Articles, guides and resources on AI workflow orchestration, written by the voolama team from first-hand work building Dave.
The HelloDave.ai blog covers how to build AI workflows that keep humans in control: governed AI, human-in-the-loop review design, model-agnostic AI, and going from a blank canvas to a live workflow in under 20 minutes. Written by the voolama team from first-hand work building the platform.
DAVE logs every agent action, routes decisions through configurable human-review steps, and stores the full record in a per-tenant audit log built for SOC 2 and GDPR compliance.…
Read →The four metrics that tell you whether your AI workflows are working, and how to read them in DAVE before the first month is out.
Read →AI workflow orchestration is the structured coordination of AI models, human reviewers, external APIs, and routing logic inside a single governed platform. This article explains…
Read →Orchestration, automation, and RPA are three distinct approaches to removing manual work from business processes. This article draws a precise line between them and gives…
Read →Agent frameworks help you build autonomous AI agents. Orchestration layers help you govern them, connect them to humans and external systems, and audit what they do. This article…
Read →Zapier, Make, and similar tools are excellent at connecting apps and moving data between them. AI workflow orchestration is a different architectural layer, designed for processes…
Read →An agentic workflow is a governed, multi-step process in which one or more AI agents act as participants alongside humans, external APIs, and routing logic, all within a platform…
Read →Running AI in operations is not just a technology decision: it is a people and permissions decision. DAVE's six default roles map directly onto the distinct jobs that keep an AI…
Read →Every AI agent needs a key to call an AI service. How that key is stored, masked, transmitted, and protected from retrieval determines whether your AI infrastructure is genuinely…
Read →SOC 2 readiness for AI workflow software comes down to a specific set of controls: immutable audit logging, compliance-tagged events, read-access capture, and configurable…
Read →Non-technical people can build production AI workflows in DAVE. The Workflow Editor is a visual drag-and-drop canvas that requires no code, and the Curate role gives process…
Read →Most AI pilots do not fail because the technology does not work. They stall because the team hits a structural gap between building a demo and running a process. Understanding…
Read →DAVE's Audit Log gives you a searchable, immutable, exportable record of every significant action in your workspace, including every agent execution, workflow event, and…
Read →Human-in-the-loop AI is not a safety disclaimer: it is a production architecture. This article explains what it means in practice, why it matters, and how DAVE implements it…
Read →A technical and operational explanation of how DAVE isolates tenants from each other: per-tenant encryption, row-level data separation, scoped authentication tokens, and the admin…
Read →Yes, and DAVE is built for it. Every AI provider you connect uses your own API key, your own account, and your own usage quota. DAVE never proxies your AI calls or marks up your…
Read →GDPR mode in DAVE is not a label: it activates specific, verifiable controls. This article explains exactly what changes when you enable it, from read-access logging and…
Read →Agencies running AI workflows for multiple clients need hard isolation between client environments, consistent security controls, and a single platform to manage everything.…
Read →AI workflow automation has two cost components: the platform and the models. This article explains both, shows how DAVE's pricing is structured, and gives you a framework for…
Read →Back-office work is repetitive, rule-bound, and high-stakes: exactly the kind of work that benefits most from structured AI automation. This article shows how to design reliable,…
Read →Fully autonomous AI processes feel efficient until something goes wrong, and then the cost arrives all at once. This article examines what actually breaks without human review…
Read →Structured AI research workflows beat ad-hoc prompting every time. This article explains how to design a repeatable, auditable research and synthesis pipeline using versioned…
Read →Automating an approval process with AI does not mean removing humans from the loop. It means giving humans the right information at the right moment, with a clear decision to make…
Read →Content teams use AI workflows to turn repeatable content processes into structured, auditable pipelines that combine AI drafting, human review, and routing logic. DAVE makes…
Read →An AI audit log that only records authentication events is not an audit log: it is a login history. This article defines what a complete AI audit trail must capture, field by…
Read →Every DAVE workflow is assembled from exactly ten node types. Understanding what each one does, when to use it, and what it cannot do is the fastest way to design workflows that…
Read →Ownership of AI workflows is not a single job title: it is a set of distinct responsibilities that map to different people across your organisation. Getting the ownership model…
Read →Reviewing AI output at scale requires a structured system, not faster reading. DAVE's Task Inbox turns every human review step into a discrete, trackable work item with a clear…
Read →Yes, you can use AI in a regulated review process, but only if the AI operates inside a structure that produces an immutable, auditable record of every decision. This article…
Read →Branching, looping, and stopping are the three structural decisions that separate a useful AI workflow from a fragile linear script. This article explains when to use each pattern…
Read →AI model lock-in happens when your workflows, credentials, and tooling are so tightly coupled to one provider that switching costs more than staying. This article explains where…
Read →Per-seat and per-usage pricing for AI tools are not equivalent choices. One scales with your team size, the other with your AI consumption. This article explains the structural…
Read →Most AI platforms sit between you and the model and charge a percentage on every token. This article explains how that markup compounds, what it means for teams running real…
Read →Yes, you can run a local model in production workflows. This article explains what that means precisely, what it does not mean, how DAVE connects to a local Ollama instance…
Read →When a reviewer clicks Reject in DAVE's Task Inbox, the comment they leave is the only signal the workflow has about what went wrong. Here is how to write reject reasons that…
Read →Accountability for AI agents in a business process is not a policy document: it is an engineering decision. This article explains the four mechanisms DAVE provides to make every…
Read →A practical framework for quantifying the value of AI workflow automation and presenting it to the people who control the budget. Covers the metrics that matter, the costs that…
Read →A practical guide to running a low-cost AI workflow pilot: how to scope it, what to build first, how to use a 30-day free trial to generate real evidence, and how to present the…
Read →Putting the approval gate in the wrong place wastes reviewer time, lets errors through, or stalls the pipeline entirely. This article gives you a principled framework for deciding…
Read →Prompt injection is the most common way a well-designed AI workflow gets subverted. This article explains what it is, why it matters in a multi-step workflow context, and the…
Read →Letting an AI agent approve its own output is a governance gap, not a feature. DAVE's Agent Review and Human Review nodes give you a clear architecture for AI-assisted quality…
Read →SaaS AI orchestration platforms trade convenience for control — and the bill arrives later than you expect. Here's the practical case for self-hosting your AI workflows in 2026,…
Read →Learn Dave, at /learn-dave, is the walkthrough surface. It carries a nine-step run through your first workflow, from adding an AI provider to confirming a completed run, and every one of those steps links to a task-level quick reference guide. Ten of those guides exist, covering adding a provider, creating an agent, creating and activating a workflow, launching an instance, building a workflow in the editor, working a task from your inbox, reading a run's timeline, scheduling a recurring run, setting up a custom field, and connecting an AI assistant over MCP. The blog is where the thinking goes; Learn Dave is where the clicks go.
The voolama team, including founders Dean Brown and Adam Valine, writes from first-hand experience building the Dave platform.
Governed AI, human-in-the-loop design, model-agnostic AI, workflow building, and practical how-to guidance for AI workflow orchestration.
Yes. The posts explain how the platform actually works, node types, security, and deployment, so you can judge fit before starting a free trial.
Three kinds, in one feed: articles, guides and resources. Every card is tagged with its type, the feed can be filtered to one type at a time, and each post has its own page at /article, /guide or /resource.
In the product's own help centre rather than on this blog. Every Dave release ships plain-language release notes inside the app, alongside a guide for each area of the product.
On Learn Dave at /learn-dave: a nine-step walkthrough of your first workflow end to end, with every step linked to one of ten short task-level guides for the jobs people do most often.
