AI Agents & LLM Apps
AI agents and assistants your customers can trust
Most chatbots fail the same way: confident, generic and occasionally wrong. We build assistants and agents that answer from your own content, use your tools within strict permissions, know when to hand over to a person, and are measured on real questions before anyone outside your team sees them.
Sound familiar?
Problems we fix
If your support inbox or your last chatbot looks like this, we can help.
Support is overloaded
The same twenty questions arrive every day, and your best people spend their time answering them instead of fixing real problems.
Knowledge is locked in documents
The answer exists somewhere in a wiki, a PDF or an old ticket. Finding it takes longer than asking a colleague.
Chatbots that go off-script
You tried a bot. It made something up in front of a customer, and now nobody wants to switch it back on.
No way to measure quality
Ask how often the assistant is right and you’ll get a shrug. That’s not good enough for customers.
What we build
What you get
Customer support assistants
Answers from your help content, around the clock, with a clean hand-off to your team when a question needs a person.
Internal knowledge assistants
Ask a question, get the answer from your own docs, policies and past tickets, with a link to where it came from.
Tool-using agents
Agents that look up an order, update a record or book a meeting, through tools that only allow what you’ve approved.
Lead qualification agents
Reply to every enquiry, ask the questions your sales team would ask, and book the call.
Document review agents
Pull the key terms out of contracts, reports and forms, compare them and flag what needs a human look.
Guardrails, evals & logs
Test sets, approval steps and a full log of every conversation, so you can see exactly what it said and why.
How we build agents
Trusted before it talks to a single customer
An agent is only useful if people trust it. Everything in our process is about earning that trust: grounding answers in your content, limiting what it can do, and measuring how often it’s right.
- 1
Collect the real questions
We gather the questions your team and customers actually ask, from inboxes, tickets and chat logs, and the answers you’d expect.
- 2
Prepare your knowledge
Help articles, policies, documents and past tickets turned into a source the agent can search and cite.
- 3
Give it the right tools
Only the actions you approve, such as looking up an order or booking a call, each with its own permissions.
- 4
Test against the real questions
We score answers on accuracy, tone and safety, and fix what falls short before anyone outside your team sees it.
- 5
Launch with a safety net
A clear hand-off to your team, approval steps for sensitive actions, and every conversation logged.
- 6
Review and improve
We read the conversations, find where it struggled and improve it, with the scores to show it worked.
Cost & timeline
What affects the price
Agents are scoped after a free call. These are the things that shape the price.
- Internal or customer-facing
- An assistant for your own team needs fewer safeguards than one that talks to customers in your name.
- How much content
- A few dozen help articles are quick to prepare. Thousands of documents across several systems take longer.
- Actions it can take
- Answering questions is simpler than updating records, issuing refunds or booking meetings, which need tools and approvals.
- Where it lives
- Your website, inside your app, Slack or email. Each channel adds a little set-up.
- Ongoing improvement
- A one-off launch, or regular reviews of conversations and quality.
Industries
Where agents fit, by industry
AI Agents & LLM Apps FAQs
Will the assistant make things up?
We ground answers in your own content, show sources, and have it say so when it doesn’t know, then measure accuracy on real questions before launch.
Can an agent take actions?
Yes, through scoped tools with permissions, and approval steps for anything sensitive such as refunds or record changes.
Where can people use it?
On your website, inside your app, or in tools your team already uses such as Slack or email.
How do you measure quality?
With test sets built from real questions, scored before launch and tracked afterwards.
Which model do you build agents on?
Whichever answers your questions best at a cost that makes sense: Claude, OpenAI or Gemini. We test more than one on your real questions before choosing.
Can it hand over to a person?
Yes, and it should. When a question is sensitive, unclear or outside what it knows, the conversation goes to your team with the full history attached.
How long before it’s live?
An internal assistant on your own documents can be ready quickly. A customer-facing agent that takes actions needs more testing, so we agree a timeline after seeing your content.
Do we need to clean up our documentation first?
Not before you start. Part of the work is finding gaps and contradictions in your content, and we’ll show you which fixes improve answers most.
Often paired with
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Learn more →Let’s see if an agent can answer your customers’ questions.
Bring a few real questions your customers ask. On a free 30-minute call we’ll tell you whether an agent can answer them well, and what it would take.
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