# AI Development > Building custom AI models, LLMs, and automated intelligent workflows tailored to your business needs. Provider: Netstatic Solutions (https://www.netstatic.in/) Page: https://www.netstatic.in/services/ai-development Email: netstaticsolutions@gmail.com ## Overview Netstatic Solutions designs and builds AI systems that solve specific business problems rather than showcase technology. We work with your team to identify where machine learning, language models or automation can remove manual effort, improve decisions or open new product capabilities, then deliver working software around that goal. Our engineers combine data engineering, model development and application engineering under one roof. That means your AI feature is not a notebook experiment but a production service with APIs, monitoring, access control and a clear path for retraining or prompt updates as your data and requirements evolve. We are model-agnostic. Depending on cost, latency, privacy and accuracy needs, we use hosted APIs such as the OpenAI API, open-source models you can run in your own environment, or classical machine learning built in Python with TensorFlow and PyTorch. The right tool is chosen for the task, not the trend. ## Capabilities - **Custom LLMs:** We adapt large language models to your domain through prompt engineering, fine-tuning and evaluation, so outputs follow your terminology, tone and business rules instead of generic responses. - **Workflow Automation:** We connect models to your existing tools and data so repetitive multi-step tasks such as triage, summarisation and data entry run automatically, with human review where it matters. - **AI Agents:** We build agents that plan, call tools and APIs, and complete tasks on behalf of users, with guardrails, logging and approval steps that keep behaviour predictable and auditable. - **RAG Systems:** Retrieval-augmented generation grounds model answers in your own documents and databases, using embeddings and vector search so responses cite sources and stay current without retraining. - **Predictive Analytics:** We train and validate forecasting, scoring and classification models on your historical data, then expose predictions through dashboards or APIs your teams can act on directly. - **Process Automation:** We combine document understanding, rules and machine learning to automate back-office processes such as invoice handling, document review and request routing across your systems. ## Benefits - **Built for your data:** Models and retrieval pipelines are shaped around your documents, terminology and workflows, so results are relevant to your business rather than generic. - **Production-ready engineering:** Every solution ships with APIs, logging, evaluation and monitoring so it can be operated, improved and handed over with confidence. - **Flexible model choice:** We balance hosted and self-hosted options against cost, latency and privacy needs, which avoids lock-in to a single provider. - **Human-in-the-loop control:** Review steps, confidence thresholds and audit trails keep people in charge of high-impact decisions made with AI assistance. ## Process 1. **Discovery and Use-Case Scoping:** We map your workflows, data sources and success measures, then shortlist AI use cases by feasibility and value and agree on a clear first release. 2. **Data and Architecture Design:** We assess data quality, choose model and retrieval approaches, and design the system architecture, security model and evaluation plan before building. 3. **Model and Application Build:** We implement pipelines, prompts or training code, and the surrounding APIs and interfaces in short iterations you can review and steer. 4. **Evaluation and QA:** We test against representative real-world samples, measure accuracy and failure modes, and tune guardrails, latency and cost before exposing the system to users. 5. **Launch and Support:** We deploy, set up monitoring and feedback capture, and continue to refine models and prompts as usage reveals new data and edge cases. ## Deliverables - Scoped use-case and architecture document - Trained or configured model pipeline - Production API and integrations - Evaluation report with test datasets - Monitoring and logging setup - Source code and handover documentation ## Technology - **Languages and ML Frameworks:** Python, TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers - **LLM and Retrieval:** OpenAI API, LangChain, LlamaIndex, pgvector, Pinecone - **Serving and MLOps:** FastAPI, Docker, MLflow, Airflow, AWS SageMaker ## Use cases - **Internal knowledge assistant:** Employees ask questions in plain language and receive answers drawn from policies, manuals and past tickets, with links to the source documents. - **Document processing automation:** Contracts, invoices and forms are classified, key fields extracted and validated, and exceptions routed to a reviewer instead of being typed in by hand. - **Demand and risk forecasting:** Historical sales or transaction data trains models that forecast demand or flag risky cases so planners can act earlier. - **Agent-assisted operations:** An AI agent drafts responses, updates records and triggers workflows across CRM and ticketing tools, pausing for approval on sensitive actions. ## Industries we apply it in - [Healthcare & MedTech](https://www.netstatic.in/domains/healthcare-medtech.md): HIPAA-compliant platforms, medical imaging AI, and scalable patient portals. - [Finance & FinTech](https://www.netstatic.in/domains/finance-fintech.md): Secure transaction systems, predictive AI for markets, and robust banking architectures. - [Enterprise SaaS](https://www.netstatic.in/domains/enterprise-saas.md): Multi-tenant architectures, subscription billing integrations, and cloud-native business tools. ## FAQ **How do you decide between fine-tuning a model and using RAG?** RAG is usually the first choice when answers must reflect changing company documents, because knowledge is updated by re-indexing rather than retraining. Fine-tuning suits cases needing a consistent style, format or specialised task behaviour. We evaluate both on your data and often combine them. **Can our data stay private when we use language models?** Yes. We can design solutions that send only minimal context to hosted APIs, mask sensitive fields, or run open-source models inside your own cloud or on-premise environment. The right approach depends on your data classification and policies, and we agree it during architecture design. **How long does an AI project typically take?** It depends on scope and data readiness. A focused proof of concept can often be shaped in a few weeks, while production systems with integrations take longer. After discovery we provide a phased plan with a first release that delivers measurable value early. **How do you measure whether the AI is working well?** We define evaluation criteria with you at the start, such as accuracy on labelled samples, answer groundedness, latency and cost per request. We build test sets from real examples, run them on every change, and track quality after launch. **What do we need to provide to get started?** Mainly a clear problem statement, access to representative data or documents, and a subject-matter expert who can review outputs. If your data is messy or scattered, we can include a data preparation phase and advise on what to collect next.