AI Chatbots

Chatbots that work with your systems, not around them

Most chatbot projects don't fail because the model can't hold a conversation. They fail because the bot has to connect to real systems — customer records, ticketing, internal tools, knowledge bases — and the integration layer was never built properly. That's where the engineering work actually lives.

Overview

What are AI Chatbots?

An AI chatbot is a conversational interface that lets users interact with software using natural language — to ask questions, get information, complete tasks, or take actions across multiple systems.

The simple version answers questions from a knowledge base. The harder version, and the one most teams need, is a chatbot connected to the systems your business runs on, taking real actions on the user's behalf. That kind of chatbot is mostly an engineering task, not a model problem.

We design chatbot systems around three principles:

Integration-first design

The chatbot is only as useful as the systems it's connected to. We treat the integration layer as the centre of the system, not as an afterthought to be wired up after the conversation works.

Conversation state that doesn't break

Users move between sessions, channels, and devices. The chatbot needs to know who they are, what was discussed, and what's still in progress — without losing context or duplicating effort.

Honest escalation

A chatbot that can't gracefully hand off to a human when it's stuck is a chatbot that will eventually embarrass the company using it. Knowing when to escalate, and making the handoff feel natural, matters as much as handling the conversation itself.

Audience

Who we work with

  • Product teams building conversational interfaces into existing software, where the chatbot needs to reason about product-specific data and take actions users care about.
  • Operations and customer teams whose processes have outgrown generic support chatbots, and who need something that integrates with internal systems rather than just answering FAQs.
  • Teams in regulated or data-sensitive environments where an off-the-shelf chatbot vendor isn't a good fit because of data residency, compliance, or integration constraints.
  • Organisations running conversations across multiple channels — web, mobile, WhatsApp, Teams, Slack — that need consistent context and capabilities everywhere.
Challenges

The problems we most often see

  • Chatbots that can answer but not do. A bot that can only return text has limited value in most real processes. The moment a user wants the bot to update a record, create a ticket, schedule something, or check a status, the integration layer becomes the whole game. Authentication, permissions, error handling, action logging — getting these right is mostly engineering work.
  • Context that breaks between sessions and channels. Users start a conversation on the website, continue on email, and follow up on WhatsApp. They expect the system to know who they are and what's already been discussed. Making that work requires a thought-out session and state model, not just connecting channels to the same model endpoint.
  • Off-the-shelf platforms that don't fit the actual use case. Generic chatbot products are excellent at what they were built for — a standard support flow on a standard CRM, for example. They become a much harder fit when the use case needs deep integration with custom systems, unusual permission structures, or product-specific reasoning.
    • Approach

    How we approach the work

    • 01 — Treat the integration layer as the most important part of the system. The language model is usually the easy bit. Getting the bot to authenticate properly, respect permissions, call internal APIs reliably, handle failures gracefully, and log actions for audit is where the real work lives. We design for these from day one.
    • 02 — Make context and state a first-class concern. How conversations persist, what the bot remembers, how state moves between channels, how old context expires — these are architectural decisions that shape every later choice. Getting them right early is much cheaper than fixing them later.
    • 03 — Be deliberate about when to use the model. Not every step in a conversation should go through an LLM. Some steps are better handled by deterministic code, some by structured forms, some by the model. Mixing these thoughtfully tends to produce systems that are more reliable, cheaper to run, and easier to debug than systems that route everything through a model.
    • 04 — Design escalation paths as part of the product. A graceful handoff to a human isn't a fallback — it's a feature. We design the escalation experience for the people on both sides of it, because review and escalation interfaces that don't fit how the team works get bypassed within weeks.
    Use Cases

    Where AI chatbots fit

    Software companies and product teams

    Chatbots embedded inside a product, where the conversation interacts with product-specific data and the bot can take actions the user cares about.

    Common use cases:

    • Product-embedded assistants that help users complete tasks inside the application
    • Customer-facing assistants for guided configuration, onboarding, or complex troubleshooting
    • Conversational interfaces for searching, querying, or interacting with product data
    • Multi-step interactions that involve reading and updating records on the user's behalf

    Operations teams in mid-sized companies

    Chatbots that integrate with the internal systems your business actually runs on — CRMs, helpdesks, ticketing platforms, custom internal tools.

    Common use cases:

    • Customer support assistants that resolve common issues end-to-end and escalate to humans when appropriate
    • Internal assistants for IT, HR, or operations questions, grounded in internal documentation
    • Sales and lead qualification bots integrated with CRM and routing logic
    • Knowledge-grounded chatbots with permission-aware retrieval, so users only see what they're allowed to see

    Multi-channel and regulated environments

    Conversational systems running across multiple channels with shared context, or operating where logs and access controls must meet compliance requirements.

    Common use cases:

    • Omni-channel deployments across web, mobile, WhatsApp, Teams, and Slack with shared state
    • Conversational systems in regulated industries, with auditable interaction logs
    • Bots that handle sensitive customer data with strict access controls and data-residency requirements
    • Internal assistants in environments where conversation history must be retained and queryable
    Process

    How a typical engagement runs

    Most chatbot projects move through five phases. We work alongside your team throughout, with weekly check-ins so you can see progress, raise questions early, and shift priorities as the project evolves.

    1

    Discovery

    We start by understanding the conversations the system needs to handle, the systems it needs to connect to, the channels it needs to support, and the quality bar the business actually requires. The output is a written plan with a realistic scope, timeline, and cost — and, where relevant, an honest recommendation on whether a custom build is the right call or whether an existing product would serve the use case better.

    2

    Build

    We design and implement the chatbot alongside your team, integrating with the systems and channels your business uses. Logging, tracing, and conversation analytics are built in from the first commit.

    3

    Validation

    We test the bot against real conversation patterns — including ambiguous inputs, failed integrations, and the edge cases that don't show up in scripted demos. Where the chatbot will operate at scale, we test under realistic load.

    4

    Deployment

    Production rollout across the planned channels, with monitoring, alerting, and the human escalation paths the use case requires. We stay close during the first weeks of live use, when the gap between test conversations and real ones tends to surface.

    5

    Handover, and what comes next

    We hand over the chatbot with full documentation, conversation evaluation infrastructure, and a clear plan for how your team will own and evolve it. Everything we build — code, infrastructure, operational knowledge — is yours.

    From there, you have two options: take the chatbot in-house and run it yourselves, or have us continue alongside you for monitoring, conversation quality reviews, and ongoing updates as the product or the underlying tooling evolves. Both work for us; we'll talk through the choice at the start of the engagement.

    Get in touch

    Let's talk about your project

    Most engagements begin with a short discovery phase: a few days spent understanding the conversations, the systems, the channels, and what success would actually look like. The output is a written plan with a realistic scope, timeline, and cost — and an honest read on whether we're a good fit.

    We're glad to start the conversation, whether you have a clearly scoped project, a rough idea you're still thinking through, or a specific problem you'd like a second opinion on.

    Contact us