AI Product Development
AI workflows: automation that can read, sort and draft
Some steps in a process need judgment: read this email, decide its category, draft a reply. We add AI to exactly those steps and keep the rest of the process predictable.
The problem
The problem this solves
Traditional automation is excellent at fixed rules: when a form is submitted, create a record and send an email. It struggles the moment a step needs understanding. Which department should this request go to? Is this review positive or negative? What is this document? Those steps stay manual, and the whole process waits for them.
Fully autonomous AI is the opposite extreme. It can handle ambiguity, but it is harder to predict and audit, which makes teams nervous about letting it touch real work.
An AI workflow sits in between. The process is defined step by step, like any workflow automation. Most steps are ordinary code. A few steps call an AI model for a specific, narrow job, such as classifying, extracting, summarizing or drafting, and return structured output the next step can rely on. For simple sorting, zero-shot classification often works without any training data.
Because each AI step is small and its output is checked, the workflow stays predictable. Low-confidence results go to a person. Every step is logged. You can see exactly where AI is used and replace a model without rebuilding the process.
We used this pattern in our deal aggregator case study, where a three-stage pipeline applied rules first, then a classifier model, then a large language model only as a fallback. That kept costs low and quality high across tens of thousands of products.
AI workflows are often the fastest way to see a return from AI, because they slot into processes you already run. Read AI automation vs traditional automation to see where each fits.
What you get
What you get
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Process design
The workflow mapped step by step, with the few AI steps clearly marked.
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AI classification
Incoming items sorted by type, priority, sentiment or department.
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AI extraction
Key fields pulled from emails, forms and documents into structured data.
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AI drafting
Replies, summaries and notes drafted for a person to approve.
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Tiered processing
Cheap rules first, AI only where rules cannot decide, to control cost.
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Review queues
Uncertain or high-impact items routed to the right person.
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Step-by-step logs
What each step received, decided and produced, for audits and debugging.
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Monitoring and alerts
Alerts on failures, unusual volumes or rising costs.
How we build it
How we build it
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1
Map the process
We document the current steps and find the ones that need judgment.
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2
Gather examples
Real past items with the correct decisions, for testing each AI step.
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3
Test AI steps
Each AI step tested on examples; rules used instead wherever they work.
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4
Build the workflow
The full workflow built with review queues, logging and error handling.
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5
Run and tune
Launched in stages, then tuned from corrections and logs.
AI and people
Where AI helps, where people decide
AI makes the repetitive parts faster. The decisions that shape your product stay with experienced people.
Where AI speeds things up
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Classifying, extracting and drafting inside the workflow.
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Suggesting rules that can replace AI steps to save cost.
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Generating test cases from historical data.
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Writing workflow code and integrations.
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Summarizing corrections to show where accuracy slips.
Where people decide
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Which steps need AI and which should stay rule-based.
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Confidence thresholds for automatic processing.
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Who reviews exceptions and how quickly.
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What the workflow must never do automatically.
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When a workflow is ready to run unattended.
Is this right for you?
When this is the right choice
A good fit when
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A process stalls on steps that need reading or judgment.
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You handle many similar items, such as emails, forms or records.
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You want AI benefits without handing full control to an agent.
Consider something else when
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Every step follows clear rules. Traditional automation is cheaper.
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Volume is low enough that manual handling costs little.
Timeline and cost
What affects the timeline and cost
A single workflow with one or two AI steps usually takes a few weeks to build and test. Larger workflows with many systems and approval paths are delivered in stages.
We do not publish fixed prices because scope drives cost. How we estimate.
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Workflow complexity
More steps, branches and exceptions add design and testing.
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Number of AI steps
Each AI step needs prompts, tests and thresholds.
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Systems involved
Each connected system adds integration work.
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Volume
Model costs scale with items processed; tiering keeps them down.
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Review design
Queues and escalation rules are part of the build.
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Data sensitivity
Sensitive data may limit which models can be used.
Keep exploring
Related services, solutions and reading
Related services
View all related services- AI Product Development AI agents, knowledge assistants, copilots and document automation built into the way your team already works.
- Automation and Integrations CRM, payment and API integrations, MCP connections and workflows that stop people copying data by hand.
- Internal tools Custom software for your own team: trackers, approval flows and back-office systems that replace spreadsheets.
- Business process automation Map a process that runs on email and spreadsheets and turn it into software with clear steps and owners.
- Admin panels The back-office screens your team uses to manage users, content, orders and settings without a developer.
- AI agents AI that takes actions in your systems, such as qualifying leads or processing requests, with people checking the results.
Solutions
View all solutions- Email triage Sort a shared inbox by topic and urgency, pull out the key details and draft replies for a person to send.
- AI automation and integrations Connect the tools you already use so data moves once, correctly, and AI reads the parts that arrive as text or documents.
- Feedback analysis Read every review, survey and ticket, group them by theme and sentiment, and show what customers keep asking for.
- Approval workflows Requests, approvals and reminders in one place, with clear owners and deadlines instead of long email threads.
