Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Build compliant conversational AI that processes sensitive text without exposing raw data.
Every user query in a chatbot or voice assistant is a potential compliance liability. Traditional cloud-based NLP centralizes sensitive conversations, creating data breach risks and violating regulations like GDPR and HIPAA. We engineer privacy-preserving language models that process text on encrypted data or on-device, ensuring raw conversational data is never exposed.
Phi-3.5 directly on user devices for zero-latency, zero-data-leakage interactions.Microsoft SEAL to run AI models on encrypted text, enabling secure cloud analysis.Move from a reactive compliance posture to a proactive technical safeguard. Protect customer trust and avoid regulatory fines by design.
Our approach integrates directly with your existing RAG infrastructure and enterprise copilot projects. For broader data strategy, see our services on sovereign AI infrastructure and federated learning systems.
Deploy natural language processing that protects sensitive text data and conversational privacy, enabling innovation in regulated sectors without compliance risk.
Build NLP applications that are compliant with GDPR, CCPA, and communications privacy laws from the ground up. We integrate differential privacy and on-device processing to ensure individual data points cannot be reverse-engineered from model outputs.
Develop chatbots and voice assistants that process sensitive conversations using encrypted embeddings and private fine-tuning. Data is processed in secure enclaves or on-device, never stored in plaintext on central servers.
Minimize your attack surface and data breach liability by eliminating centralized repositories of sensitive text. Our private NLP architectures ensure raw PII and confidential communications are never exposed during AI processing.
Accelerate deployment of NLP in healthcare, finance, and legal sectors by building with approved privacy-enhancing technologies (PETs) from the start. Avoid costly redesigns and compliance audits later in the development cycle.
A clear breakdown of project phases, key outputs, and estimated timelines for implementing privacy-preserving NLP solutions, from initial assessment to production deployment.
| Phase & Key Deliverables | Starter (4-6 Weeks) | Professional (8-12 Weeks) | Enterprise (12-16+ Weeks) |
|---|---|---|---|
Initial Privacy & Compliance Assessment | |||
Architecture Design for On-Device or Encrypted Inference | Basic Design | Detailed with Threat Model | Comprehensive with Red Team Review |
POC: Encrypted Embeddings or Private Fine-Tuning | Single-Method POC | Comparative POC (2 Methods) | Full Pipeline POC with Integration Test |
Production Model Development & Integration | 1 Core Model | 2-3 Models with A/B Testing | Multi-Model System with Orchestration |
Privacy-Preserving RAG Pipeline Implementation | Basic Vector Search with DP | Advanced Multi-Tenant RAG with Access Controls | |
Deployment: On-Premise or Secure Cloud | Containerized Deployment | Kubernetes Orchestration with Monitoring | Hybrid/Edge Deployment with CI/CD Pipeline |
Compliance Documentation & Audit Trail | Basic Data Flow Map | GDPR/CCPA Impact Assessment | Full NIST AI RMF & ISO/IEC 42001 Alignment |
Ongoing Support & Model Updates | 30-Day Warranty | 6-Month SLA with Updates | Dedicated Engineer & Quarterly Reviews |
Our privacy-preserving NLP solutions are engineered for sectors where conversational data sensitivity is paramount. We deliver compliant, high-accuracy language models that operate on encrypted data or on-device, eliminating data sovereignty and leakage risks.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Answers to common technical and commercial questions about implementing privacy-preserving AI for your natural language applications.
We implement a multi-layered approach. For on-device processing, models like Phi-3.5 run locally, ensuring data never leaves the user's device. For cloud-based tasks, we use fully homomorphic encryption (FHE) with libraries like Microsoft SEAL, allowing inference on encrypted text. We also apply differential privacy during fine-tuning, adding calibrated noise to training data to prevent reverse-engineering of individual inputs. This combination ensures compliance with strict communications privacy laws like GDPR and CCPA.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
