AI systems and agents built into real software
AI systems development means building software where a language model does part of the work, such as answering from your documents, drafting, classifying or carrying out multi-step tasks with your tools. StackEndBox builds these for companies and founders who want AI features that run in production, with sensible limits, rather than a demo.
In practice that covers AI automation of defined workflows, AI agents that use your tools and data, and AI features inside a product. If an agent is what you are after, our page on AI agent development goes into that in detail. We treat AI as part of the system, not something bolted on. That means retrieval from the right sources, tool use with clear boundaries, and human approval where a mistake would matter.
How do you decide whether an AI pilot is ready to expand?
Agree the decision before running the pilot. For an internal knowledge assistant, the question is whether authorised staff can find supported answers with an acceptable review burden. The example below is a proposed evaluation design. It contains no claimed customer accuracy, savings or independently verified benchmark result.
| Decision | Scope to agree | Evidence to request |
|---|---|---|
| Baseline and dataset | Agree permitted documents and a held-out set of representative and difficult questions. Compare with the current search or manual method. | Keep development examples separate from acceptance examples. Report the number tested, omitted and unanswerable, not an accuracy percentage without a denominator. |
| Answer support | Require an answer to identify a source that supports its claim and an abstention when evidence is missing. | Review source correctness and answer correctness separately. Include stale, conflicting and absent material. A plausible citation is not proof of a supported answer. |
| Access and actions | Define which sources each role can use and keep consequential actions under explicit approval. | Test a source the user cannot access and an instruction embedded in retrieved content. Neither should grant broader access or authorise an action. |
| Operating decision | Agree acceptable latency, provider-cost assumptions, escalation and a stop condition before expansion. | Review failed and human-reviewed cases alongside successful ones. Record remaining uncertainty and monitoring responsibility before adding more users or autonomous tools. |
A synthetic evaluation is a method demonstration. Production readiness requires an authorised dataset and real acceptance evidence. Avoid collecting raw prompts and personal source text by default. Agree redaction, retention and restricted diagnostic access for any necessary logging.
Generate and download a local project-scope starter. Use it to prepare a non-confidential brief, then discuss the specific milestone with us. The tool sends no project information.
Relevant decision guides and references
Who this is for
- Teams adding AI features to a product they already run.
- Operations and support teams that want AI to handle defined, repeatable work with approvals where needed.
- Founders building an AI-native product that needs retrieval, tools and reliable behaviour in production.
When this is the right fit
- The work is repetitive and rule-shaped, but with enough variation that plain scripts do not cope.
- Your answers live in documents, tickets or databases that people currently search by hand.
- A wrong output is recoverable, or a person can review it before it takes effect.
- You can describe what a good result looks like, even roughly, so it can be tested.
What we build
Retrieval over your data
Systems that answer from your documents and records, so responses are grounded in what your business actually knows.
Agents that use tools
Agents that call your APIs and internal tools to complete tasks, with limits on what they may do on their own.
Human approval steps
Review and approval stages for actions that should not run unattended.
AI inside your product
Features such as summarisation, classification and assistance, integrated into the interface people already use.
Workflow automation with AI
Processes that read, decide and act across your tools, ending in a record of what happened.
What you get
- A working AI feature or agent deployed alongside your product or tools.
- A set of test cases built from your real examples, used to check behaviour before and after changes.
- Restricted diagnostic records with agreed redaction and retention, so failures can be reviewed without collecting unnecessary personal data.
- Approval and permission rules written down and enforced in the system.
- Documentation of which model provider and data sources are used, and what is sent to them.
What AI can and cannot be trusted to do
AI outputs can be wrong, incomplete or confidently mistaken. Every system we build needs evaluation against real examples before launch, human approval for actions that carry risk, and monitoring afterwards, because behaviour can change as inputs, data and models change.
We cannot promise a particular accuracy rate or that an agent will never make a mistake. We design so that mistakes are visible, limited and reversible wherever possible.
Build, buy or hybrid?
The main choices are how much to automate and where the model runs.
Off-the-shelf AI tools
A chat assistant, a helpdesk add-on or a workflow tool may already do what you need. Start here if your process is standard and your data does not need special handling.
No-code and low-code automation
For simple automations, such as moving a form submission into a spreadsheet and sending a notification, tools like n8n, Make or Zapier can be the right answer, and we will say so. Custom work earns its place when volume, reliability, data handling or AI steps that need approval outgrow them.
Managed model APIs
Calling a model provider's API from your own software is the usual route for custom behaviour. You get strong models without running infrastructure, but your prompts and data go to the provider, so their terms matter.