- AI lead qualification Score, research and route every new inquiry so your team talks to the best leads first.
- Scheduling and drafting automation Turn one piece of content into posts for each channel, schedule them and track results, with approval before anything goes out.
Industries
View all industries- Professional services Client portals, proposal drafting, knowledge assistants and internal tools for consultancies, agencies and firms.
- Insurance Claims intake, document and photo processing, policy knowledge assistants and customer portals for brokers and insurers.
- Recruiting and staffing Candidate screening assistants, ATS integrations, interview notes and onboarding automation for recruiters and staffing firms.
- Property management Tenant portals, maintenance tracking, owner statements and inbox automation for property managers.
- Tax and compliance services Client portals, document checklists, case queues and deadline tracking for tax preparers and compliance firms.
- Ecommerce Online stores, product data cleanup, support assistants, price monitoring and order automation for online retailers.
Case studies
View all case studies- Fashion deal aggregator Sale items from more than thirty brands are collected every night and classified consistently by a three-stage pipeline.
- Tax filing service platform Clients upload documents and see where their case stands, and staff work from one queue with deadlines and reminders.
- Facility management platform Every request, inspection and cost for every building now lives in one place, with a clear owner and status.
- Payroll system for an IT services company Payroll is calculated from recorded inputs, reviewed once and sent as payslips in a single batch, with every change logged.
- Beach rental booking system A long-running booking system kept earning while it was extended, tested and given a safe, gradual path to a modern stack.
- Goal and effort tracking app A product design built around handwritten daily proof, locked plans and honest self-scoring. The build is in progress.
Guides and articles
View all guides and articles- What can AI do for a small business? Practical AI uses for small businesses, from inboxes and documents to customer support, with costs and first steps.
- How to measure the ROI of AI automation Measure the return on AI automation with a baseline, the right metrics and honest accounting of costs.
- AI automation vs traditional automation How rule-based automation and AI automation differ, where each fits and how to combine them.
- How to build an AI workflow with MCP servers Plan, connect and test an AI workflow that uses MCP servers, from a single task to a reliable process.
- No-code vs custom development Where no-code tools shine, where they hit limits, and when custom software pays off.
- How to automate payroll Turn a manual monthly payroll into a reviewed, tested process with accurate payslips and a full audit trail.
AI models
View all ai models- General purpose All-round language models for writing, summarizing, support, extraction and most everyday business tasks.
- Speech Speech-to-text and text-to-speech models for transcription, call analysis and voice assistants.
- Image and video Models that generate or edit images and video from text and reference images.
- Claude Haiku 4.5 Anthropic's fastest and lowest-cost Claude model, with near-frontier intelligence for high-volume and real-time work.
- Gemini 3.5 Flash-Lite Google's lowest-cost current Gemini model for high-throughput work such as sub-agent tasks and document parsing.
- Nano Banana (Gemini image) Google's Gemini-native image generation and editing models, known as Nano Banana 2 and Nano Banana Pro, which replace Imagen in the Gemini API.
MCP servers
View all mcp servers- Automation Servers that connect AI to automation platforms and analytics, so it can trigger workflows and read results.
- Communication Servers for chat, email, SMS and phone, so AI can read messages, draft replies and send notifications.
- Productivity Servers for notes, tasks, projects, calendars, spreadsheets and design tools used across the business.
- Airtable Airtable's official hosted MCP server lets AI list workspaces, create bases and read records, with wider read and write access to tables and automations.
- Asana Asana's official MCP server lets AI search work, read and create tasks and projects, add comments and post project status updates.
- Canva Canva's official MCP server lets AI search, create, resize, export and comment on designs, upload assets and use brand kits.
Glossary terms
View all glossary terms- Workflow automation Workflow automation uses software to move work through a series of steps, such as routing, approvals and notifications, based on rules and events.
- Structured output Structured output is when an AI model returns its answer in a fixed format, such as JSON matching a schema, so software can use it reliably.
- Zero-shot classification Zero-shot classification is sorting text or images into categories an AI model was not specifically trained on, using only the category names or descriptions.
- Artificial intelligence Artificial intelligence is the broad field of building software that performs tasks that normally need human judgment, such as understanding language or images.
- Automation Automation is using software to perform a task or process with little or no human effort each time it runs.
- Natural language processing Natural language processing is the field of AI that deals with understanding and generating human language, in text or speech.
FAQ
Questions about AI workflows
Have a question that is not here? Ask us directly.
In an AI workflow, the steps are fixed and AI handles specific ones. An agent decides its own steps within limits. Workflows are more predictable and easier to audit. See AI agents.
Yes, for simpler cases those tools work well and we use them. For high volume, complex logic or sensitive data, custom code gives more control. See business process automation.
By using rules first, smaller models for simple steps, and large models only when needed, plus caching repeated work. We track cost per item from the start.
The item goes to a review queue with the AI suggestion attached, and a person decides. Those decisions become new test cases.