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Generic chatbot platforms (Dialogflow, no-code bots) work for narrow flows. Custom NLP development matters when you need domain-specific understanding, integration with internal systems, control over data, or features the platform doesn't support — like document Q&A over your knowledge base, multilingual support tuned for your customers, or advanced retrieval and reasoning. We often start with a platform and graduate to custom builds as needs grow.

Both. Prompt engineering and Retrieval-Augmented Generation get most use cases to production faster and cheaper than fine-tuning. We fine-tune when there's a measurable accuracy gap on domain-specific tasks, when latency/cost demand a smaller specialized model, or when sensitive data requires a self-hosted model.

Yes. For regulated industries — healthcare, finance, legal — we deploy open-weight models (Llama, Mistral, Qwen) and self-hosted vector databases inside your VPC or on-prem infrastructure, so no data leaves your perimeter.

Every project gets a tailored eval harness: golden datasets for accuracy, hallucination detection, latency benchmarks, cost-per-request tracking, and red-team prompts for safety. Production systems get continuous monitoring with alerting on drift.

English, Vietnamese, Japanese, Chinese, Korean, French, German, Spanish, and other major languages out of the box. Less common languages may require additional fine-tuning data, which we assess during discovery.

A focused proof of concept (single use case, clean data) ships in 4–8 weeks. A production system with integration, evaluation, and monitoring typically takes 3–6 months. Multi-channel or multi-language platforms run longer.

PII redaction pipelines run before any data reaches an LLM. We use named-entity recognition and pattern-based detectors, audit every prompt/response, apply role-based access, and follow ISO 27001-aligned controls. For strict regimes (HIPAA, GDPR, financial regulations), we deploy fully self-hosted stacks.