AI Chatbot Development
An AI chatbot that actually knows your product, your data, and what to do when it doesn't know the answer.
Most chatbot projects fail for the same reasons: the bot isn’t properly connected to your data, can’t do anything useful beyond answering questions, and has no graceful path to a human when it’s out of its depth. We build chatbots that avoid those failures from the start.
Why most chatbots fail in production
The technology is not the hard part.
The technology is not the hard part. LLMs are accessible, capable, and affordable. The hard part is everything that surrounds the model.
Not grounded in your data.
A chatbot that runs on a general-purpose AI model without being connected to your actual product knowledge, documentation, or policies will hallucinate. It gives plausible-sounding answers that are wrong for your specific context. Users stop trusting it within days.
No escalation design.
When the chatbot doesn’t know the answer — and it will sometimes not know — it needs a clear path to a human, with the context of the conversation preserved. Most chatbots are built with no escalation design, so they fail silently: the user gets a useless response and has to start over.
Integration that only looks deep.
A chatbot that can read from your systems but not act on them is limited. One that’s promised to integrate with your CRM or helpdesk but does so through shallow webhooks breaks under real usage. Integration depth is where chatbot projects most commonly over-promise and under-deliver.
No security design.
Prompt injection — users attempting to override the chatbot’s instructions or extract data they shouldn’t have access to — is the top security risk in production LLM applications. Most chatbots are deployed without addressing it.
Platform chosen for the wrong use case.
SaaS chatbot platforms like Intercom, Zendesk, or Drift are excellent for standard FAQ, lead capture, and appointment booking with your team’s data already in those systems. They are the wrong choice when you need proprietary data integration via RAG, deep API connections to custom systems, or a chatbot built as a core feature of your product rather than a widget sitting on top of it.
We are custom chatbot builders. We build for the use cases where platforms fall short.
When you need custom development instead of a platform
The honest split.
If you are not sure which situation you are in, tell us about the use case. We will give you a straight answer, including if we think a platform would serve you better.
A chatbot platform is the right choice when...
Your use case is standard customer support or lead capture, your data lives in systems the platform already integrates with, and the platform’s chat interface works for your users.
Custom development is the right choice when…
You need the chatbot to answer from your specific proprietary knowledge, product documentation, or internal data that isn’t already in a SaaS platform. You need the chatbot to perform actions in your own systems: create records, update fields, trigger workflows, retrieve user-specific information. You need the chatbot to be embedded inside your product as a core feature rather than a widget overlay. You have compliance requirements (HIPAA, SOC 2, GDPR) that require careful data handling in the AI pipeline. You need the chatbot to work within a product architecture you’ve built rather than adapting your product to a platform’s constraints.
What we build
Seven kinds of chatbots, built for production.
Customer Support Chatbots
AI-powered support assistants built on your product knowledge, documentation, policies, and historical support data. Users ask questions, get accurate answers from your actual content, and are routed to a human agent with full conversation context when the chatbot reaches its limits. We design the escalation paths as carefully as the chatbot itself. A 30-second escalation with full context is far better than a frustrated user who has to start over with a support rep.
Product-Embedded AI Assistants
Chatbots built directly into your product as a core feature, not a widget on top of it. Context-aware: the assistant knows which part of the product the user is in, what they were doing, and what permissions they have. Connected to your product’s data model. Useful for SaaS products where users need guidance through complex workflows, for platforms where users ask questions about their own data, and for any product where an in-context assistant reduces support volume and improves retention.
Internal Knowledge Chatbots
Employee-facing assistants connected to your internal documentation, policies, onboarding materials, HR processes, and knowledge bases. Instead of searching through Notion, Confluence, Slack, or email for answers, employees ask the chatbot. We build these with appropriate access controls: different roles see different information, sensitive content stays protected, and the assistant only answers from sources it is authorised to use.
Sales and Lead Qualification Chatbots
AI chatbots that engage website visitors, qualify leads through natural conversation, answer product and pricing questions, and route qualified prospects to your sales team with context. Not scripted decision trees that frustrate visitors. Generative AI that handles the variety of real buyer questions, identifies intent, and creates a hand-off experience that feels like a natural progression rather than an abrupt transfer.
Agentic Chatbots
Chatbots that don’t just answer questions — they take actions. Create support tickets, look up order status, update account details, book appointments, trigger approval workflows. The chatbot is the interface; the agent capability is the engine underneath. We design the action boundaries carefully: the chatbot knows what it is allowed to do without confirmation and what requires explicit user approval before executing.
Chatbot Integration and Enhancement
If you have an existing chatbot that isn’t performing well, we assess what’s wrong and fix it. Common issues: the knowledge retrieval pipeline isn’t returning the right content, the escalation logic is poorly designed, the chatbot doesn’t have the right system integrations, or the prompting is producing inconsistent outputs. We work within your existing stack or redesign the relevant layer, depending on what the problem is.
