Services · AI Chatbot Development
AI Chatbot Development Services
Custom AI chatbots that answer from your own documents and systems, for customer support, internal knowledge, and lead qualification. Each one is tested against real questions before launch and hands off to a person when it should. Builds start from $15,000, depending on scope.
What is AI chatbot development?
AI chatbot development is building a conversational assistant on a large language model that answers from your business's own documents and systems instead of general internet knowledge. AI chatbot development services cover connecting that content, writing the rules for what the chatbot may say and do, testing it against real questions, and handing off to a person when needed.
A general-purpose chatbot knows nothing about your return policy or your price book. A custom chatbot closes that gap with a technique called retrieval-augmented generation, or RAG, which is simpler than the name suggests. Code and Trust is a chatbot development company that builds these systems as part of its AI implementation services.
If the core project is the underlying ingestion, search permissions and evaluation rather than a conversation interface, see our RAG development services.
How retrieval over your documents works
1. Index your content
Your help articles, policies, product documentation, and records are split into passages and stored in a searchable index.
2. Find the relevant passages
When someone asks a question, the chatbot finds the passages most likely to contain the answer.
3. Answer from those passages
The language model writes its reply from the passages it was handed, not from whatever it absorbed in training.
4. Show where the answer came from
Answers carry citations back to the source document, so a reviewer can check what the reply was grounded in.
The chatbot is only as good as the content behind it, so part of the work is finding gaps and outdated pages before launch. We build on the OpenAI and Anthropic APIs, with Python and LangChain for the logic and PostgreSQL with pgvector for the index.
Custom chatbot vs. an off-the-shelf chatbot tool: when to build
A custom chatbot is worth building when it has to answer from content spread across several systems, read or update live business data, follow handoff and data rules specific to your business, or be tested against a written accuracy bar. When your questions are simple and your content fits on a help page, an off-the-shelf chatbot tool is usually enough.
For a short FAQ, an upload-and-paste widget is often the right call, and we will say so. Custom starts to pay off when the answers live in a CRM, ticketing system, or database, or when some questions must never be answered without a person.
Off-the-shelf tool vs. configured platform vs. custom-built chatbot
| Dimension | Off-the-shelf chatbot tool | Configured platform | Custom-built chatbot |
|---|---|---|---|
| Data access | Files and web pages you upload or point it at. | Uploaded content plus the connectors the platform offers. | Your documents, databases, and internal systems, read through access rules agreed in writing. |
| Accuracy on your content | Good on simple, well-written FAQs. Hard to test or tune beyond that. | Tunable within the platform’s settings and prompt options. | Tested against questions drawn from your real conversations before launch, against a threshold agreed up front. |
| Integrations | A website widget and a few prebuilt connectors. | Connectors plus some custom code, within the platform’s limits. | Built to your systems: your CRM, ticketing, product data, and internal tools. |
| Ownership | You rent access. The vendor owns the software and the roadmap. | You own your configuration, but it only runs inside the vendor’s platform. | Source code, prompts, and test cases transfer to you on final payment. |
| Cost shape | A monthly subscription, often priced per seat, per conversation, or per resolution. | A subscription plus the cost of setup, configuration, and upkeep. | A build priced up front, plus model usage billed to your own account. |
What a custom AI chatbot can do
A custom AI chatbot can answer customer support questions from your product documentation, act as an internal knowledge assistant over your handbooks and records, qualify leads and record the answers in your CRM, and look up live data such as order status. Each answer can cite its source, and conversations reach a person when the rules say so.
Customer support
Answers customer questions from your product documentation and policies, and routes the conversation to your support team when it should not answer alone.
Internal knowledge assistant
Lets your team ask plain-language questions of handbooks, procedures, and historical records instead of searching shared drives.
Lead qualification
Asks a site visitor the questions your sales team would ask, records the answers in your CRM, and hands qualified conversations to a person.
Answers from live business data
Reads from systems you already run, such as order status, when they have an API or a database we can reach.
A chatbot answers. Software that takes actions on its own, such as updating records across systems, is an agent, and we build those through our agent factory for AI agents that take actions. Connecting a chatbot to your systems is the same work as our API development and integration services, and drafting replies your team approves before sending sits closer to AI workflow automation.
How our AI chatbot development services work
Our chatbot development services run in four phases: discovery and scoping, design and build, launch and handoff, ongoing partnership. Before the build we agree in writing what the chatbot may answer, when it hands off to a person, which data may enter a prompt, and the accuracy threshold it must clear.
Discovery & Scoping
Understand the workflow, agree on the deliverables, and set the scope, price, and delivery milestones before the build.
Design & Build
Design, develop, and test the agreed scope. Review working software and give feedback at the milestones in the delivery plan.
Launch & Handoff
Deploy the software, prepare the team handoff, and provide the agreed post-launch support. Source code and IP transfer on final payment.
Ongoing Partnership
Most large builds are fixed-price, with a monthly retainer after launch. Ongoing development, maintenance, and support are scoped separately.
Scoping can start with a 90-minute AI workflow audit, which is complimentary when it is bundled with a larger build and available as a paid one-off on its own. Prototype, staging, and deployment milestones are agreed in the written scope.
Accuracy, guardrails and human handoff
Accuracy, guardrails and human handoff are settled in writing before the build. We test the chatbot against your actual data and an accuracy threshold agreed up front, and if it misses that threshold, we do not deploy it. Topics with significant consequences route to a named person, and inputs, outputs, and overrides are logged.
Decision boundaries. We write down which questions the chatbot may answer on its own and which stop and route to a person. Anything with significant downstream consequences, such as approvals or financial matters, gets a human-in-the-loop step.
Data boundaries. We agree which of your data may enter a prompt. A regulated healthcare or financial setting gets tighter routing and retention rules than an internal assistant.
Logging. Model inputs, outputs, and human overrides are logged, so someone can reconstruct a specific answer months later.
The test cases, prompts, and model configuration transfer to you with the source code and IP on final payment. We do not provide legal advice or compliance certification; your own counsel reviews what we build.
What drives chatbot development cost
Chatbot development cost depends on how many content sources and systems the chatbot draws on, how deeply it integrates, how many handoff and data rules it needs, how thoroughly it is tested, and how sensitive the data is. Builds start from $15,000, depending on scope, with the price agreed in writing before any code is written.
Content and data sources
How many document collections and systems the chatbot draws on, and how clean and current that content is.
Integrations
Whether it only answers, or also reads from and writes to your CRM, ticketing, or internal tools, and whether those have usable APIs.
Guardrails and handoff
How many topics need handoff rules, and how the conversation reaches a person when the chatbot stops.
Testing depth
How many real questions the test set covers and how high the agreed accuracy threshold is set.
Data sensitivity
Which data may enter a prompt, logging and retention rules, and any regulatory constraints on the content.
Model usage is separate from the build: API costs from OpenAI or Anthropic run directly to your own account. AI implementation work usually takes 2 to 4 weeks for quick implementations, and longer, up to months, depending on complexity. Most large builds are fixed-price, with a monthly retainer after launch. Post-launch support and maintenance are scoped per engagement. To scope yours, get an estimate for your chatbot project.
Related services
Related services cover the work around a chatbot build. AI implementation fits AI into your operations starting with a workflow audit, the agent factory builds agents that take actions, AI workflow automation builds the pipelines behind the scenes, and API development connects the chatbot to the systems that hold your data.
Thinking about a chatbot for your business?
Tell us who would use it and where the answers live today. We will tell you whether an off-the-shelf tool will do, or what a custom build would cover.