Generative AI development services built for production
We turn large language models into dependable product features and internal tools, with your data, your guardrails and running costs you can predict before launch.
Our generative AI development services take you from a promising prompt to a feature your customers and staff can rely on every day. We build retrieval-augmented (RAG) applications, AI copilots inside existing software, content and document generation pipelines, and multimodal tools that read images and PDFs, using OpenAI, Anthropic Claude, Llama and other models chosen for each task.
We have shipped generative AI in our own work. On The Hawk and Dainik Hawk, the English and Hindi news platforms we built and run, stories imported from news agencies automatically receive AI-generated SEO headlines, tags and meta descriptions. We bring the same production mindset to client projects: versioned prompts, automated output checks, cost limits and graceful fallbacks when a model is slow or unavailable.
Generative AI for business works best on narrow, high-volume tasks with clear quality criteria. We help you pick those tasks, prove value with a small pilot and then scale, while you keep full ownership of the code, prompts and evaluation data.
What our Generative AI App Development team delivers
RAG applications on your data
Our RAG application development connects a language model to your documents, databases and APIs through a retrieval layer, so answers cite your own sources. We handle chunking, embeddings, hybrid search, re-ranking and access control, and we measure retrieval quality separately from answer quality so problems are easy to trace and fix.
AI copilots inside your product
AI copilot development adds assistance where your users already work: drafting replies in a helpdesk, summarizing records in a CRM, generating reports from dashboards or writing product listings in an admin panel. We design the interface so suggestions are easy to accept, edit or reject, and we log every interaction for later review.
Content and SEO generation pipelines
Batch pipelines that produce product descriptions, article summaries, headlines, tags, meta descriptions and translations from structured inputs and style rules. Outputs pass automated checks for length, banned terms and required facts, and can be routed to an editor for approval before they are published. Our news platforms use this kind of pipeline for SEO headlines, tags and meta descriptions.
Document understanding and extraction
Multimodal models read invoices, contracts, forms, ID documents and scanned PDFs and return structured fields, summaries or risk flags. We combine them with validation rules and confidence scores, send unclear cases to a review queue, and push clean data into your ERP, CRM or database through APIs.
Fine-tuning and model selection
Not every task needs the largest model. We benchmark hosted and open models on your examples, then choose the smallest one that meets the quality bar. Where prompting alone falls short, our LLM fine-tuning services adapt an open model such as Llama to your tone, format or domain vocabulary and deploy it in your own cloud account.
LLMOps and production readiness
Production LLM application development needs more than an API key. We set up prompt versioning, evaluation suites that run on every change, tracing, rate limits, caching, cost dashboards and provider fallbacks, so the feature stays fast and affordable as usage grows and models are updated.
What you get
Built around a measurable task
Every project starts with a defined job, sample inputs and a quality bar agreed with you. That makes it clear whether the model is good enough before money goes into a full rollout.
Predictable running costs
We estimate token usage per task, add caching and model routing, and set spending alerts. You see the expected monthly model cost before launch, not after the first invoice arrives.
Your data stays under your control
We choose providers and settings that keep your data out of model training, apply role-based access to retrieval, and can host open models in your own AWS, Azure or Google Cloud account.
Human review where it matters
For content that is published, sent to customers or used in decisions, we build approval steps, edit tracking and audit logs, so people stay accountable for the final output.
Our process
Tools & tech we use
Use cases
- Sales proposals and RFP answers drafted from past documents
- Product descriptions and translations for large e-commerce catalogs
- Summaries of support tickets, sales calls and meeting notes
- Contract review with clause extraction and risk flags
- SEO headlines, tags and meta descriptions for publishers
- Policy and compliance Q&A for regulated teams
Ready-made solutions to launch faster
Related services
Generative AI App Development: FAQ
How much do generative AI development services cost?
Generative AI development cost depends mostly on scope. A single feature added to an existing product, such as document Q&A or AI drafting, costs considerably less than a standalone app. A complete application with its own interface, integrations and admin tools is usually priced like other custom web apps, typically $8,000 to $60,000+. Model usage is a separate monthly cost. After a free consultation, we fix the price against a written scope.
How long does generative AI app development take?
A prototype on your own data typically takes two to four weeks. Taking it to production with integrations, evaluation, guardrails and monitoring usually adds another four to ten weeks, depending on the number of systems involved and how much human review the workflow needs. We demo progress weekly, so you can stop, adjust or expand scope at clear checkpoints.
Should we use RAG or fine-tuning?
Use RAG when answers depend on facts that change or must be traced to a source, such as policies, product data or contracts. Use fine-tuning when you need a consistent style, format or specialized vocabulary that prompting cannot achieve reliably. Many production systems combine both: a lightly tuned model for tone and format, with retrieval supplying current facts.
Which language model should we build on?
There is no single right answer, and the right choice changes as models improve. We test OpenAI, Claude and open models such as Llama on a sample of your real tasks and compare quality, speed, cost and data-handling terms. We then build a provider-neutral layer, so switching models later is a configuration change rather than a rewrite.
Who owns the prompts, models and outputs?
You do. ZedBlu signs an NDA before the project starts, and you own 100% of the source code, prompts, evaluation sets, fine-tuned open-model weights and generated content we produce for you. Accounts with model providers can sit under your organization, so billing, keys and usage data stay with you if you ever change vendors.
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