0%0%A generative AI demo takes a weekend; a product your team relies on takes engineering. Dezvo builds LLM applications end to end — assistants, copilots, content generation, and document automation — with the structured outputs, evals, and guardrails that separate production systems from prototypes.
Generative AI development is the engineering discipline of building products on large language models: choosing and routing models (Claude, GPT, Gemini, or open-source), grounding them in your data with retrieval, constraining them with structured outputs and guardrails, and measuring them with evaluation suites so quality doesn't silently regress.
The difference between a demo and a product is everything around the model — retrieval quality, prompt versioning, cost controls, fallback behaviour, and monitoring. That surrounding system is what Dezvo builds.
Customer-facing and internal assistants grounded in your docs and data — on web, WhatsApp, Slack, or in-app. Escalation to humans when confidence drops.
In-product copilots that draft, summarise, and act inside your existing software — embedded in the workflow, not a chat window bolted on the side.
Product descriptions, reports, emails, and marketing copy at scale — brand-voice tuned, template-constrained, human-reviewed where it matters.
Contracts, invoices, and forms parsed, classified, extracted, and drafted — LLM understanding plus deterministic validation on every field.
Vercel AI Gateway or LiteLLM: cheap models first, escalation on confidence, provider fallback on outage. No single-vendor lock-in.
JSON-schema-constrained responses and tool calling — so downstream code consumes typed data, not free text that breaks parsers.
A frozen test set scored on every deploy. Accuracy, latency, and cost tracked across prompt changes and model upgrades.
Input sanitisation, output filtering, PII redaction, and prompt-injection defence — before anything customer-facing ships.