Generative AI Development Services on Azure OpenAI

Custom LLM apps, RAG systems, and fine-tuning with GPT-4o — shipped with human oversight.

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What generative AI development requires to work in production

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Off-the-shelf chatbots impress in demos and disappoint in production — they hallucinate, ignore your data, and can’t be trusted with real decisions. Codevision builds generative AI grounded in your knowledge and wrapped in the controls a business needs:

  • Grounded in your content — RAG so answers come from your data, with sources.
  • Tuned to your domain — fine-tuning and prompt engineering for your language and tasks.
  • Built on Azure OpenAI — GPT-4o inside your tenant, under your controls.
  • Safe to ship — guardrails and human review on anything consequential.

we build

Custom LLM Applications

Purpose-built generative apps for your workflows, not generic chat wrappers.

RAG Systems

Retrieval-augmented generation that grounds answers in your knowledge base with citations.

Model Fine-Tuning

Tuning open and hosted models to your domain, tone, and tasks.

Prompt Engineering

Structured prompting and evaluation for reliable, repeatable outputs.

Content & Document Generation

Drafting, summarization, and analysis pipelines for document-heavy teams.

Evaluation & Guardrails

Testing, safety filters, and monitoring so quality holds in production.

GenAI engineered for production, not demos

We treat generative AI as software — tested, grounded, and governed.

Technology

Azure OpenAI Expertise

Certified specialists building on GPT-4o inside your tenant.

Accuracy by Design

RAG and evaluation pipelines that keep outputs grounded and measurable.

Human-in-the-Loop

Review steps wherever generated output drives a real decision.

Responsible Delivery

Safety filters and governance aligned with Microsoft's Responsible AI standards.

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Reduction in drafting/research time

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Answer accuracy on grounded queries

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Weeks to a working prototype

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GenAI solutions delivered

How we build a GenAI solution

A path that proves accuracy before you scale.

Define & Ground

We pin down the use case and connect the data the model must draw from.

Prototype

We build a working prototype and evaluate it against real queries.

Harden

We add guardrails, safety filters, and human review points.

Deploy & Monitor

We ship on Azure and track accuracy, cost, and drift over time.

Built for trust, security, and governance

Generative output carries real risk, so safety and traceability are built in.

Grounded & Cited

RAG keeps answers tied to your sources, reducing hallucination.

In-Tenant & Private

Runs on Azure OpenAI in your tenant; your data never trains external models.

Responsible AI

Safety filters and bias checks aligned with Microsoft's standards.

Human Review

People approve any generated output that drives a consequential action.

Real Results. Real Impact.

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Trusted by Businesses Worldwide: 100+ Happy Clients

They've experienced our services and they know how we did it.

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Frequently Asked Questions

Generative AI development is building applications that create content — text, code, summaries, answers — using large language models, tuned and grounded for a specific business. Codevision builds these on Azure OpenAI with RAG, fine-tuning, and guardrails.

We build focused assistants through to full LLM applications, including:

  • Custom LLM apps for your specific workflows
  • RAG systems grounded in your knowledge base
  • Fine-tuned models for your domain and tone
  • Content and document generation pipelines

Retrieval-augmented generation feeds a model your own documents at query time so answers come from your data with sources. Use it whenever accuracy and traceability matter more than open-ended creativity.

When it helps. Fine-tuning suits fixed tone or specialized tasks; often RAG plus strong prompting gets there with less cost and risk. We recommend the lighter option that meets your accuracy target.

We reduce hallucination with layered controls:

  • Grounding answers in your content with RAG
  • Evaluation pipelines that test outputs against real queries
  • Safety filters and guardrails
  • Human review on any consequential output

Yes. Solutions run on Azure OpenAI inside your tenant, and your data is never used to train external models.

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