Services · Artificial intelligence · AI agent development

Agents that do real work, with a person signing off.

Q: Will the agent make mistakes?

Yes, sometimes, so we design for it. Language models can misread a request or pick the wrong tool. We limit what each agent can do, add approval steps for risky actions, test against a set of real cases and log every run. When the agent is unsure it hands over to a person.

Q: Which model do you use?

The one that fits the task and your data rules. We build with OpenAI GPT models, Anthropic Claude, Google Gemini, Azure OpenAI, AWS Bedrock and open models such as Llama and Mistral. We compare them on your test cases and on cost, and keep the code able to switch models later.

Q: Can an agent work inside our existing systems?

Yes, through their APIs or a small connector. Agents work through the same APIs your other software uses, for example your CRM, ERP, helpdesk or booking system. Where a system has no API we look at exports, webhooks or a small service that sits in between.

Q: How is customer data protected?

Access is limited and handling follows GDPR and the data protection law in each market. Each agent sees only the data its tools return, and personal data is masked in logs where possible. We can deploy in your own cloud region and use model endpoints that do not train on your data. Data handling is designed around GDPR and the data protection law in each market.

Q: What do you need from us to start?

One workflow, one owner and real examples. We need a person who runs the process today, access to the systems the agent will use and a set of past cases with the right outcome. With those we can design the job and build the first test set.

We build AI agent development around your own systems: agents that read your CRM, call your APIs and take actions inside clear limits. Each agent has a job description, a set of tools it may use and a point where it hands over to a person.

We start with one agent on one workflow, measure it against a test set and widen its scope only when the logs show it is ready.

Get in touch All AI services

Built for: Operations, sales and support leaders at businesses who want an agent to carry part of a process, not just answer questions.
Typical timeline: A first agent on a single workflow usually runs in a pilot within four to eight weeks, depending on how many systems it must touch.
What you own: You own the agent code, prompts, tool definitions, evaluation sets and logs, deployed in your own cloud account.
Rated: 4.7 / 5 from 23 Google reviews

200+ companies across the US and the UAEFrom a first store to a listed developer

What you get

What an agent can do for you

An agent is a model with tools, memory and rules. These are the parts we design and build.

Explain your project

Tool use and actions
We give the agent a small set of named tools such as create a ticket, look up an order or book a slot. Each tool checks its inputs before anything touches a live system.
Routine steps get done without someone copying data between screens.
Approval steps
Actions that move money, change a record or send something to a customer can wait for a person to approve. The agent prepares the action and explains why it chose it.
Your team keeps control of the decisions that carry risk.
Customer service agents
Agents answer questions from your help centre, check order or account status and open a case when they cannot resolve the issue. The handover to a person carries the full conversation.
Customers get an answer at any hour and staff pick up only the cases that need them.
WhatsApp sales agents
A WhatsApp Business API agent asks qualifying questions, records the answers in your CRM and books a call with the right salesperson. It follows your scripts and stops when a lead asks for a human.
Sales staff spend their time on leads that are ready to talk.
Operations and internal agents
Internal agents reconcile reports, chase missing documents, prepare weekly summaries and answer staff questions about policy. They run on a schedule or when a trigger fires.
Back office work that repeats every week stops eating whole afternoons.
Multi agent workflows
Larger processes are split between agents with narrow roles, for example one that gathers data, one that drafts and one that checks. We build these with LangGraph or plain code so each step can be inspected.
Complex processes stay understandable and each step can be fixed on its own.
Evaluation sets
Before launch we build a set of real cases with the answers your team expects. Every change to prompts, tools or models is run against it.
You can see whether a change made the agent better or worse before customers do.
Monitoring and logs
Every run is logged with the inputs, tool calls, outputs and cost. Alerts fire when error rates, refusals or spend move outside the limits you set.
Problems are caught from the dashboard, not from a complaint.

Who this is for

Where agents fit first

The best first agent has a clear trigger, a short list of actions and a person who already does the job today.

Your inbox or WhatsApp line is full of the same questions

A service agent answers the repeat questions from your own content and hands anything unusual to the team with the context attached.

Start with the top twenty questions.

Leads arrive faster than sales can call them

A qualifying agent replies within seconds, asks your questions, scores the lead and books the call, so nobody waits for a reply.

Works with your CRM and calendar.

A process runs on spreadsheets and reminders

An operations agent watches for the trigger, collects what is missing and prepares the next step for approval.

Approvals stay with the process owner.

How we build an agent

01Job designWe write down what the agent does, which tools it may use, what it must never do and when it must ask a person.
02BuildWe build the tools, prompts and connections to your systems, using OpenAI, Claude, Gemini or an open model as the task requires.
03EvaluateWe run the agent against your test cases, review failures with your team and fix them before anyone outside sees it.
04Launch and watchWe release to a small group, monitor logs and costs, then widen access in steps.

How we work together

How engagements run

Most clients start with a fixed scope pilot for one agent, then move to a monthly arrangement for new agents, tuning and monitoring. We work with businesses directly or alongside your IT team.

Discovery and pilot

Teams deciding where AI should start

A short, fixed piece of work: the use cases ranked, the data checked, and one pilot built against real examples so you can judge it on evidence rather than a demo.

Use cases ranked by value and risk
Data and systems review
A working pilot on your own examples
A written plan for what comes next

Get in touch

Build and launch

A use case that is ready to go live

A written scope and a fixed quote, then the build: integrations, guardrails, an evaluation set and a launch with your team trained to use it.

Written scope and acceptance tests
Integration with your systems
Evaluation set and human review steps
Launch, training and handover

Get in touch

Run and improve

AI that is live and needs looking after

A monthly plan to watch quality, cost and usage, update prompts and models as vendors change them, and add the next use case when the first one earns it.

Quality and cost monitoring
Model and prompt updates
A monthly report on usage and results
The same team that built it

Get in touch

Technology

What we build this with.

Models are chosen per use case for quality, cost and where your data is allowed to go. The systems around them run on the stack below.

01Web

React
Next.js
TypeScript
Node.js
Laravel
WordPress

02Data and cloud

PostgreSQL
MySQL
MongoDB
AWS
Azure
Docker

Questions about AI agents

What is the difference between a chatbot and an agent?

An agent takes actions, a chatbot only replies.

A chatbot answers questions from content it has been given. An agent can also look things up in your systems, update records and trigger the next step in a process. That extra reach is why agents need tighter limits, approvals and logging.

Will the agent make mistakes?

Which model do you use?

Can an agent work inside our existing systems?

How is customer data protected?

What do you need from us to start?

Services that usually go with this one.

AI automationAutomate the repetitive work, keep people on the judgement.AI integrationAdd AI to the systems you already run.MLOps and AI platformsKeep your models working after launch day.

Tell us what the business needs. We will tell you what to build first.

Schedule a free consultation and get a written recommendation, with costs, before you commit to anything.

Get in touch

PROVEN RESULTS

4.7 / 5from 23 Google reviews

Agents that do real work, with a person signing off.

What an agent can do for you

Tool use and actions

Approval steps

Customer service agents

WhatsApp sales agents

Operations and internal agents

Multi agent workflows

Evaluation sets

Monitoring and logs

Where agents fit first

Your inbox or WhatsApp line is full of the same questions

Leads arrive faster than sales can call them

A process runs on spreadsheets and reminders

How we build an agent

How engagements run

Discovery and pilot

Build and launch

Run and improve

What we build this with.

Questions about AI agents

Services that usually go with this one.

Tell us what the business needs. We will tell you what to build first.