AI Product Development
AI copilots that draft, summarize and suggest while people decide
A copilot sits inside the tools people already use and does the first draft of routine work. People review, edit and decide. That keeps quality high and adoption easy.
The problem
The problem this solves
Knowledge workers spend a lot of time on first drafts: the reply to a customer, the summary of a long case, the proposal based on last month's one, the notes after a meeting. The work is not hard, but it is slow, and it pulls attention from the decisions that need expertise.
Generic AI chat tools can help, but people have to copy information in and out, context is lost, and sensitive data ends up in tools the company does not control. Adoption stays patchy because the AI is always one more tab away.
An AI copilot brings AI into the place where the work already happens. It sees the record, the case or the document on screen, drafts the next piece of work, and waits for a person to accept, edit or reject it. Because a person always reviews the output, copilots are one of the safest and fastest ways to get real value from AI.
Good copilots are specific. "Summarize this customer's history before a call" or "draft a quote from this request" beats a blank chat box. We find the two or three tasks where a first draft saves the most time, design the prompts and context carefully, and measure how often people accept the suggestions.
We can build copilots into software you sell, as a product feature, or into internal tools your team uses. Either way, costs are tracked per use and data stays inside the systems you control.
What you get
What you get
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Task selection
The few tasks where a first draft saves the most time, chosen with the people who do them.
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In-context interface
Suggestions shown right where the work happens, with one-click accept, edit or discard.
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Context from your data
The copilot uses the current record, history and relevant documents, within the user's permissions.
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Prompt and output design
Instructions and formats that produce consistent, on-brand drafts.
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Feedback loop
Accept and edit rates recorded, so suggestions improve over time.
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Cost controls
Usage limits, model choice per task and cost per suggestion tracked.
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Data safeguards
Sensitive fields excluded or masked, and providers chosen to match your data rules.
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Adoption reporting
Which features people use, and how much time they likely save.
How we build it
How we build it
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1
Find the drafts
We watch real work and list the drafting tasks that take the most time.
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2
Design the experience
Where suggestions appear and how people accept or change them.
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3
Prototype and test
Suggestions generated for real past cases and reviewed by your team.
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4
Build
Integration into your product or tools, with logging and cost tracking.
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5
Launch and measure
Rollout to a group first, then improve based on acceptance rates.
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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Generating first drafts and summaries in the product itself.
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Testing many prompt versions against real past cases.
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Writing the integration code and interface components.
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Analyzing which suggestions people accept or rewrite.
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Drafting help text that explains the copilot to users.
Where people decide
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Which tasks deserve a copilot.
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What the copilot may see and what stays hidden.
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The tone and standards drafts must meet.
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Every final output, since people accept or edit each suggestion.
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When a copilot feature is ready for all users.
Is this right for you?
When this is the right choice
A good fit when
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Your team writes similar drafts, summaries or replies every day.
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The information needed is already in your systems.
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A person can quickly check and correct the output.
Consider something else when
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The task needs no human judgment at all. An automated workflow may fit better.
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The data needed lives outside any system the copilot can reach.
Timeline and cost
What affects the timeline and cost
A first copilot feature for one task usually takes a few weeks from prototype to pilot. Each further task is faster, because the context, logging and interface are already in place.
We do not publish fixed prices because scope drives cost. How we estimate.
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Number of tasks
Each task needs its own prompts, tests and interface.
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Context gathering
Pulling the right data from several systems adds work.
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Host product
Adding to a modern product is quicker than to an old one.
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Data rules
Sensitive data needs masking, provider checks and logging.
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Usage volume
Model costs depend on how often suggestions are generated.
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Rollout and training
Larger teams need staged rollouts and short training.
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.
- LLM integration Add a large language model to software you already have, with the guardrails, costs and logging handled.
- AI agents AI that takes actions in your systems, such as qualifying leads or processing requests, with people checking the results.
- Landing pages Single-purpose pages for a campaign, a launch or a product, built to load fast and test quickly.
- WordPress development Custom WordPress themes, plugins and cleanups for teams who want to keep editing content themselves.
- AI workflows Step-by-step automations where AI handles the reading, sorting and drafting inside a process you control.
Solutions
View all solutions- Automated quoting Draft accurate quotes and proposals from a request, your price rules and past work, for a person to approve.
- AI-assisted content workflows Briefs, first drafts, edits and repurposing in a workflow where people set the angle and approve every word.
