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AI agents and assistants · United Kingdom Assistants that know your business. Agents that stay within their limits.

AI agent and assistant development for UK businesses: internal and customer-facing assistants grounded in your knowledge, and agents that complete multi-step tasks in your systems with defined permissions and human oversight.

Discuss your project

UK-registered company. International engineering team.

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AI agents and assistants for UK businesses.

An assistant answers. An agent acts. Both are only useful when they know your business and stay inside the lines you draw. Krapton builds AI assistants grounded in your documents and systems, and AI agents that carry out multi-step tasks such as triaging enquiries, preparing quotes, updating records or drafting responses, with permissions, approvals and audit trails designed in.

We work with models from OpenAI, Anthropic and Google, connect them to your tools through well-defined functions, and evaluate behaviour against real scenarios before release. Human review is a feature, not an afterthought.

  • OpenAI, Anthropic and Google models
  • Function calling and tool use
  • LangGraph and orchestration
  • Vector databases
  • Node.js and Python
  • Microsoft 365 and Slack integrations
  • Next.js

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Where it earns its keep.

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    Customer-facing assistants

    Website and in-app assistants that answer from approved content and hand over to people at the right moment.

  • 02 /

    Internal knowledge assistants

    Answers for staff from policies, procedures and past work, integrated with Microsoft 365, Google Workspace or Slack.

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    Task agents

    Agents that gather information, take defined actions in your systems and ask for approval when the stakes are high.

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    Inbox and ticket triage

    Classification, extraction, drafting and routing for shared inboxes and helpdesks.

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Built to be trusted in production.

Evaluation before release, guardrails by design and people in control of the decisions that matter.

AI development overview

  1. 01

    Define the boundary

    What the assistant may answer, what the agent may do, and what always needs a person. Written down before design.

  2. 02

    Ground and connect

    Retrieval over approved knowledge and typed tool functions for actions, with least-privilege access to each system.

  3. 03

    Evaluate against scenarios

    A scenario set covering normal, edge and adversarial cases, rerun on every change.

  4. 04

    Deploy with oversight

    Confidence thresholds, approval steps, logs and dashboards so behaviour stays visible in production.

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The decisions that shape the build.

Agents that act in systems need least-privilege credentials, rate limits and reversible actions. Customer-facing assistants must be clear that they are automated and must hand over when a person is needed.

What every AI engagement includes

  • A use case with a measurable result and representative examples
  • An evaluation set rerun on every prompt, model or data change
  • UK GDPR-aware data flows, with UK or EU hosting where residency matters
  • Human review and defined permissions for anything consequential
  • Cost and quality monitoring after launch

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Applied AI, connected to a real product.

Krapton’s own AI products, then a success story from our international portfolio describing the work delivered.

Dental.AI project visual

Healthcare AI

Dental.AI

Dental.AI set out to give patients instant, AI-driven insight into their oral health from a single dental image — which meant pairing a clinical-grade analysis pipeline with a web experience patients actually trust.

Read the success story

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AI agents and assistants: questions UK teams ask.

What is the difference between an AI assistant and an AI agent?

An assistant answers questions and drafts content from knowledge you provide. An agent can also take actions: look things up in systems, update records, send messages or run workflows, within permissions you define and with approvals where needed.

Can an assistant answer from our own documents?

Yes. Retrieval-augmented generation indexes your approved documents and systems so answers are grounded and cited, and it can be restricted by user permissions so people only see what they are allowed to.

How do you stop an agent doing something harmful?

Least-privilege access to each tool, an explicit list of allowed actions, approval steps for anything consequential, rate limits, reversible operations where possible and full logging. We test adversarial scenarios before release.

Which platforms can it integrate with?

Microsoft 365 and Teams, Google Workspace, Slack, CRMs such as HubSpot and Salesforce, helpdesks, databases and your own applications through APIs.

How do we know it is working after launch?

Quality, usage and cost dashboards, feedback capture on every answer, sampled human review and a scenario set that is rerun whenever prompts, models or data change.

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Connect the capabilities.

Let’s scope your AI agents & assistants.

Bring the task, the examples and the systems involved. We’ll tell you what an evaluation set, a prototype and a first release would look like.

What are you thinking?