AI Product Development
AI agents that do real work inside your business
An AI agent does more than chat. It reads, decides and takes actions in your tools. We build agents for well-defined jobs, with limits, logs and a person in the loop.
The problem
The problem this solves
Much of the work in a business follows a pattern: read something, look up related information, decide what to do, then update a system or send a message. A new lead arrives and someone researches the company and scores it. A support request comes in and someone checks the order and replies. An invoice lands and someone matches it to a purchase order.
These tasks are too varied for traditional automation, which needs every rule written in advance, yet too repetitive to be a good use of skilled people. That gap is where an AI agent fits. A large language model reads the input, uses tool calling to look things up and take actions, and follows instructions you control.
The risk is giving an agent too much freedom. Agents make mistakes, misread instructions and sometimes act confidently on wrong information. So we design agents narrowly: one clear job, a limited set of tools, permissions scoped to that job, confidence thresholds, and a human review step for anything that matters. Every action is logged so you can see what the agent did and why.
Connecting agents to your systems is increasingly done through the Model Context Protocol, which gives a standard, permissioned way for AI to use your CRM, inbox, files or database. We use it where it fits and build custom tool connections where it does not.
The best first agent is usually small and measurable: one inbox, one type of request, one clear success measure. Once it works and the team trusts it, the same patterns extend to more work. Our guide on how businesses use AI agents covers the options.
What you get
What you get
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A scoped agent job
One clearly defined task, with inputs, allowed actions and what success looks like.
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Tool connections
Access to your CRM, email, database or files through APIs or MCP servers, limited to what the job needs.
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Model selection
Candidate models tested on your examples, balancing accuracy, speed and cost.
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Human review steps
Approval queues for actions that affect customers, money or records.
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Guardrails
Limits on actions, spending and data access, with automatic escalation when unsure.
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Full activity log
Every input, decision and action recorded for review and auditing.
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Evaluation suite
A test set of real cases the agent must pass before and after every change.
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Performance dashboard
Volume, accuracy, escalations and cost per task, tracked over time.
How we build it
How we build it
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1
Pick the job
We choose one task with clear inputs, outputs and a measurable benefit.
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2
Collect examples
We gather real past cases and agree on the right outcome for each.
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3
Prototype and evaluate
A working agent tested against the examples, with models compared.
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4
Build for production
Tool connections, permissions, review queues and logging built properly.
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5
Supervised launch
The agent runs with every action reviewed at first, then more autonomy as trust grows.
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6
Improve
Mistakes become new test cases, and the agent is tuned regularly.
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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Drafting agent instructions and example outputs.
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Generating the tool connection code and tests.
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Creating evaluation cases, including tricky ones.
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Summarizing agent logs to find patterns in mistakes.
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Comparing models on cost and accuracy automatically.
Where people decide
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Which job the agent should do, and which it should never do.
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What the agent is allowed to access and change.
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Where a person must approve before an action happens.
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What accuracy is good enough to launch.
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When the agent earns more autonomy.
Is this right for you?
When this is the right choice
A good fit when
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A repetitive task involves reading, looking things up and updating systems.
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You have past examples that show what a good result looks like.
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Mistakes can be caught by a review step before they cause harm.
Consider something else when
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The task follows fixed rules with no judgment. Traditional automation is cheaper.
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Every decision is high stakes and must be made by an expert. Use AI to assist, not act.
Timeline and cost
What affects the timeline and cost
A prototype agent tested on your real examples usually takes a few weeks. A production agent with integrations, review queues and monitoring takes longer, depending on the systems involved. We plan it so you can stop after the prototype if results are not good enough.
We do not publish fixed prices because scope drives cost. How we estimate.
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Number of tools
Each system the agent uses needs a secure, tested connection.
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Accuracy required
Higher accuracy targets need more examples, testing and review design.
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Review workflow
Approval queues and escalation paths are part of the build.
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Usage volume
Model costs scale with the number of tasks; we estimate them up front.
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Data sensitivity
Sensitive data may require specific providers or self-hosted models.
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Monitoring needs
Dashboards and alerting for quality and cost add work.
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.
- MCP integration Connect AI assistants to your CRM, files, databases and tools through Model Context Protocol servers, with safe permissions.