Chatbot Analytics and Continuous Improvement
A chatbot that launches is not a chatbot that’s done. We build in conversation logging, resolution rate tracking, escalation rate monitoring, and query analysis from day one. Post-launch, we use this data to identify where the chatbot is underperforming: queries where the retrieval isn’t returning the right content, escalation triggers that are firing too often, conversation paths that are abandoning before resolution. We run improvement cycles to address these systematically. The goal is a chatbot that gets more useful over time, not one that degrades as your product and content evolve.
Have a specific chatbot use case in mind?
Tell us what users need to do and what systems the chatbot needs to connect to. We’ll walk through whether custom development is the right approach and what it would take to build it properly.
How we build chatbots that work
Seven steps, anchored by retrieval and security.
Step 1: Define the chatbot's knowledge scope and action boundaries
What should the chatbot answer? What is it specifically not supposed to answer? What actions should it be able to take? What always requires human involvement? These boundaries are defined in writing before any code is written. They determine the data architecture, the integration requirements, and the escalation design.
Step 2: Design the data and retrieval architecture
If the chatbot needs to answer from your proprietary content, we design the RAG pipeline: how documents are processed, chunked, embedded, and stored; how queries retrieve the right content; and how we validate retrieval quality so the chatbot is answering from relevant material. This step determines whether the chatbot gives accurate, grounded answers or produces confident-sounding hallucinations.
Step 3: Build the integration layer
We connect the chatbot to the systems it needs: your product’s API, your CRM, your helpdesk, your knowledge base. We build the action layer for chatbots that take actions, with proper permissions, error handling, and idempotency so that a repeated request doesn’t create duplicate records.
Step 4: Design and build the escalation flow
For every situation where the chatbot should hand off to a human, we design the exact escalation path: what triggers it, what context gets passed, how the human agent picks up the conversation, and how the hand-off is communicated to the user. Done well, escalation is invisible to the user. Done poorly, it destroys the experience.
Step 5: Security hardening
We implement prompt injection protections, access controls, response filtering, and output validation. For customer-facing chatbots, we test against common adversarial inputs before launch. For internal chatbots, we enforce role-based access so employees can only retrieve content appropriate to their permissions.
Step 6: Test with real inputs
We test with representative real-world queries, including edge cases, ambiguous requests, and adversarial inputs. We calibrate the retrieval quality, tune the response behaviour, and verify the escalation paths work correctly across all trigger conditions.
Step 7: Deploy and monitor
We deploy with conversation logging, escalation rate tracking, answer quality monitoring, and cost tracking. These metrics are how you know whether the chatbot is working and where to improve it post-launch. We stay involved for a defined period after launch to tune based on real usage data.
Where chatbots deliver clear value
The use cases with the clearest ROI.
Customer support deflection.
The most common use case and the clearest ROI. A well-built support chatbot handles 30 to 60 percent of support volume without human involvement, at a fraction of the cost per interaction. The key word is well-built: a chatbot that produces wrong answers or frustrating experiences increases support volume, not reduces it.
Product onboarding and guidance.
Users who don’t understand how to use your product churn. A context-aware in-product assistant that answers questions, guides through setup, and explains features reduces time-to-value and decreases early churn.
Internal knowledge access.
The average employee spends significant time searching for information that already exists in internal documentation. A properly built internal knowledge chatbot retrieves that information instantly, reduces duplicate questions to the same teams, and makes onboarding faster for new hires.
Lead qualification and sales assistance.
Website visitors who get immediate, accurate answers to product questions convert better than those who fill out a form and wait. A well-designed sales chatbot qualifies intent, answers questions, and creates a seamless path to your sales team for conversations that are ready for it.
Complex workflow assistance.
For products with multi-step processes, an in-product chatbot that understands where a user is and what they need to do next reduces abandonment in complex workflows. This is especially valuable in B2B SaaS where the product has configuration or setup complexity.
Industries where we build chatbots
One concrete use case per domain.
Real estate and proptech.
Property search assistants that understand buyer or renter intent and surface relevant listings. Transaction workflow assistants that guide buyers and agents through process steps. Document Q&A chatbots that answer questions from property documents, contracts, and disclosure packages. Investor-facing assistants that retrieve portfolio data and reporting.
Healthcare.
Patient intake and FAQ chatbots that collect information and answer common pre-appointment questions. Provider-facing knowledge assistants for clinical protocols and policies. Appointment coordination chatbots that connect to scheduling systems. All built with data privacy and compliance requirements as starting constraints, with human escalation designed into every patient-facing decision path.
Education platforms.
