AI automation and chatbot development

JasperByte builds AI chatbots and automation agents for companies in Calgary, Alberta and across Canada. We connect large language models such as OpenAI GPT and Anthropic Claude to the systems your business already runs on — your CRM, your helpdesk, your database — so the AI can actually complete work rather than just talk about it.

What this involves

Most AI projects stall at the demo. A chatbot answers questions in a sandbox, everyone is impressed, and then it never touches a real customer because nobody solved the hard parts: authentication, data access, error handling, and what happens when the model gets it wrong.

We build for the part after the demo. That means retrieval grounded in your own documentation, tool calls into your real APIs, guardrails on what the model is allowed to do, and logging you can audit when someone asks why it said what it said.

What you get

  • Customer support chatbotsTrained on your documentation and ticket history, escalating to a human with full context when confidence drops.
  • Lead qualification agentsConversational intake that scores and routes enquiries into your CRM instead of dumping them in an inbox.
  • Retrieval-augmented searchAnswers grounded in your own content, with citations, so staff stop hunting through shared drives.
  • Document and email processingExtract structured data from invoices, applications, and PDFs, then push it into the system that needs it.
  • Internal copilotsPurpose-built assistants for your team, scoped to the data each role is allowed to see.
  • Evaluation and monitoringTest suites for prompts, quality tracking over time, and alerts when output drifts.

How we work

From scope to production.

  1. Scope

    We map the workflow you actually want automated and decide honestly which parts should use a model and which should just be code.

  2. Prototype

    A working prototype against your real data within the first sprint, so you can judge quality before committing to a build.

  3. Harden

    Guardrails, fallbacks, rate limits, cost controls, and human handoff paths. This is where most of the engineering lives.

  4. Operate

    Deploy, monitor quality and spend, and iterate on prompts and retrieval as your content changes.

Typical stack

OpenAIClaudeAI WorkflowsPythonNodejsN8NPostgreSQLRedis

FAQ

Common questions about AI automation.

How much does an AI chatbot cost to build?

It depends almost entirely on how many systems it has to touch. A focused support bot grounded in your existing documentation is a much smaller build than an agent that reads your CRM, checks inventory, and books appointments. We scope in stages and give you a fixed range before any work starts, so you are never signing a blank cheque.

Which AI model do you use?

Whichever fits the job. We work with OpenAI and Anthropic Claude models most often, and we design the integration so the model is a swappable component. That matters, because the price and quality landscape shifts every few months and you should not have to rebuild to take advantage of it.

What about our data privacy?

We use API tiers that do not train on your data, keep sensitive fields out of prompts wherever possible, and can run retrieval entirely inside your own infrastructure. For regulated work we document exactly what leaves your network and what does not.

What happens when the AI gets something wrong?

It will, occasionally — anyone who tells you otherwise is selling something. We design for it: confidence thresholds, escalation to a human with the full conversation attached, and logs you can review. The goal is a system that fails visibly and safely rather than confidently and quietly.

How long does an AI automation project take?

A scoped chatbot or automation typically runs four to eight weeks from kickoff to production. You will see a working prototype against your own data inside the first two weeks.

Still have questions? Talk to an engineer, not a salesperson — start a conversation or email info@jasperbyte.com.

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