AI features and agents, built into the tools you already use
Our AI integration services add language models to your product and your daily operations, and build agents that handle routine tasks within clear limits, with a person in the loop where it counts.
// projects from $2,500, one fixed quote before we start
- your team spends hours on routine work like sorting tickets, reading forms or drafting replies
- you tried a chatbot or a demo and it wasn't reliable enough to put in front of customers
- you want an AI feature in your product that works with your real data
- the AI needs to reach your own systems, such as a helpdesk, customer database or order system
- you want a plain answer on where AI helps your business and where it doesn't
- A written use case: what the AI does alone, what needs a person's approval and what it never does
- The feature or agent, connected to your systems and running in your own accounts
- A test set of real examples from your business, used to measure it before launch and after every change
- Hard limits in code, such as spending caps, approval steps and a hand-off to a person for cases outside its rules
- A log of every action it takes, with the reason, so any decision can be reviewed
- Usage and cost monitoring for the model, with alerts
- Documentation and a handover session for your team
Where AI projects stall
Most business owners have seen an AI demo that looked impressive in a meeting, and that’s often what starts the search for AI integration services. Getting from that demo to something you’d let near a customer is harder. The model answers well most of the time, then gets one case badly wrong. It can’t see the information it needs, because that lives in your CRM (your customer database), your helpdesk or a spreadsheet. Nobody can say what it did yesterday, or why. The monthly bill grows with every new user. So the project stalls, or it launches and your team quietly stops trusting it.
Good integration work closes that gap. The feature has to work inside your real systems, on your real data, with rules about what it may do on its own and a record of what it did.
What we build
The engine behind most of this work is a language model: the kind of AI behind ChatGPT, which reads and writes text. On its own it only produces words. Connected to your systems, it can look things up and take actions.
- AI features in your product. Search that understands questions, summaries of long records, drafted replies, tagging and sorting of incoming items, and data pulled out of forms, emails and PDFs.
- Agents for routine operations. An agent is software that uses a language model to read a situation, choose the next step and carry it out in your systems. Typical jobs include sorting support tickets and drafting the reply, checking invoices against orders, routing inquiries to the right person and updating records after a call.
- Assistants for staff or customers. Chat or email assistants connected to your helpdesk, CRM or order system, so they look up the facts instead of guessing them. If the answers must come from your own documents, with sources, see our page on answers from your documents.
- Connections and controls. The parts that make the rest safe: links to your systems’ APIs (the interfaces software uses to talk to other software), permissions, logs, spending caps and alerts.
This is custom AI development: built around your process and your data, on a model chosen for the task. That might come from OpenAI, Anthropic or Google, or be an open model you host yourself.
How we keep agents in check
The question to put to any AI agent development company is what happens when the agent gets something wrong. Here is how we answer it.
Limits live in code. Every agent gets a written list of what it may do alone, what needs approval and what it never does. Those limits are enforced by the software around the model, not left to the instructions the model reads. A refund above a set amount goes to a person. A message to a customer can wait for review until you trust the agent with it.
Tested on your own cases. Before launch we gather real examples from your business (tickets, emails, documents) and compare the agent’s results with what your team would have done. It goes live when it meets the bar you agreed, and the same tests run again after every change to its instructions, its data or the model.
Every action is logged. Each step the agent takes is recorded with what it saw and why it acted, so you can review a decision days later and correct the rule behind it.
The model can change. Models improve and prices move. We keep the model behind a single connection, so switching providers is a contained change instead of a rebuild.
We have used AI-assisted tools for our own routine work since 2020. Building AI into a client’s systems takes the same habit: let the model do what it does well and check the rest. Senior engineers build each agent and review every line before it ships.
What an AI agent costs
How much does it cost to build an AI agent? It depends more on the job than on the technology. Every project starts with a free scoping call, where we agree on the use case and write down what we’ll deliver. Then you get one fixed quote before any code is written. Projects start at $2,500, and the price only changes if the scope changes.
What makes an AI project bigger or smaller:
- how many systems it reads from or writes to
- how many kinds of task it handles, and how varied your real cases are
- how much a mistake would cost, which decides how much testing and review it needs
- whether the data it needs is clean and in one place, or spread across files and tools
- whether it faces your customers or only your staff
An assistant that drafts replies for your staff to approve is a smaller project. An agent that acts across several systems without a person checking each step is a larger one. Model usage is billed separately by the provider, and we estimate it with you before you commit. How we work walks through each step from the first call to handover.
Tell us the task you’d most like to take off your team’s plate.
Questions about ai features & assistants.
What will it cost to run each month?
Model providers charge by usage, so running costs depend on how often the feature is used and which model it calls. We estimate them during scoping, set a spending cap and use the smallest model that does the job well.
Will the AI make things up?
Language models can state wrong things with confidence. We reduce that by giving the model only the information it needs, checking its output against rules before anything happens, and sending cases outside those rules to a person. When answers must come from your own documents, we build a system that cites its sources.
Is our data used to train AI models?
The business APIs of the major providers, such as OpenAI, Anthropic and Google, don't use your data for training by default, and we keep those settings. For sensitive data we can also use a model hosted in your own cloud account.
Which AI model do you use?
The one that fits the task and your budget. That might be a model from OpenAI, Anthropic or Google, or an open model running on your own servers. We build so the model can be swapped later without rewriting the feature.
Can the agent act on its own?
Only where you decide it may. Most agents start by preparing actions for a person to approve. Once the tests and the logs show it handles a task well, you can let it do that task alone, within limits set in code.
Tell us what you're building.
We'll come back with a clear plan and a fixed quote. No obligation, and no surprise invoices.
Thanks, we've got it.
A senior engineer will reply within one business day to set up a time to scope your project.
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