AI Chatbot Development Company for US & EU Businesses

It depends on the workload, not on brand loyalty. GPT-4o leads on tool-calling reliability and structured-output adherence at low latency; we default to it for transactional support bots that hit APIs. Claude 3.7 leads on long-context grounding and refusal calibration; we default to it for legal, compliance, and policy-heavy assistants. Gemini 2.0 leads on cost per token at frontier quality for high-volume read-heavy workloads. Every engagement starts with a side-by-side eval against your real ticket data, presented as a written comparison with cost, p95 latency, and refusal-rate numbers before we pick.

Three layers. First, RAG grounding: every factual answer cites a passage from your knowledge base via Pinecone or pgvector, and the LLM is prompted to refuse when retrieval confidence is below a tuned threshold. Second, the eval harness: a golden set of 300 to 800 real questions with labelled correct answers, scored every release with Ragas (faithfulness, answer relevance, context precision/recall) plus rubric-based LLM-as-judge. Third, monitoring in production: Langfuse traces every conversation, flags low-confidence answers for human review, and feeds them back into the golden set. Hallucination rate is a tracked SLO, not a vibe.

Yes, and the handoff is a first-class part of the design, not an afterthought. We integrate with Intercom, Zendesk, Salesforce Service Cloud, Front, and HubSpot Service Hub via their native APIs. The handoff includes the full conversation transcript, the user intent the bot detected, retrieval citations, and a confidence score so the human agent has context. Handoff triggers are configurable: explicit user request, low confidence, sensitive intent (billing dispute, legal, complaint), or after N failed clarifications. We tune the threshold against your CSAT and AHT targets in the first month.

Web chat widget (vanilla JS or React drop-in), Slack, Microsoft Teams, WhatsApp Business via Twilio or Meta Cloud API, SMS, Telegram, Intercom Messenger, Facebook Messenger, and voice via Twilio Voice or LiveKit. The conversation engine is channel-agnostic: same flows, same RAG index, same eval suite. Channel-specific work is mostly authentication and rich-message rendering. A typical second channel adds two to three weeks; a third channel adds one. WhatsApp Business takes longer because of Meta template approval, which is paperwork, not engineering.

Engagement starts with a GDPR-aligned DPA and a data flow diagram showing every place a user message lands. EU clients run on EU regions only (AWS eu-west-1, eu-central-1, GCP europe-west). PII redaction (Presidio plus custom rules) runs before any prompt hits the LLM provider. Conversation logs are retained per your policy with right-to-erasure tooling built in. For Anthropic, OpenAI, and Google we use zero-retention API endpoints where available. We are GDPR-aligned, ISO 27001 ready, SOC 2 Type II in progress, HIPAA-capable for healthtech, and CCPA-acknowledged for US consumer products.

Chatbots are fixed-scope and quoted all-in in USD across four tiers. A scripted FAQ and deflection bot starts from $400 (1–2 weeks). A lead-qualifying sales bot with CRM handoff starts from $1,100 (2–3 weeks). An internal manager-assistant bot with tool calls and RAG grounding starts from $1,400 (3–4 weeks). An orchestrated multi-agent system starts from $2,900 (4–6 weeks). You see the line-item budget at the end of discovery and sign off before any code is written. There is no recruitment markup and no tool surcharge; LLM API consumption runs on your own accounts, so you keep the cost lever.

Both, chosen deliberately. We use Voiceflow and Botpress when a visual flow builder genuinely speeds delivery on a simple, stable bot — but the conversation engine ships as code in your repository, not locked inside a vendor console. That means no per-message platform fees, no vendor lock-in, and no “the platform is down” incident outside your control. For anything with RAG grounding, tool calls, or an eval harness, custom code is the default because those need version control, CI, and test coverage that no-code tools do not give you.

Yes — that is the manager-assistant and multi-agent territory. The bot calls your APIs through typed tool schemas: look up an order, create a Zendesk ticket, check inventory, update a CRM record, trigger a workflow. Every tool has strict input validation, and irreversible or sensitive actions (refunds, cancellations, data changes) go behind a confirmation step or a human-approval gate by default. Actions are logged with the full reasoning trace in Langfuse, so you have an audit trail of what the bot did and why.

Yes. The frontier models we use (GPT-4o, Claude 3.7, Gemini 2.0) are natively multilingual, so conversation handling across English and major EU languages needs no separate model per language. The real work is in the knowledge base: we either maintain per-language RAG indexes or translate-at-retrieval depending on how your content is authored, and we extend the golden set with per-language eval cases so answer quality is measured in each language, not assumed from English. Language detection and per-language fallbacks are part of the flow design.

You do, from day one. IP is transferred at the start of the engagement, not held hostage until final payment. The conversation engine, prompts, RAG pipeline, eval golden set and infrastructure-as-code all live in your repository and your cloud accounts. LLM API consumption also runs on your own provider accounts, so you keep the cost lever and any zero-retention contractual terms. There is no proprietary runtime you have to keep paying us to access — if you part ways with us, the bot keeps running.

Optionally, as a monthly retainer — and it is where a chatbot earns its keep. Post-launch work is a loop: Langfuse flags low-confidence and thumbs-down conversations, we fold them into the golden set, expand intents, and run a weekly eval regression so you can prove month two beats month one. Retainers also cover model-upgrade ablations (moving from one frontier model to a cheaper or newer one is a measured A/B, not a leap of faith), prompt and cost optimisation, and new-channel rollout. If you would rather run it in-house, we hand over runbooks and train your team instead.

Against the metric that matches the tier, agreed before build. For a FAQ bot it is deflection rate and CSAT on bot-handled conversations. For a sales bot it is qualified leads and booked calls. For a manager-assistant bot it is staff hours saved and time-to-answer. Underneath all of them sit the engineering SLOs — faithfulness, answer relevance, containment, p95 latency and cost per conversation — tracked in Langfuse and Helicone and reviewed weekly. You get a dashboard, not a quarterly slide claiming success.