- CRM integration Connect your CRM to forms, email, billing and support so customer records stay complete without typing.
- AI-Powered Web Development Business websites, customer portals, dashboards and web applications that load fast, rank well and are easy to change.
- AI copilots AI helpers built into your product or internal tools that draft, summarize and suggest while people decide.
- AI chatbots Website and messaging chatbots that answer common questions well and hand everything else to a person.
Solutions
View all solutions- AI lead qualification Score, research and route every new inquiry so your team talks to the best leads first.
- AI support agent An assistant that answers routine questions from your own content, checks orders and hands anything else to a person.
- Email triage Sort a shared inbox by topic and urgency, pull out the key details and draft replies for a person to send.
- AI sales research agent Short, sourced briefings on each prospect before a call, drafted by an AI agent from public information and your CRM.
- Internal help desk assistant An assistant in Slack or Teams that answers policy and how-to questions from your handbooks and opens tickets when needed.
- Scheduling automation Let customers book, change and confirm appointments themselves, by web, chat or phone, with reminders that cut no-shows.
Industries
View all industries- SaaS and startups MVPs, subscription billing, AI features, multi-tenant platforms and scaling support for founders and product teams.
- Real estate Listing sites, lead handling, CRM automation and document workflows for agencies, brokers and developers.
- Professional services Client portals, proposal drafting, knowledge assistants and internal tools for consultancies, agencies and firms.
- Vacation rentals Booking websites, owner and guest portals, turnover scheduling and guest messaging for rental operators.
- 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- What are AI agents and how do businesses use them? What AI agents are, real business uses, the controls they need and how to start with one safely.
- What is the Model Context Protocol? A business guide to MCP: what it is, how MCP servers work, who supports it and how to start using it.
- AI chatbot vs AI agent What separates a chatbot that answers from an agent that acts, and when you need each.
- 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 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.
AI models
View all ai models- Reasoning Models that think through multi-step problems before answering: analysis, planning, math, complex documents and agent work.
- Coding Models that write, review and explain code, and power coding assistants and developer tools.
- Claude Fable 5.1 The top tier of the Claude family, for the most demanding reasoning and long-horizon agent work, priced above Opus.
- 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.
- Gemini 3.1 Pro Google's most advanced Gemini model for reasoning, software engineering and agent work, reading text, images, audio, video and PDFs. Available as a preview.
- 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.
MCP servers
View all mcp servers- CRM and sales Servers that connect AI to CRM and support platforms, to look up customers, update records and prepare follow-ups.
- Communication Servers for chat, email, SMS and phone, so AI can read messages, draft replies and send notifications.
- Browser automation Servers that let an AI agent open web pages, click, fill forms, take screenshots and extract data from sites.
- Search and web Servers that give AI live web search, page fetching and research tools, so answers can use current information.
- Apify Apify's official MCP server lets AI find and run ready-made scrapers and automation tools, called Actors, and read their results.
- Brave Search Brave's official MCP server gives AI live web, news, image, video and local search from Brave's own independent index.
Glossary terms
View all glossary terms- AI agent An AI agent is software that uses a language model to plan steps, call tools and act toward a goal, instead of only answering one question.
- Tool calling Tool calling is when an AI model asks the application to run a tool, such as a search or database lookup, and then uses the result in its answer.
- Model Context Protocol The Model Context Protocol, or MCP, is an open standard for connecting AI assistants to external tools and data in a consistent way.
- 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.
- Function calling Function calling is a model feature that lets an AI model request a specific function, with structured inputs, for the application to run.
- MCP server An MCP server is a program or hosted service that exposes a system's data and actions as tools that AI assistants can use through the Model Context Protocol.
FAQ
Questions about AI agents
Have a question that is not here? Ask us directly.
A chatbot answers questions in a conversation. An agent takes actions: it looks things up, updates records and moves work forward, within limits you set. See AI chatbot vs AI agent.
Yes, which is why we design for it. Agents get narrow jobs, limited permissions, confidence thresholds and human review for important actions, and every action is logged.
Any system with an API or an MCP server, such as CRMs, email, calendars, databases, file storage and ticketing tools. Browse our MCP server directory for examples.
Something frequent, measurable and low risk, such as qualifying inbound leads or sorting a shared inbox.