Self-hosted models
Worth considering when data must stay inside your environment or volumes make API costs a concern. It adds operational work and often means less capable models, so it should be a deliberate choice.
When not to hire us. We are probably not the right fit if you want AI to make high-stakes decisions with no human review, if you need a guaranteed accuracy figure, or if an existing tool already covers the task and you only need to configure it.
What affects the cost of AI automation and agents
We do not publish prices, because cost follows scope and risk. These are the drivers that move it most.
Scope of the workflow
How many steps, tools and systems the automation touches. One well-defined task is far smaller than a process that crosses five tools.
State of your data
Documents and records that are scattered, duplicated or out of date need preparation before an AI system can use them well.
Risk and approvals
Actions that need permissions, human approval steps and audit logging take more design and testing than read-only assistance.
Evaluation
Building test cases from your real examples, and re-running them as things change, is part of the work and not optional.
Model and hosting choices
Managed model APIs charge by usage, paid to the provider. Self-hosted models move the cost into infrastructure and upkeep.
Volume
Provider usage charges grow with the number of requests, so expected volume affects running cost as well as build cost.
Ongoing monitoring
Behaviour can drift as inputs, data and models change, so monitoring and adjustment continue after launch.
How we work
We begin with the workflow and outcome, then design how the model, your data and your tools fit together. We test critical flows, permissions and edge cases against real examples, ship to production and keep monitoring and improving.
What we need from you to start
- The workflow in plain words: what comes in, what a person does today and what the result should be.
- Sample inputs and good outputs, including awkward cases, so we can test against reality.
- Access to, or a description of, the documents, databases and tools the system should use.
- Your rules about which data may be sent to an outside model provider.
- Someone who can review outputs during development and decide where approval is required.
Technology
- TypeScript
- Python
- Node.js
- PostgreSQL
- Supabase
- Anthropic
Frequently asked questions
- What do AI automation agencies do?
- Practice differs between providers. StackEndBox is a product engineering company, and for us AI automation means taking a defined, repeated piece of work, such as triaging requests, extracting data from documents or drafting replies, and building it into a system that does the steps, uses your tools and sends risky actions for approval.
- What is an AI agent?
- An AI agent is software in which a language model chooses the next step and calls tools, such as your APIs or database, to complete a task. Agents need limits on what they may do on their own and review for anything risky.
- What is the difference between AI automation and workflow automation?
- Workflow automation moves data and triggers steps by fixed rules. AI automation adds a model for the steps that need reading, classifying or drafting. Many jobs need only the first, which is simpler and easier to test, and we will say when that is the case.
- Will no-code tools such as n8n, Make or Zapier do?
- For simple, low-volume automations they often will, and they are quicker to set up. Custom development makes sense when volume, reliability, data handling or AI steps with approvals outgrow them.
- How much does AI agent development cost?
- It depends on scope and risk, so we do not quote without them. The drivers are the number of steps and tools, the state of your data, approval and permission needs, evaluation, model choice and volume, listed under “What affects the cost of AI automation and agents”.
- Can AI be added to software we already have?
- Often yes. We look at where AI helps your existing product and connect it through APIs and your data.
- Will the AI act without supervision?
- Only where you are comfortable with that. We design approval steps for actions that carry risk, and limit what an agent is allowed to do.
- Where does the AI get its information?
- From the sources we connect, such as your documents, database and tools, so answers can be traced back to where they came from.
- How do you check that the AI is accurate?
- We test against examples from your own work and review failures before launch, then monitor in production. We cannot guarantee zero errors, which is why risky actions get human approval.
- What happens to our data?
- It depends on the design. If a managed model provider is used, the relevant data is sent to them under their terms, and we document exactly what is sent. Sensitive data can be excluded, redacted or kept in your environment where needed.
- Who owns what you build?
- Ownership of the code and deliverables is agreed in the project scope before work begins. Model providers and open-source components remain under their own terms.
- Do you only do prototypes?
- No. We build AI systems to run in production, including permissions, error handling and monitoring. A prototype can be a useful first step to test an idea cheaply.
- How do we start?
- Book a call or email us a description of the workflow. We reply with questions about data, risk and what good output looks like, then propose a first scoped piece of work.
Related services
Decision guides
Tell us what needs to work
Tell us what you want from ai systems & agents and we will reply with the questions that shape scope. Book a call, email us, or use the contact form on our homepage.
Meetings and delivery are online. We welcome projects from the United States, United Kingdom, Dubai and the wider UAE, and other international markets.
Email opens an editable draft in your mail app. Nothing is sent until you send it. You can also write directly to hello@stackendbox.com.