AI Product Development Services

AI product development services cover building AI products from scratch and embedding AI into software you already run — LLM integrations, RAG pipelines, agentic workflows, and multimodal document intelligence. Every build runs under the Axon AI Operating Model where evals, grounding, and EU AI Act governance are part of delivery

Our generative AI development services include LLM integration, retrieval-augmented generation, agentic AI, and multimodal AI — across major providers (OpenAI, Anthropic, Mistral) plus self-hosted open-source models.

LLM integration services connect a language model into your product: assistants, semantic search via RAG, and tool calling — with eval-driven prompt engineering and observability from day one. It’s often the fastest path to a measurable AI win.

AI work amplifies data concerns — customer data flows through models that aren’t always yours. We handle this in three layers. First, IP: everything we build with you — model weights, fine-tuning datasets, prompts, evaluation sets, RAG indexes — belongs to you under the same standard assignment terms covering source code. Second, data residency: you choose where data sits. EU-only workloads stay in EU regions; sensitive data can run on private cloud or on-premise. Third, model selection: we work across providers (OpenAI, Anthropic, Mistral, plus self-hosted open-source models), so vendor data policies are a deliberate choice, not a default.

Both have a place, and most engagements end up hybrid. Cloud-hosted models (OpenAI, Anthropic, Bedrock, Azure AI) give you the latest frontier capabilities with minimal ops overhead — pay-per-use, scale up instantly, no model-serving infrastructure to maintain. On-premise or private-cloud models (self-hosted Llama, Mistral, Qwen, and similar) give you full data control, predictable costs, and air-gapped deployment when regulations require it — but you take on the serving, scaling, and updating work. We typically map the decision against four factors: data sensitivity, latency requirements, scale, and your team’s MLOps capacity. For regulated industries, a common pattern is a cloud frontier model for general tasks plus self-hosted smaller models for sensitive data flows.

Hallucinations and accuracy drift are real — anyone shipping AI to production needs engineering discipline, not faith. Our approach has four layers. Evaluation: every AI feature ships with a golden dataset and automated eval suite that runs on every change, so accuracy regression is caught before deploy. Grounding: for factual queries we use RAG patterns that constrain answers to verified sources, with citations exposed to users. Guardrails: structured output schemas, input validation, and refusal patterns for out-of-scope queries. Human-in-the-loop where stakes are high: medical, legal, and financial decisions stay reviewable by people. Production monitoring tracks accuracy, latency, and cost continuously — not as a one-time launch metric. We’re also honest about scope: some use cases shouldn’t be AI-autonomous at all, and we’ll tell you that.

Our delivery teams are in Vietnam (Da Nang, Ho Chi Minh City, Thu Duc, Can Tho), with onsite specialists embedded at client sites. For EU clients, there’s a 4-hour daily overlap between CET afternoon and Vietnamese morning-to-midday — that’s when standups, planning, and live discussions happen. For clients in other regions (US, APAC, Middle East), we adjust the team’s working hours to maintain at least 1-2 hours of live overlap with your business day. Outside the overlap window, work is async. Working language is English, over your Slack or Teams.

It starts with an initial call. We listen to what you’re building, your needs, and your expectations. We walk you through what we offer and propose a team shape, timeline, and pricing model. No deck, no sales script. If we’re not the right fit, we’ll tell you. If we are, we move to contract discussion and kick off hiring and onboarding.