From custom models to autonomous agents, we build AI that ships — measurable, production-grade, and integrated into the systems your business already runs on.
"FNA Technology designs, builds, and deploys AI systems entirely in-house — from custom machine learning models and RAG pipelines to autonomous agents and computer-vision solutions. Since 2023 we have delivered 25+ projects for founders and operators across India, the United States, Singapore, the UAE, and the United Kingdom, with a 5/5 client rating. You work directly with the senior engineers building your models, not account layers or rotating juniors. Named products powered by our AI include Akeed and Khedmah."
AI development services encompass the design, development, and deployment of intelligent systems that learn from data and automate decision-making. This includes custom machine learning models for prediction and classification, large language model integrations for natural language understanding, autonomous agents that take actions across tools, and computer vision systems for image and video analysis.
Production-grade AI goes beyond a proof of concept. It requires clean data pipelines, robust evaluation frameworks, safety guardrails, system integrations, and ongoing monitoring. A properly engineered AI service connects directly into your existing software and workflows, delivering measurable operational results — not just impressive demos.
FNA Technology builds AI for businesses where automation, accuracy, and reliability matter. Every project starts with a clear business metric, and we deploy only when the system meets it.
Bespoke models trained on your data for prediction, classification, scoring, and recommendation — designed around the metric that matters to your business.
RAG pipelines, fine-tuning, and prompt engineering on top of leading foundation models — grounded in your knowledge base and guard-railed for safety.
Autonomous agents and advanced AI bots that take action across tools — handling support, workflows, and research end to end.
Image, video, and facial recognition systems for detection, inspection, and identity — deployed to cloud or edge.
AI-driven business process automation that removes manual effort from document processing, data entry, and decisioning.
The pipelines, monitoring, and infrastructure that keep models reliable in production — from ingestion to retraining.
AI project costs depend on the type of system — a fine-tuned LLM assistant is a different scope from a custom computer vision pipeline or a multi-agent orchestration platform. Data availability, integration complexity, and the accuracy bar you need to clear all influence the investment.
We scope every AI project during discovery, quoting a fixed price for each milestone so you see exact costs before committing. Contact us to describe your use case and get a transparent estimate.
Common questions about our ai development services services.
A focused AI assistant or chatbot can go live in 2 to 4 weeks. Custom ML models that require data collection, training, and evaluation typically take 2 to 4 months for a production-ready first version. We commit to a concrete timeline after discovery, once we understand your data and success metrics.
It depends on the project. RAG-based assistants and LLM integrations can work with your existing documents and knowledge base. Custom ML models for prediction or classification do require representative training data. During discovery we assess what data you have and what needs to be collected.
Yes. We build AI systems that connect directly into your existing stack via secure APIs, webhooks, or database pipelines. Integration is scoped from day one so the AI service works within your current workflows, not alongside them.
We run structured evaluation frameworks before deployment, testing against your specific accuracy benchmarks. For LLM-based systems we implement guardrails, content filtering, and hallucination checks. After launch, we monitor model performance and retrain as needed.
You work directly with senior engineers throughout the project. FNA Technology is a boutique studio — the engineers who design and train your models are the same people who deploy and support them. No account managers or junior handoffs.
Yes. We audit existing AI implementations, identify accuracy and performance issues, and either optimise the current system or rebuild it. This includes migrating between model providers, improving RAG pipelines, or replacing rule-based systems with learned models.
Share your goals and technical requirements. We will propose the right scope, stack, and delivery schedule.