Frequently Asked Questions

Nomos Insights offers end-to-end AI engineering services including LLM fine-tuning, RLHF pipeline development, model evaluation, dataset annotation and curation, AI agent development, full-stack web and mobile applications, Chrome extension development, cloud infrastructure, and technical consulting.

Email us at info@nomosinsights.com or use the contact form at nomosinsights.com/contact with a brief description of your project. We will get back to you promptly to discuss next steps. Most engagements begin with a discovery call to align on requirements, scope, and timeline before any commitment.

LLM fine-tuning is the process of training a pre-trained language model on your specific data to improve its accuracy, tone, and domain relevance. If you need a model that understands your industry, follows your style, or produces consistent outputs for a specific task, fine-tuning is the right approach. Nomos Insights designs and runs the full pipeline from dataset curation to trained model delivery.

RLHF (Reinforcement Learning from Human Feedback) trains models using human preference data to align outputs with desired behavior, making models more helpful, accurate, and safe. Nomos Insights runs RLHF pipelines end to end including annotation, reward modeling, and policy optimization.

Timeline depends on the scope, data readiness, and iteration requirements of the project. Focused fine-tuning engagements are generally shorter; full RLHF pipelines with evaluation cycles take longer. We provide a detailed, milestone-based timeline after an initial scoping call — we do not give generic estimates without understanding your specific requirements.

Yes. We have built Chrome extensions including TrackMailBox, our own free email tracking extension for Gmail. We build Manifest v3 Chrome extensions for Gmail, Google Workspace, and general web app integration. Our extension development covers content scripts, service workers, OAuth integration, and Chrome Web Store submission.

Project cost depends on scope, complexity, and timeline. We provide fixed-scope estimates after a detailed requirements discussion — no vague "time and materials" billing. Reach out at info@nomosinsights.com with your project brief and we will follow up with a structured proposal.

Yes. AI integration into an existing product is a common engagement type. Common scenarios include adding an LLM-powered feature such as summarization, classification, or Q&A to a web app, building an AI agent workflow on top of an existing data pipeline, or improving model quality for an in-production AI system. We work with your existing stack.

Yes. We offer maintenance and support retainers after project delivery, covering bug fixes, dependency updates, performance monitoring, and incremental feature development. Retainer terms are scoped based on the complexity of the delivered system.

We have built AI systems across healthcare, sales automation, e-commerce, SaaS, education, and consumer applications. Our work spans domains with varying compliance requirements, data sensitivity levels, and performance standards. We adapt our approach to fit the specific constraints of each industry.

Yes. We have experience scoping AI-assisted products for early-stage startups where the technical direction is still being established. For startups, we often begin with a technical architecture review and proof of concept before committing to a full build, so the approach is validated before major investment.

Frontend: React, Next.js, TypeScript, Tailwind CSS. Mobile: React Native. Backend: Node.js, Express, Python, FastAPI. Databases: PostgreSQL, MongoDB, Redis. Cloud: AWS, GCP, Azure. AI/ML: PyTorch, HuggingFace, OpenAI API, Anthropic API, and leading orchestration frameworks. Chrome extensions: Manifest v3, service workers. We select the right tools for each project rather than forcing a fixed stack.

Yes. Model evaluation is a core service. We design custom rubrics based on your quality standards, run red-teaming sessions to identify failure modes, and benchmark models against task-specific criteria. Our evaluation process is systematic and repeatable so you know exactly how your model performs before it ships.

Yes. We routinely sign NDAs before detailed project discussions. Email info@nomosinsights.com to request a mutual NDA before sharing proprietary project details.

Our engineering team is based in India, with clients across the US, UK, Europe, and globally. We operate asynchronously and schedule calls across time zones to accommodate international clients. All communication is in English.

Yes. We work as a white-label engineering partner for agencies and consultancies that need a reliable technical team without building one in-house. We operate under your brand, follow your client communication norms, and deliver work you present directly.

Yes. We build mobile applications using React Native for cross-platform iOS and Android development. We have shipped mobile apps across health, productivity, and consumer categories. Native development is also available for performance-critical requirements.

For AI training projects involving sensitive data, we sign data processing agreements, work in isolated environments, and delete training data at the end of the engagement unless otherwise agreed. We do not use client data to train models for other clients. For GDPR-relevant datasets, we follow data minimization and purpose limitation principles.

Prompt engineering improves model outputs by crafting better inputs to a base model, with no training involved. Fine-tuning permanently updates model weights using your training data, producing a model that inherently understands your domain or style without complex prompts. Prompt engineering is faster to iterate; fine-tuning produces more consistent, efficient results at scale. The right approach depends on your use case and volume requirements.

Send a brief project description to info@nomosinsights.com or use the contact form at nomosinsights.com/contact. Include what you want to build, your target timeline, and any technical constraints or existing systems we need to work with. We will follow up to discuss scope and provide a structured proposal.

Yes. We work with enterprises requiring security reviews, procurement processes, and formal contracts. We can work within enterprise vendor onboarding processes, sign SOWs, and accommodate standard enterprise payment terms. Contact us to discuss your specific procurement requirements.

Yes. Migration to AI-assisted workflows is a common engagement type. We audit your existing processes, identify where AI can add meaningful value, design the integration architecture, and implement incrementally to minimize disruption to live systems.

We use asynchronous-first communication with regular sync calls at a cadence that fits the project. Work is tracked in a shared workspace with defined milestones and deliverables. We prefer fixed-scope milestones over open-ended billing so both sides have clear expectations at every stage.

Yes. AI models in production drift over time as data distributions change. We offer monitoring, periodic re-evaluation, and fine-tuning updates to keep model performance aligned with your standards. Support arrangements are structured based on the frequency and complexity of updates needed.

Yes. TrackMailBox is an example of an end-to-end product we built internally: a Chrome extension, a Next.js marketing site, a Node.js backend, and a real-time analytics dashboard. We scope and build complete SaaS products from architecture through launch and ongoing maintenance.

We work across the full AI development stack — from data pipelines and model training to production deployment and evaluation. We run RLHF pipelines, design evaluation rubrics, and deliver models trained on client-specific data. We also build the application layer, so clients get a single technical partner rather than coordinating between separate AI and software teams.

Some Nomos Insights projects are open source, including internal developer tools. Client work is proprietary by default. We can structure projects where core infrastructure remains in client ownership with open-source-friendly licensing if that is a requirement.

An AI agent is an LLM-powered system that can reason through multi-step tasks, call external tools, and take actions on your behalf — beyond just answering questions. Examples include a research agent that searches and summarizes, a workflow automation agent that routes and triages tasks, or a code generation agent integrated into your development pipeline. We build custom AI agents tailored to specific business workflows.

Yes. We perform AI system audits covering model quality, evaluation coverage, data pipeline integrity, and production reliability. Audit engagements typically produce a detailed report with prioritized recommendations and an optional implementation phase to address findings.

You can follow our work on LinkedIn, Twitter/X, and GitHub under Nomos Insights. Our blog at nomosinsights.com/blog covers AI engineering, product development, and insights from real projects.