AI privacy + search + agents

Private AI. Better search. Stronger applications.

Hemlig protects public AI APIs and RAG data, improves exposure in AI search engines, and helps candidates upgrade CVs, resumes, statements, and application materials with multi-model agents.

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SecureRAG Gateway

Defend public AI APIs, RAG stores, tool calls, logs, and returned context from extraction and PII leakage.

AI Search Growth

Make websites easier for ChatGPT, Gemini, and other answer engines to discover, cite, and recommend.

CareerLift Agent

Improve CVs, resumes, statements, and application packages using GPT, Claude, Gemini, DeepSeek, Qwen, and more.

Products

Three focused offers for teams that need AI to be useful, visible, and safe.

SecureRAG Gateway

Privacy-preserving infrastructure for companies exposing AI APIs to the public, including automotive agents, finance assistants, healthcare LLMs, and enterprise support bots.

Protect RAG datastores from extraction and probing attacks.
Guard tool calls, logs, and returned context against PII leakage.
Add retrieval policy, redaction, risk scoring, and audit trails.

AI Search Growth

SEO and answer-engine optimization for teams that want their pages to be found when people ask ChatGPT, Gemini, and other AI search systems for resources.

Rewrite pages for clear entities, topics, snippets, and citations.
Create AI-readable indexes, structured metadata, and crawl paths.
Track discoverability and improve pages with measurable tests.

CareerLift Agent

A multi-model agent that improves CVs, resumes, statements, cover letters, and application packages for jobs, internships, graduate school, and scholarships.

Tailors materials to role, program, rubric, and applicant history.
Checks consistency, missing proof, weak claims, and tone.
Produces polished drafts while preserving the applicant's voice.

Model choice

Use the model stack your customers already trust.

We design model-agnostic workflows so product teams can route by quality, cost, latency, compliance, and region.

OpenAI GPT

OpenRouter

Gemini

Claude

DeepSeek

Qwen

MiniMax

Grok

Local models

Research informed

Built from active privacy and AI-systems research.

Hemlig translates research experience in privacy-preserving machine learning, foundation model privacy, and RAG privacy into deployable product controls. Recent work includes differentially private RAG with random projection and split-learning privacy studies for modern AI models.

Read research highlights

Threat modeling

Extraction, prompt injection, membership inference, and private-context replay risks.

RAG privacy

Retrieval filters, random projection, redaction, and privacy-aware ranking.

AI visibility

Entity-rich pages and agent-readable indexes for AI answer engines.

Human outcomes

Application agents that improve evidence, fit, and clarity without generic writing.

Start with the product that moves revenue first.

Tell us whether you need SecureRAG, AI Search Growth, CareerLift, or a custom multi-model deployment.

Contact Hemlig