Chatbot Development
Chatbot development today means production LLM engineering: assistants built on GPT, Claude, or open-weight models, grounded in your company's knowledge with retrieval-augmented generation (RAG), wrapped in guardrails, and connected to the systems where work actually happens. StepTo builds customer support bots, internal knowledge assistants, and action-taking agents—with the evaluation discipline that separates a demo from something you can put in front of customers. Rates run $35–75/hr from our Serbian engineering team.
Chatbots & Assistants We Build
- Customer support assistants: RAG-grounded bots that answer from your help centre and policies with citations, deflect routine tickets, and hand off to human agents with full context.
- Internal knowledge assistants: Search-and-answer over wikis, SOPs, contracts, and drives—with per-user permissions so people only see what they are entitled to.
- Action-taking agents: Assistants that call your APIs to check orders, book appointments, update records, or trigger workflows—with confirmation steps and audit logs.
- Lead qualification & sales bots: Website assistants that qualify visitors, answer product questions, and book meetings into your CRM.
- Voice assistants: Phone-based bots via Twilio and speech platforms for booking, status lookup, and after-hours coverage.
Our LLM Engineering Stack
- Models: OpenAI GPT, Anthropic Claude, Google Gemini, and self-hosted Llama/Mistral for data-residency-sensitive deployments—benchmarked on your real conversations.
- RAG pipeline: Document ingestion, chunking, embeddings, and vector search (pgvector, Pinecone, Qdrant) with re-ranking and source citation.
- Guardrails & safety: Topic and tone constraints, prompt-injection defences, PII redaction, and configurable escalation to humans.
- Evaluation: A test suite of real user questions scored automatically on every prompt, model, or content change—so quality is measured, not assumed.
- Channels & integrations: Web widget, WhatsApp, Messenger, Slack, Teams, SMS, and voice; Zendesk, Intercom, Salesforce, HubSpot, and custom APIs.
- Analytics: Deflection rates, resolution quality, escalation reasons, and cost per conversation in an operations dashboard.
Delivery Path & Pricing
- Scoping & data audit (1–2 weeks): Use-case selection, content inventory, channel plan, and model shortlist with cost-per-conversation estimates.
- Pilot (6–10 weeks, $20,000–$60,000): A production-quality assistant on one channel, grounded in your content, with evaluation results you can inspect.
- Expansion ($60,000–$150,000+): Action-taking integrations, more channels and languages, and deeper workflow automation.
- Operation: Continuous tuning via staff augmentation from $4,500/month or a dedicated team from $13,500/month—see pricing.
Why StepTo for Chatbot Development?
- Production LLM experience: We build assistants with evaluation suites, guardrails, and monitoring—the unglamorous work that makes them trustworthy.
- Integration depth: The value is in connecting the bot to your CRM, helpdesk, and internal APIs; that systems-integration work is our core business.
- Model-agnostic: No reseller incentives—we pick the model that wins on your data, your budget, and your compliance constraints.
- Cost efficiency: $35–75/hr Serbian engineering rates, 40–60% below typical Western European and US AI consultancies.
FAQ: Chatbot Development
- How much does custom chatbot development cost?
- A production support assistant grounded in your knowledge base (RAG) typically costs $20,000–$60,000 and ships in 6–10 weeks, including evaluation and guardrails. Assistants that take actions—looking up orders, booking appointments, updating records via your APIs—range from $60,000 to $150,000+. Ongoing tuning and expansion is usually handled via staff augmentation from $4,500/month or a dedicated team from $13,500/month.
- Do you use GPT, Claude, or open-source models?
- We work with OpenAI (GPT), Anthropic (Claude), Google Gemini, and self-hosted open-weight models such as Llama and Mistral. The choice depends on quality requirements, cost per conversation, latency, and data-residency policy—regulated clients often need EU-hosted or on-premise inference. We benchmark candidates against your real conversations before committing.
- How do you stop the chatbot from making things up?
- Retrieval-augmented generation (RAG) restricts answers to your approved content with source citations; guardrails constrain topics and tone; and an evaluation suite of real user questions is scored on every prompt or model change so quality regressions are caught before deployment. For high-stakes flows we add human handoff rather than letting the bot guess.
- Which channels can the chatbot run on?
- Web widgets on your site or app, WhatsApp Business, Facebook Messenger, Slack and Microsoft Teams for internal assistants, SMS, and voice via telephony platforms like Twilio. One conversation engine serves all channels, so answers and business rules stay consistent everywhere.
- Can the bot integrate with our CRM, helpdesk, and internal systems?
- Yes—that is usually where the ROI is. We integrate with Zendesk, Intercom, Salesforce, HubSpot, and custom internal APIs so the assistant can check order status, create tickets, escalate to human agents with full conversation context, and log outcomes for reporting.