- Meeting notes automation Transcribe meetings and calls, summarize decisions and push action items into your CRM or project tool.
- Contract review assistant Highlight unusual clauses, missing terms and deviations from your standard positions, so reviewers focus where it matters.
- 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- SaaS and startups MVPs, subscription billing, AI features, multi-tenant platforms and scaling support for founders and product teams.
- Marketing agencies Landing pages, content workflows, client reporting and white-label development capacity for marketing agencies.
- Professional services Client portals, proposal drafting, knowledge assistants and internal tools for consultancies, agencies and firms.
- Legal Contract review assistants, knowledge search, intake and document automation for law firms and in-house legal teams.
- Education and elearning Course platforms, student portals, content workflows and AI tutoring assistants for schools and training companies.
Case studies
View all case studiesGuides and articles
View all guides and articles- Where AI helps in development, and where it does not An honest map of the development tasks AI speeds up and the ones that still need experienced people.
- How we review AI-generated code The checks we apply to every AI-assisted change, from tests and security to readability and business rules.
- AI development security checklist A practical checklist for keeping code, data and credentials safe when a team builds with AI tools.
- Prompt writing basics for business teams How to write prompts that get consistent, useful results from AI tools, with templates your team can reuse.
AI models
View all ai models- General purpose All-round language models for writing, summarizing, support, extraction and most everyday business tasks.
- Coding Models that write, review and explain code, and power coding assistants and developer tools.
- Claude Opus 5.5 Anthropic's recommended starting point for most serious work: long-running agentic coding and knowledge work, with a 1M token context window.
- Mistral Medium 3.5 A newer open-weight Mistral model for agent and coding work, with image input, tool calling, structured outputs and a 256K window.
- Codestral 25.08 Mistral's low-latency coding model for code completion and generation, with fill-in-the-middle support, tool calling and a 128K window.
- GPT-6 Sol The middle model of the GPT-6 family, positioned for complex coding and agentic workflows at a mid price level.
MCP servers
View all mcp servers- Developer tools Servers for code hosting, version control, files, containers, error tracking and documentation lookup.
- Context7 Upstash's Context7 MCP server gives coding assistants current, version-specific documentation for thousands of libraries.
- Docker A community MCP server that lets AI list, run, stop and remove Docker containers and manage images, networks and volumes.
- Everything The MCP project's Everything server is a test server that shows every protocol feature. For building and testing MCP clients only.
- Figma Figma's official MCP server gives AI design context, screenshots, variables and assets, and can now write to the canvas.
- Filesystem The MCP project's Filesystem reference server lets AI read, write, edit, move and search files, only inside folders you allow.
Glossary terms
View all glossary terms- AI copilot An AI copilot is an assistant built into a tool people already use, which suggests, drafts and explains while the person stays in control.
- Large language model A large language model, or LLM, is an AI model trained on vast amounts of text that can understand and generate language, and often images and code.
- Prompt A prompt is the instruction and context you give an AI model to get the output you want.
- Claude Code Claude Code is Anthropic's agentic coding tool, which works in a developer's terminal, IDE, desktop app or browser to read code, run commands and make changes.
- Prompt engineering Prompt engineering is the practice of designing, testing and improving prompts so AI models produce reliable results for a specific task.
From the blog
View all from the blog- Why we build with AI, and what we still do by hand How our studio uses AI in every stage of software development, the work it does well, the decisions we keep with people and why that split protects clients.
- What a week of AI-accelerated development actually looks like A day-by-day look at a typical week of AI-accelerated development: planning, drafting with AI, review, testing, client demos and the parts that still take time.
- Our review process for AI-generated code How we review AI-generated code before it ships: small changes, automated checks, a person reading for logic and security, rule tests and client sign-off.
FAQ
Questions about AI copilots
Have a question that is not here? Ask us directly.
An AI helper built into software you already use. It drafts, summarizes or suggests based on what is on screen, and a person decides what to keep. See our AI copilot glossary entry.
A copilot suggests and a person acts. An agent acts on its own within limits. Copilots are a lower-risk starting point. See AI agents.
Yes. Copilot features are a common way to add AI value to a product. We handle model choice, costs and data safeguards. See LLM integration.
We track how often suggestions are accepted, edited or discarded, and how long tasks take before and after. That shows real value, not just usage.