AI development company for practical, production-ready AI
We build generative AI features, AI agents, retrieval-augmented search and machine learning models that work with your data and systems, with evaluation and guardrails built in from the start.
ZedBlu is an AI development company that turns promising AI ideas into software people rely on every day. Our work spans LLM application development, document and email automation, semantic search over your knowledge base and predictive models, all integrated into the web and mobile products your team and customers already use.
Demos are easy; production AI is not. The hard parts are grounding answers in your own data, controlling cost per request, protecting sensitive information and knowing when the model is wrong, so we design for all four from the start with evaluation datasets, logging, human review where needed and clear fallbacks.
We run AI in production ourselves. On the news platform we built for The Hawk and Dainik Hawk, each story pulled in from a news-agency feed is given an AI-written SEO headline, tags and a meta description, which removes a repetitive task from the newsroom every day.
What our AI & Generative AI Development team delivers
Generative AI applications
Generative AI development for drafting, summarizing, classifying, translating and extracting data inside your existing tools. We choose between OpenAI, Anthropic Claude and open-weight Llama models based on quality, speed, cost and data-residency needs, and keep the model layer swappable.
RAG and knowledge assistants
RAG development, short for retrieval-augmented generation, lets assistants answer from your documents, policies, product data and tickets, with citations back to the source. We handle chunking, embeddings, vector databases, access control and content freshness, so answers come from current, permitted material.
AI agents and workflow automation
AI agent development for multi-step tasks such as triaging requests, updating CRM records, drafting replies, reconciling documents or booking appointments through your APIs. Agents run with scoped permissions, audit logs and human approval for sensitive actions, so automation stays under your control.
AI chatbots for support and sales
Chatbots on your website, app or WhatsApp that answer questions, qualify leads and hand off to a person with full context. Connected to your knowledge base and business systems, they give answers specific to your products and policies rather than generic replies.
LLM features in existing products
OpenAI integration services, plus Claude and Llama integrations, for products that already have users. We release AI features behind feature flags, measure adoption and quality, keep prompt and token costs in check, and mask or exclude personal data before it reaches any model.
Machine learning models
Forecasting, scoring, recommendation and anomaly detection models built with Python, TensorFlow and PyTorch. We start with a simple baseline, prove the model beats it on your historical data, then deploy it behind an API with monitoring for drift and accuracy.
Computer vision and document AI
Extract fields from invoices, IDs, forms and contracts, or analyze images captured by cameras and phones. We combine OCR, vision models and LLMs with validation rules, and route low-confidence results to a person for review instead of guessing.
AI proof of concept
Not sure where AI fits? A short, fixed-scope proof of concept on your real data tests feasibility, accuracy and running cost before you commit to a full build. You get a working prototype, an evaluation report and a costed roadmap for production.
What you get
Grounded in your data
Answers and decisions come from your documents, databases and systems, with sources shown where it matters. That reduces made-up answers and lets users check what the AI tells them.
Measured, not guessed
Every AI feature ships with an evaluation set built from real examples. We track accuracy, latency and cost per task before and after each change, so improvements are proven rather than assumed.
Private and secure by design
Sensitive data stays out of prompts where possible, providers are configured not to train on your data, open models can run privately in your cloud, and every access is logged for audits.
No model lock-in
A thin abstraction layer lets you switch between OpenAI, Claude and Llama as prices and capabilities change, without rewriting your application or retraining your team.
Our process
Tools & tech we use
Use cases
- Internal knowledge assistants for HR, legal and IT policies
- Customer support automation on web chat and WhatsApp
- Invoice, purchase order and KYC document extraction
- AI-generated SEO metadata and tagging for publishers
- Lead scoring and personalized sales email drafts
- Demand forecasting for inventory planning
- Call and meeting summaries pushed into your CRM
Ready-made solutions to launch faster
Related services
AI & Generative AI Development: FAQ
How much does it cost to hire an AI development company?
AI app development cost depends mainly on data sources, integrations, languages, channels and how much evaluation and review the use case needs. A focused AI chatbot or assistant typically costs $3,000–$25,000+. Custom agents, document pipelines and machine learning models usually cost more because of data preparation and integration work. Model usage fees are separate and scale with volume. A free consultation comes first, and a fixed quote follows it.
How long does AI development take?
A proof of concept on your real data typically takes 2–4 weeks. A production-ready assistant or automation with integrations, guardrails and monitoring usually takes 6–12 weeks. Projects that need custom machine learning models or large data cleanup take longer. Milestones are set out in the proposal and demonstrated every week.
Which AI model should we use: OpenAI, Claude or Llama?
It depends on the task, data sensitivity and budget. Hosted models from OpenAI and Anthropic are strong at reasoning, writing and tool use, with no infrastructure to manage. Open models such as Llama can run in your own cloud for stricter data control or lower cost at high volume. We benchmark candidates on your own examples before choosing and keep the model replaceable.
Is our data safe when we use generative AI?
We design for data protection from the start: sensitive fields are masked or excluded, API providers are configured so your data is not used for model training, access is controlled by role and every request can be logged for audit. Where policy requires it, open models run inside your own cloud account so data never leaves your environment. An NDA covers the project from the outset.
How do you stop AI from giving wrong answers?
We ground responses in approved content through retrieval, ask the model to cite sources, restrict what it may answer and test against an evaluation set of real questions before each release. For high-stakes actions, a person approves before anything is sent or changed. No AI system is perfect, so we also design clear fallbacks and an easy handoff to a human.
Can we hire AI developers to work with our in-house team?
Yes. If you have product or data leadership in-house, you can hire AI developers who work as an extension of your own team. They join your sprints and tools, overlap with your working day and cover LLM integration, retrieval, agents and classic machine learning. Code, prompts and evaluation sets are committed to your repositories, and you own all of it.
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