictable performance matter. The two are often combined, and this guide is for owners and technical leads at companies of 20 to 200 people deciding what to build in.Read Sentry vs PostHog: which one does your team need?Sentry is built for developers who find and fix errors and performance problems, and PostHog is built for product teams who need to understand user behavior through analytics, session replay, feature flags and experiments. Each has moved into the other's territory: PostHog now offers error tracking and Sentry offers session replay. This guide is for technical leads and product owners choosing monitoring for a web application, and for small teams deciding whether one tool is enough.Read SQL vs NoSQL: which database should a business application start with?Most business applications should start on a relational (SQL) database, usually PostgreSQL or MySQL, and add a NoSQL system only for a specific workload that needs it. Two old beliefs no longer hold: NoSQL products now offer transactions, and relational databases scale much further than their reputation suggests. This guide is for owners and operations leaders at companies of roughly 20 to 200 people who are approving a database choice.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.
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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 automationApache KafkaAPI (application programming interface)
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