Student support chatbots that answer questions about courses, schedules, and requirements. Learning assistants that help students navigate course material and get guidance. Teacher and administrator-facing knowledge assistants. Built for both early-stage edtech and institutional platforms.
Enterprise SaaS.
In-product assistants that reduce support volume and guide users through product complexity. Internal knowledge chatbots for support teams, sales teams, and operations. Onboarding assistants that accelerate time-to-value for new users and new employees.
Marketplace platforms.
Buyer-facing assistants that answer product and service questions, assist in selection decisions, and handle common post-purchase queries. Seller-facing assistants that help with listing creation, policy questions, and dispute guidance. Designed for the specific dual-user dynamic of marketplace products.
Technology we build with
The stack behind a grounded, secure chatbot.
Language models
OpenAI (GPT-4 and newer models), Anthropic Claude, Google Gemini, and open-source models (Llama, Mistral) for use cases where data privacy or cost constraints make them the better choice. We choose the model based on the use case requirements, not on familiarity.
RAG and retrieval
Vector databases (Pinecone, pgvector, Weaviate, Chroma) for knowledge retrieval. Document processing pipelines (chunking, embedding, indexing). Query optimisation to improve retrieval precision. Hybrid search approaches combining semantic and keyword retrieval where the use case requires it.
Orchestration
LangChain and LlamaIndex for chatbots with retrieval and tool-use. LangGraph for chatbots with more complex state management and multi-step agentic behaviour.
Integration layer
REST APIs, webhooks, and SDKs. CRM connectors (Salesforce, HubSpot, and custom). Helpdesk integrations (Zendesk, Freshdesk, Intercom APIs). Knowledge base integrations (Notion, Confluence, custom documentation systems). Product API integrations for chatbots that act on your system.
Security
Prompt injection protection patterns, output filtering, access control enforcement, conversation logging, and PII handling appropriate to your compliance requirements.
Deployment
Web widget, mobile SDK, Slack and Teams integration, and API endpoint for custom interfaces. Deployed on AWS, Google Cloud, or Azure aligned with your existing environment.
What we don't build
The honest boundaries.
We don’t train custom NLP models or fine-tune foundation models for chatbot behaviour. The LLMs we build on are capable enough for the vast majority of chatbot use cases without fine-tuning, and fine-tuning adds significant cost and complexity for marginal benefit in most scenarios. If your use case genuinely requires it, we’ll tell you and help you understand the trade-offs.
We don’t build voice AI systems. Building voice interfaces requires text-to-speech and speech-to-text infrastructure that is a distinct engineering domain. If your use case involves voice, we can advise on the right tools and partner with the appropriate specialists.
We don’t operate as a managed chatbot service. We build and hand over. You own the codebase, the deployment, and the ongoing operation. We can stay involved for iteration and enhancement, but the chatbot is yours to run.
"We needed AI search built into our platform without rebuilding the whole product. GTC designed the integration cleanly, it shipped on time, and it actually improved how users found things. That was the measure that mattered."
"They were honest from the start about what AI would and wouldn't solve for our specific product. That scoped the project correctly from day one. The integration worked in production on the first try."
FAQ
Questions teams ask before building an agent.
Chatbot platforms are subscription products with pre-built connectors and configurable templates. They are fast to deploy and work well when your use case fits their design: standard customer support workflows, FAQ responses, and lead capture where your data is already in or compatible with their system. Custom development is the right choice when you need the chatbot to answer from proprietary data that isn’t in a platform’s native format, when the integration requirements involve your own systems and APIs, when the chatbot needs to be a core product feature rather than a third-party widget, or when compliance requirements require control over how your data moves through the AI pipeline. We build the second category. If a platform would serve your needs better, we’ll tell you.
RAG stands for Retrieval-Augmented Generation. It’s the architecture that makes a chatbot answer from your specific content rather than from general AI training data. Without RAG, a chatbot either has generic knowledge that doesn’t reflect your product, or it has to be fine-tuned on your content, which is expensive and complex. With RAG, when a user asks a question, the system retrieves the most relevant content from your knowledge base and includes it in the model’s context before generating an answer. The chatbot answers from your actual documentation, policies, and product knowledge. RAG is not optional for chatbots that need to give accurate, specific answers about your product. It’s the foundation.
Hallucination — the model generating confident but incorrect answers — is the primary quality risk in any chatbot deployment. We address it at multiple layers. The retrieval architecture is designed to surface highly relevant content, which gives the model accurate material to answer from. The system prompt is designed to instruct the model to acknowledge when it doesn’t have the answer rather than guessing. Output confidence thresholds can be configured to trigger escalation when the model’s certainty is low. We also test specifically for hallucination patterns during development, including testing questions the chatbot shouldn’t know the answer to, to verify it declines gracefully rather than inventing something. No chatbot eliminates hallucination entirely, but well-designed retrieval and careful prompting reduce it dramatically.
