Hand repetitivework to agentsyou can audit
We build AI agents and automations that read, decide and act inside the tools your team already uses, with clear limits on what they can do and a log of everything they did.
AI agents built around the work your team repeats
Every business has work that follows a pattern but still needs judgement: triaging support tickets, qualifying inbound leads, chasing missing documents, reconciling orders between two systems. AI agents and workflow automation can take a large share of that work, but only if they are connected to the right systems and given clear rules about when to act on their own and when to hand over to a person.
We start by mapping one workflow end to end with the people who do it today, including the exceptions they deal with. Then we decide which steps are plain automation, which need a language model to read or write something, and which must stay with a human. The result might be a support chatbot that resolves common questions from your help centre, or an agent that drafts replies for staff to approve.
Every agent we build has scoped permissions, an approval step for anything risky, and a full log of the inputs, tool calls and outputs behind each action. That makes it possible to review mistakes, tighten rules and expand what the agent handles as confidence grows, instead of switching on a black box and hoping it behaves. Your team stays responsible for the outcome, and the system is built so they can see and correct its work.
What our AI Agents & Automation service covers
- 01
Customer support chatbots
Chat assistants for your website, app or WhatsApp that answer from your help centre and order data, collect details, and hand over to a human with the full conversation attached.
- 02
Task-specific AI agents
Agents that use tools such as your CRM, ticketing system, calendar or database to complete a defined job, like lead qualification, ticket triage or document follow-up.
- 03
Workflow automation
Rule-based and AI-assisted automations across your SaaS stack, built in n8n, Make or Zapier where that is enough, and as custom code where reliability or volume demands it.
- 04
Document and email processing
Reading incoming emails, PDFs and forms, extracting the fields that matter, and routing or entering them into the right system with a review queue for low-confidence cases.
- 05
Internal knowledge assistants
An assistant in Slack, Teams or your intranet that answers staff questions from policies, wikis and past tickets, respecting who is allowed to see what.
- 06
Agent monitoring and guardrails
Dashboards, action logs, spending limits, approval rules and test suites that let you see what agents are doing and change their behaviour safely.
What you get from working with us
Staff time back for judgement calls
Routine lookups, copy-paste between systems and first drafts move to automation, so your team spends its time on the cases that need a person.
Faster first responses
Customers and leads get an accurate answer or acknowledgement at any hour, with a clean handover when the question goes beyond what the agent should handle.
Every action is traceable
Each step an agent takes is logged with its inputs and reasoning, so you can audit decisions, investigate errors and show reviewers how the system works.
Automation that grows in steps
Agents start with narrow permissions and approval gates. As results hold up, you widen what they handle without rebuilding the system.
Who this is built for
- Support teams answering the same questions across chat and email
- Sales teams losing inbound leads to slow follow-up
- Operations teams re-keying data between systems
- Businesses whose Zapier setup has outgrown simple triggers
- Companies that want AI help but need approval and audit trails
From brief to launch, step by step
- 01Step 01
Workflow mapping
We sit with the people doing the work, document each step, the systems involved and the exceptions, and agree where automation stops and a human takes over.
- 02Step 02
Integrations and test cases
We connect the APIs the agent needs with least-privilege access and collect real past cases, including awkward ones, to test the agent against before it touches live work.
- 03Step 03
Shadow mode pilot
The agent runs alongside your team, drafting or suggesting actions without executing them. We compare its output with what staff actually did and adjust rules and prompts.
- 04Step 04
Go live and widen scope
We switch on autonomous actions for the cases it handles reliably, keep approval for the rest, and review logs with you to decide what it takes on next.
Tools we trust
- OpenAI API
- Anthropic Claude API
- LangGraph
- Model Context Protocol (MCP)
- n8n
- Make
- Zapier
- Node.js
- Python
- Twilio
- WhatsApp Business API
- Slack API
Questions buyers ask us
Straight answers about AI Agents & Automation. Anything else? Ask us directly.
Cost is driven by how many systems the agent connects to, how many workflows and exceptions it covers, whether it needs to take actions or only answer questions, and the review and logging your team requires. Model usage and automation platform fees are ongoing costs on top. A discovery call lets us look at your workflow and suggest a sensible first scope.
It depends on access to the systems involved, the number of workflow branches, and how long the shadow-mode pilot needs to run before your team is comfortable. A single, well-defined workflow with good API access moves much faster than a multi-department process. We agree on milestones with you after mapping the workflow.
Usually, yes. Most modern CRMs, help desks, ERPs and communication tools have APIs we can connect to, such as HubSpot, Salesforce, Zendesk, Shopify, Google Workspace and Slack. Older or in-house systems may need a small integration layer or database access. We check this during discovery before promising anything.
Agents only get the permissions their job needs, and actions with real consequences, like refunds, outbound emails or record deletion, can require human approval. We test against past cases before launch, run in shadow mode first, log every action, and set limits on volume and spend. When the agent is unsure, it hands the case to a person.
No-code platforms are a good fit for straightforward flows your team wants to edit themselves. Custom code makes more sense at high volume, when logic gets complex, when you need proper testing and version control, or when data cannot pass through a third-party platform. Many projects combine both, and we recommend based on your workflow.
You own the code, prompts, workflow definitions and logs, delivered into your accounts and repositories. After launch, agents need attention as your tools, policies and model versions change. We document how everything works so your team can maintain it, and we offer ongoing support to monitor, test and extend agents if you prefer.
Ready to scope your AI Agents & Automation project?
A short call to understand your goals, constraints and timeline. You leave with a clear next step, with no obligation.