Our generative AI development services
- LLM application development — custom apps and features powered by large language models.
- RAG (retrieval-augmented generation) — assistants grounded in your documents and knowledge base for accurate, sourced answers.
- AI copilots & assistants — in-product helpers that speed up real workflows.
- Content & document generation — drafting, summarization, and document automation at scale.
- Semantic search — natural-language search across your content and data.
- Conversational interfaces — see AI chatbot development.
- Model selection & integration — the right model (proprietary or open-source) wired into your stack.
- Guardrails & evaluation — accuracy, safety, and oversight built in.
Why choose TechEsperto for generative AI
Grounded in your data. RAG and fine-tuning approaches that make outputs accurate and specific — not generic or hallucinated.
Built into your products. We embed GenAI into your apps, web, and CRM, where it reaches your users and teams.
Production-grade, not demos. Evaluation, guardrails, and monitoring so it works safely in the real world.
Security & governance first. Data privacy, access control, and the option to deploy in your own cloud.
Senior engineers, transparent pricing, and code you own.
Proven delivery. 500+ projects, 30+ countries, 98% client retention, ISO 9001 certified.
Our generative AI process
- Assess — use case, data sources, and success metrics.
- Prototype — a proof of value on your real content/data.
- Build — RAG pipeline / app, integrated into your product.
- Evaluate — accuracy, safety, bias, and impact.
- Deploy & monitor — guardrails, monitoring, and continuous tuning.
See our full approach on Our Process and start with AI consulting if you’re scoping.
Technologies we use
OpenAI, Anthropic, and leading open-source LLMs; LangChain and RAG pipelines; vector databases (Pinecone, Weaviate, pgvector); embeddings; and secure integration via integration & cloud, with data foundations from data & analytics.
Industries we serve
We build generative AI for healthcare, fintech & BFSI, retail & eCommerce, education, professional services, and media & publishing. Browse all industries.
Frequently asked questions
What is RAG and why does it matter? Retrieval-augmented generation grounds an LLM in your own documents, so answers are accurate, current, and sourced — far better than a generic model for business use.
Can you build a copilot or assistant trained on our content? Yes — using RAG (and fine-tuning where appropriate) we build assistants grounded in your knowledge base.
How do you prevent hallucinations and ensure safety? With grounding (RAG), guardrails, evaluation against test sets, and human oversight where needed.
Which LLM should we use — proprietary or open-source? It depends on accuracy, cost, privacy, and data-residency needs. We recommend and can deploy open-source models in your own cloud when required.
Can you add GenAI to our existing app or CRM? Yes — we embed it into your app, web product, or CRM.
How do you handle data privacy? Data minimization, access control, encryption, and private deployment options. See integration & cloud.