Custom AI agents built for a real workflow, integrated with the systems you already run, and bounded by controls you decide.
We build AI agents that do work inside your business - reading from the systems you already run, taking defined actions, and handing off to a person when the decision warrants one. Every agent is scoped around a specific workflow, with explicit limits on what it can access and what it is allowed to do without approval.
The useful question is not which category an agent falls into, but which task it takes off a person's desk. These are the shapes that work comes to us in most often.
Handle the tier-one volume that follows a predictable shape - order status, account questions, returns eligibility - by reading the real record rather than guessing. The design work is mostly in deciding what the agent must escalate rather than what it can answer.
Answer questions from your own documentation, policies, and runbooks, with citations back to the source. Useful where the knowledge exists but is spread across systems nobody wants to search individually.
Carry a multi-step process from trigger to completion - intake, validation, enrichment, routing - calling each system in turn and stopping where a human decision is required.
Product discovery, order lookups, and post-purchase queries wired into your store platform. Fits alongside the commerce work we already do on Shopify, Magento, WooCommerce, and Odoo.
Translate a business question into a query against data you already hold, and return the result with the query shown. Pairs with our data science and Power BI work where the reporting layer already exists.
Reconcile records between systems, flag exceptions, and prepare routine documents for review. Best suited to work that is rule-heavy, high-volume, and currently done by copying between screens.
The engineering decisions that separate a working agent from a demo
We build for a specific process with a defined boundary, which is what makes an agent testable and safe to put in front of real work.
Retrieval against sources you control, so answers trace back to a document rather than to model recall.
Every tool the agent can call is declared and scoped. There is no ambient access to systems outside the workflow.
Consequential steps route to a person by design, not as a fallback after something goes wrong.
Tested against real cases from your workflow, including the ones that usually break automation.
Logged tool calls and decisions, so behaviour can be investigated instead of guessed at.
A methodical approach to delivering high-performance digital solutions.
Understand the workflow as it runs today, including the exceptions people handle manually.
Agree what the agent will do, what it will not do, and where a human stays in the loop.
Design the tools, data access, retrieval strategy, and permission model before building.
Build the narrowest version that exercises the real path, and test it against real cases.
Connect the agent to production systems behind scoped credentials and validated interfaces.
Run the agent against a test set drawn from actual workload, and review where it fails.
Release into production with logging, alerting, and a defined rollback in place.
Watch real usage, feed new failure cases into the test set, and tune from evidence.
The tooling we build on. Model choice is a per-project decision driven by the workflow, the data involved, and cost at expected volume.
Not every project needs every capability below. Depending on the workflow, some are essential and others add cost without adding value - we will say which is which during scoping.
The agent is given a defined set of functions it may call - not open access to your stack. Each tool has a schema, a permission scope, and a failure path.
Reading from and writing to the systems that hold your data. Where an API doesn't exist, an integration layer is built first - that is standard engineering, not AI work.
Grounding answers in your documents by retrieving relevant passages at request time, so content stays current and answers can cite a source.
Constraining the agent to return validated, typed data when the result feeds another system, rather than prose another service has to parse.
Sequencing multiple steps and tools, with state carried between them and defined behaviour when a step fails partway through.
Pausing before a consequential action so a person can approve, edit, or reject it. Which actions require this is decided per workflow.
A test set built from real cases, including the awkward ones, run against the agent before launch and again whenever a prompt, tool, or model changes.
Logging what the agent did, which tools it called, and what it received back - so a bad outcome can be traced to a step rather than written off as model behaviour.
An agent is only as useful as the systems it can reach. Where an API exists, connection is straightforward; where one doesn't, the interface has to be built first - which is integration work rather than AI work, and we scope it as such.
Scoped, read-mostly access to the tables an agent genuinely needs, rather than a broad connection.
CRM, ticketing, ERP, and internal services, through the same integration discipline we apply to any system-to-system work.
Catalog, order, and customer data from the store platforms we already build on.
Documentation, policy libraries, and wikis, indexed for retrieval and re-indexed as they change.
Line-of-business tools, including older systems that need an interface built before an agent can reach them.
Webhooks, queues, and scheduled runs, so an agent starts from a business event rather than only from a chat box.
The pattern below is what most of our agent work looks like once it reaches production. The important property is that the agent never reaches a business system directly - it calls declared tools, and those tools enforce what is permitted.
A message, a webhook, a scheduled run, or a state change in a system you already operate.
Receives the request with its instructions, its permitted tools, and the boundaries of the task.
Decides which tools to call and in what order, carrying state across steps.
Scoped function calls, API requests, and retrieval against your indexed content.
The record is read, or a change is prepared against the system of record.
Returned directly for low-risk work, or routed to a person when the action warrants sign-off.
We are deliberate about not overstating autonomy here. An agent following this pattern is capable within its defined scope and unable to act outside it, which is the property that makes it safe to connect to a system that matters.
Connecting a probabilistic system to a production database is a decision that deserves engineering discipline. These are the controls we apply by default.
An agent gets the narrowest set of permissions its workflow requires. A support agent that reads order status has no route to issue a refund unless refunds are explicitly part of its scope.
What the agent may retrieve is scoped deliberately, including at the row level where a tenant or customer separation exists. Data handling requirements from frameworks like GDPR are considered at the design stage rather than retrofitted.
Credentials live in a secrets store, not in prompts, code, or environment files checked into a repository. Tokens are scoped and rotatable.
Each run records the inputs, the tools called, and the outputs returned, so behaviour can be reconstructed after the fact.
Consequential actions route to a person. We decide together which actions qualify, based on what an incorrect action would cost to undo.
Defined behaviour when a tool errors, a response fails validation, or the agent cannot complete the task - including a clean escalation path rather than a confident wrong answer.
We do not hold SOC 2 or ISO 27001 certification and do not offer certified compliance, guaranteed uptime, or guaranteed model accuracy. What we offer is engineering that takes those requirements seriously - see our security posture for a direct account of where we stand.
Agent development sits on top of capabilities that have to exist first. If the underlying data is scattered or the systems have no usable interface, the agent is not the first problem to solve. Most engagements involve some combination of AI development, machine learning, and data science alongside the agent itself.
Where an agent needs to reach a system that has no API, or where the workflow it automates lives inside software that doesn't exist yet, the work extends into integrations and custom software development. For a broader view of how we approach AI projects end to end, our AI software development guide covers the wider lifecycle.
Deep technical analysis, architectural case studies, and strategic perspectives from our senior development teams.
AI implementation has a cost structure unlike most software projects - the visible costs are only a fraction of what you'll actually spend.
AI agents aren't just smarter chatbots - they understand a goal, gather information, and complete the task. Here's where they deliver business value.
From picking your highest-ROI use case to piloting and scaling: the end-to-end framework for deploying a custom AI agent for your business.
Explore our complementary expertise to accelerate your digital transformation journey.