Partnering with Navatech, we developed an AI-powered chatbot for construction firms to enhance on-site safety, which has also gotten a NEOM partnership. Leveraging structured data pipelines, it delivers real-time answers, safety guidelines, and incident reporting, boosting compliance and decision-making.
We developed an AI-powered copilot for doctors that provides real-time support to the medical professional for clinical decision-making, diagnosis accuracy, and treatment planning with data-driven insights and recommendations.
An AI-powered harness designed to give navigation support to visually impaired people. We designed an advanced AI/ML algorithm for context-aware navigational assistance and applied ML models for path optimisation.
We start by understanding your business workflows, operational bottlenecks, and AI objectives to identify where LLMs can create measurable value.
Output: AI opportunity assessment, Business use case definition, Success metrics framework, LLM implementation roadmap
Our team evaluates your enterprise data ecosystem to prepare high-quality, AI-ready datasets for training and retrieval workflows.
Output: Data readiness assessment, AI-ready dataset preparation, Synthetic data generation strategy, Data governance and privacy recommendations
Based on the task complexity, we design or adapt the model architecture. It can be instruction-following, RAG-enabled, or multimodal. At this third and most crucial phase, we decide prompts, inputs, outputs, and control flows.
Output: LLM architecture blueprint, Model selection strategy, Prompt orchestration framework, Working AI prototype.
We fine-tune and adapt the selected model using curated data at this stage. We apply advanced techniques like transfer learning, reinforced learning, and human feedback (RLHF) and bias mitigation to improve the reliability and relevance of output.
Output: Fine-tuned LLM models, AI guardrails and validation layers, Hallucination reduction mechanisms, Performance optimization framework
Before deployment, we rigorously test model performance, validate outputs, and integrate the solution into your enterprise systems and workflows. This helps ensure the AI system performs reliably within real operational environments.
Output: AI evaluation and benchmark reports, Security and response validation, Enterprise system integrations, Production-readiness assessment
We deploy the models using scalable infrastructure (cloud, on-prem, or hybrid) and set up LLMOps pipelines for monitoring and retraining. We also provide ongoing support to make sure the solution evolves with your business needs.
Output: Production deployment setup, LLMOps and monitoring dashboards, Performance tracking framework, Continuous optimization roadmap
Developing a complete Large Language Model (LLM) from scratch, including training, fine-tuning, and production deployment, generally takes 3 to 6 months for a functional, business-grade AI solution.
For a truly enterprise-scale model built entirely from scratch, the timeline is often 6 months to over a year.
If we check the LLM development timeline summary, then PoC development can take 4-6 weeks, MVP AI solution around 2-3 months, production ready AI around 3-6 months, and enterprise-scale custom models over 6 months.
As an ISO 27001 and SOC 2 Type II compliant technology partner with experience supporting HIPAA and GDPR-aligned environments, we follow strict enterprise-grade data security and governance practices throughout the AI development lifecycle.
All training data is processed within isolated infrastructure environments and is never co-mingled across client projects or used to train third-party foundation models. We also execute required agreements such as NDAs, DPAs, and BAAs before any sensitive data exchange begins.
Depending on your security and compliance requirements, we can support private cloud deployments, restricted data access controls, encrypted storage and transfer, audit logging, and secure data deletion or retention policies after project completion.
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