This is one of the most important design decisions in any chatbot project, and we treat it as seriously as the happy path. We design escalation logic that specifies what triggers a human handoff: when the model’s confidence is below a threshold, when the user’s request is outside the chatbot’s defined scope, or when the user explicitly asks to speak to a person. When escalation triggers, the conversation context is passed to the human agent so the user doesn’t have to repeat themselves. The escalation experience is as important as the chatbot experience. A bad hand-off erases the goodwill the chatbot built in the preceding conversation.
Prompt injection is the top security vulnerability in production LLM applications: users attempting to override the chatbot’s instructions through cleverly worded inputs, or malicious content in documents the chatbot reads attempting to hijack its behaviour. We treat this as a first-class security requirement, not an afterthought. Protections include: strict system prompt design that limits the model’s susceptibility to override attempts, input filtering for common injection patterns, output validation to catch unexpected behaviour before it reaches the user, and access controls that ensure the chatbot cannot reveal system prompts or access data outside its defined scope. For customer-facing deployments, we run adversarial testing before launch.
Yes. Modern AI chatbots can do both: answer questions from your knowledge base and take actions in your systems. We call these agentic chatbots. The chatbot can create support tickets, look up order or account status, update user preferences, book appointments, or trigger approval workflows, depending on what integrations you need. We design the action layer with explicit permission boundaries: the chatbot confirms before taking consequential actions, operates within role-based access controls, and has clear limits on what it is and isn’t allowed to do autonomously.
The main input is the content the chatbot should answer from: product documentation, knowledge base articles, FAQs, policies, support playbooks, or any other structured or unstructured content relevant to the chatbot’s scope. The better the content quality, the better the chatbot performs. We assess your content during scoping and flag gaps or quality issues before we start building. For chatbots that take actions, we need API access or documentation for the systems the chatbot will connect to. We do a data and integration readiness assessment as part of the scoping process so there are no surprises during the build.
A focused chatbot with well-defined scope, clean knowledge content, and standard integrations typically takes four to eight weeks from scoping to production deployment. Chatbots with complex custom integrations, larger knowledge bases, or agentic action capabilities take longer. The most common timeline extender is content readiness: if your documentation needs significant cleanup or restructuring before it’s usable as chatbot knowledge, that adds to the timeline. We assess this upfront.
Yes. CRM and helpdesk integration is part of most chatbot builds we do. This includes reading customer data to personalise responses, creating and updating records as part of agentic actions, and routing conversations to the right human team with context. We build clean, maintainable integration layers rather than brittle point integrations. If your CRM or helpdesk has an API, we can integrate with it.
Yes. CRM and helpdesk integration is part of most chatbot builds we do. This includes reading customer data to personalise responses, creating and updating records as part of agentic actions, and routing conversations to the right human team with context. We build clean, maintainable integration layers rather than brittle point integrations. If your CRM or helpdesk has an API, we can integrate with it.
The primary channels we build for are web (embedded chat widget or full-page interface), in-product (built into your SaaS or mobile application as a native feature), and Slack or Teams (for internal knowledge and support chatbots). We also build API-based chatbot backends that can be connected to any channel your front-end team manages. We don’t currently build voice AI interfaces, which require a separate text-to-speech and speech-to-text infrastructure layer beyond the chatbot itself.
This is one of the most important post-launch operational questions. The answer depends on how the chatbot’s knowledge is structured. For RAG-based chatbots, updating the knowledge base typically means updating the source documents, re-processing the new or changed content through the embedding pipeline, and refreshing the vector index. This can be largely automated. For chatbots with hard-coded policies or workflow logic in the system prompt, those require prompt updates. We design the update process as part of the initial build so your team can manage routine content updates without needing our involvement for every change, while more significant architectural updates come back to us.
Chatbots are often the right first step. They establish the data and integration foundations that more sophisticated AI systems build on: a clean knowledge retrieval pipeline, tested system integrations, and a production-grade deployment with real user data flowing through it. An agentic chatbot, one that can both answer questions and take actions, sits directly on the path toward more complex agent systems. The architecture is compatible. If you are thinking about a longer AI roadmap, we can design the chatbot with that progression in mind from the start, so you are not rebuilding foundations when you want to extend the system’s capabilities. The AI Agent Development and Agentic AI Development pages cover where that path leads.
Ready to integrate AI into your product properly?
Ready to build an agent that actually works?
Tell us what workflow or use case you have in mind. We’ll walk through what the agent would actually need to do, what the technical approach looks like, and what it would take to build it properly.
Thirty minutes. A senior engineer. A straight answer on what’s feasible and what isn’t.
No pitch. No proposal until we understand what you’re trying to automate.