AI Chatbots That Actually Help Your Customers

Accuracy depends almost entirely on your data quality. On domain-specific questions backed by good documentation, we typically achieve 85-95% accuracy after the first two weeks of tuning. The bot clearly signals uncertainty when it can't ground an answer — and when confidence is low, it offers to connect with a human rather than guessing. We measure accuracy against an evaluation set of real customer questions, not vanity benchmarks.

Yes — and we build the feedback loop into the product from day one. Customers can rate answers, agents can flag incorrect responses during escalations, and we use the flags to expand the evaluation set, improve retrieval, and refine prompts on the next deploy. The system gets measurably better over time, with each improvement gated by the eval suite so quality never regresses.

The minimum viable input is your help center or documentation. From there, the chatbot improves dramatically with each additional source: FAQs, product descriptions, pricing pages, ticket history, win/loss notes from sales, and structured data from your product database. The more grounded sources you provide, the less the bot has to fall back on the base model's general knowledge — and the higher the accuracy on questions that matter to your business.

All data stays in your infrastructure or approved cloud providers. We implement role-based access controls, audit logs of every conversation, automatic PII redaction in logs, and zero-data-retention contracts with model providers (OpenAI Enterprise, Anthropic ZDR, Azure OpenAI, AWS Bedrock) for any sensitive data flow. No training data leaves your environment, and we're happy to sign a BAA or DPA before kickoff for HIPAA, SOC 2, or GDPR-bound work.

We monitor chatbot performance daily — deflection rate, CSAT signal, escalation rate per topic, latency, and cost — update the knowledge base when your documentation changes, and tune responses based on real user feedback and agent flags. Maintenance plans start at $750/month and scale up to a full-retainer model where new features ship monthly alongside performance work. Most clients move to a retainer after the initial engagement.

Yes — that's the line between a chatbot and an AI agent. We can wire the bot up to your APIs to look up an order status, change a subscription, schedule a meeting, create a support ticket, or update CRM data. Anything with a write effect goes through explicit confirmation prompts and gets logged for audit. If you need heavy multi-step automation, see our AI agent development service for that specifically.

A focused single-channel chatbot ships in 4-6 weeks. A multi-channel deployment (web + WhatsApp + Slack, for example) with custom escalation and CRM writes runs 6-8 weeks. The two-week AI Strategy & Audit is a lighter starting point if you're not yet sure which use case to ship first. We commit to dates during scoping and ship to them.

Per-conversation cost depends on conversation length and the model. A typical support chatbot runs $0.01–$0.10 per conversation in API costs after caching and prompt optimization. At 10,000 conversations a month that's $100–$1,000 in model spend. We design every system with token budgets, caching, and fallback to cheaper models for routine questions so the cost is predictable and visible on a dashboard.

Almost never the goal we recommend. The math that works is: deflect 30-70% of repetitive volume so your existing team has more time for the conversations that actually need a human. The bot escalates with full context so agents start ahead of where they would on a fresh ticket. Customers who do reach a human are happier because the bot didn't waste their time, and your team grows into a higher-leverage role rather than a queue-clearing one.