AI that answers,
follows up and gets it done.
Twenty automations across your website, support, sales and back office. We start with whichever one is costing you the most, prove it on your own data, and widen it from there.
What this actually involves.
Every business we talk to is losing money in roughly the same five places. Visitors land on the website at 10pm, find no answer and leave. Support answers the same eight questions all day. A lead comes in on Friday and gets a call on Tuesday, by which point they have bought elsewhere. Staff spend their mornings copying information between systems. And none of those systems talk to each other.
None of that is an AI problem. It is a response-time and repetition problem, and AI happens to be very good at both. So we start from the leak, not from the technology: which of those five is costing you the most right now, and what would it be worth to close it.
Then we build for that one thing, prove it on your own data, and only widen it once the numbers hold. Plenty of ideas do not survive that stage. Finding out in week three is a great deal cheaper than finding out after launch.
Five problems, and the 20 things we build to fix them.
Nobody buys an agent. They buy an end to the thing that is costing them money. Find yours below, then look at what sits under it.
AI for your website
Visitors leave without getting an answer or becoming a lead.
Most enquiries never get made. Someone arrives outside office hours, cannot find what they need in thirty seconds, and goes to a competitor. An assistant that actually knows your services turns a share of that traffic into named, qualified leads.
- AI website assistant, answering product and service questions 24/7
- AI lead capture and qualification, scoring prospects before they reach sales
- AI conversion optimisation, finding where and why visitors drop off
AI for your customers
Support spends its day answering the same questions.
Pricing, availability, order status, opening hours, policy. The same queries, every day, taking your team away from the problems that genuinely need a person. Automating the repetitive tier usually frees up more capacity than hiring would.
- AI customer support automation for pricing, policy and FAQ enquiries
- AI appointment and booking automation, without waiting on a staff member
- AI order and service status assistant, answering "where is my order?"
- AI review and feedback automation, catching unhappy customers before they post
- AI WhatsApp automation for enquiries, reminders and order updates
AI for sales
Leads are not followed up quickly or consistently.
Speed of response predicts conversion more reliably than almost anything else, and most follow-up depends on somebody remembering. Automating it means every enquiry gets chased, including the ones that went quiet before anyone noticed.
- AI sales assistant, qualifying requirements and recommending services
- AI follow-up automation across email, WhatsApp and SMS
- Abandoned enquiry recovery, reviving quotes that were never completed
- AI quotation and proposal generation from customer requirements
- AI customer reactivation campaigns for dormant accounts
- AI sales and CRM automation, updating records and creating tasks
AI for operations
Staff spend hours a week on repetitive admin.
Reading emails and routing them. Typing invoice details into another system. Chasing documents during onboarding. Hunting for a file somebody saved somewhere. This work is invisible on a P&L and expensive in practice.
- AI email automation, classifying, drafting and routing to the right team
- AI document processing for invoices, forms, contracts and applications
- AI internal assistant that searches company knowledge and drafts reports
- AI customer onboarding, collecting information, documents and approvals
AI for business automation
Your systems do not talk to each other.
The website, the CRM, the inbox, WhatsApp, payments and the accounting package each hold part of the picture, and a person is the integration layer between them. That person is the bottleneck, and they are the single point of failure.
- Website-to-CRM automation, so leads are created, assigned and tracked automatically
- End-to-end process automation connecting website, CRM, email, payments and internal tools
What you get from the engagement.
Task and workflow agents
Agents scoped to one job inside a real workflow: triaging a queue, reconciling two systems, drafting and filing a document, chasing an exception until it clears.
Tool and system access
The agent acts through your APIs, with its own credentials and its own permission set. It can do what we gave it permission to do and nothing more.
Evaluation and guardrails
A test set built from your real cases, run on every change. Confidence thresholds, refusal behaviour and an escalation path when the agent is out of its depth.
Human in the loop
Approval steps where the cost of being wrong is high, and full autonomy where it is not. Which is which is a business decision, so we make it an explicit one.
Process automation
Not every problem needs an agent. Where a deterministic workflow is faster, cheaper and easier to audit, we build that instead and say so.
Monitoring and cost control
What the agent did, what it cost, where it failed and how often a human had to step in, visible on a dashboard rather than discovered at the end of the month.
A sequence, not a retainer that drifts.
- 01
Find the task
A week of looking at where time actually goes. We come back with a shortlist, an estimate of hours saved and an honest note on which ones are not worth automating.
- 02
Prove it on your data
A working agent on one task, measured against cases you have already handled, so you can compare its answers to what your team actually did.
- 03
Put it into the workflow
Integration with the systems it needs to act on, permissions, logging, approval steps and a rollout to a small group first.
- 04
Widen it
More volume, then adjacent tasks. Each step repeats the same evidence test rather than assuming the first success generalises.
What lands on your side.
- A shortlist of candidate tasks, with hours saved and a recommendation on each
- A working agent on your own data, with its evaluation set
- Integrations and scoped credentials for every system it touches
- Audit log of every action taken, with the reasoning retained
- Approval and escalation rules agreed with the people who own the process
- A dashboard for accuracy, cost per task and human intervention rate
- Runbook and handover so your team can extend it without us
The questions we get first.
How is this different from the chatbot we already have?
A chatbot answers. An agent acts. It can create a record, send a request, update a system or escalate to a person, which is where the actual time saving comes from and also where the risk sits. Most of our work is the difference between those two: permissions, logging, evaluation and knowing when the agent should stop and ask.
What stops it doing something expensive or wrong?
Three things, in order. It only has access to the tools we gave it, so there is a hard ceiling on what it can touch. Actions above an agreed threshold need a human to approve them. And every action is logged with the reasoning attached, so when something does go wrong you can see exactly what happened rather than guessing. We also agree a kill switch before launch, not after.
How do you know it is accurate enough to trust?
We build an evaluation set from cases your team has already handled, so there is a known right answer to compare against. That runs on every change, and the pass rate is a number you see rather than a reassurance you receive. Where accuracy is not good enough for full autonomy, the agent still saves time by preparing the work for a person to approve.
What does it cost to run?
Model inference is usually the smaller cost and the easier one to control. We route each step to the smallest model that holds quality, cache what is stable and track cost per completed task as a first-class metric. If an agent only makes sense at a price point your volume cannot support, we would rather find that out in the pilot and tell you.
Which tasks are actually worth automating?
High volume, well defined, and currently done by a person following a process they could describe to you. Tasks that are rare, highly judgement-based, or where the rules change constantly are usually a poor fit, and we will say so. The first week of an engagement exists largely to separate the two.
Will this put our people out of work?
In our experience it moves them rather than removes them. The work agents handle well is the queue-clearing and form-filling that nobody enjoys and that grows with the business. Teams usually end up handling the exceptions and the judgement calls, which is the part the agent is worst at. It is worth being clear internally about that from the start, because teams can tell when they are not being told.
Let's talk about Agentic AI & Automation.
A short call is usually enough to tell whether we are the right fit. If we are not, we will say so.