Local Private AI. On Your Terms.

Run Advanced AI Language Models entirely inside your own environment where you data lives. AskSLM delivers secure, local inference for legal, healthcare, finance, government and other regulated industries offering full data sovereignty, verifiable compliance, and zero cloud risk.

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100% On-Premise Zero Cloud Usage Built for Regulated Industries

The Problem: Why Current AI Fails High-Value Markets

Frontier models from OpenAI, Anthropic, and Google proved what AI can do but their default cloud, black-box approach blocks adoption in the very industries that need AI the most.

No Accuracy Guarantees. Trained on massive, largely unverified internet corpora with disclaimers like “may produce inaccurate information.” In practice, models can mix vendors, versions, and configuration details even when provided with correct documentation unacceptable for production infrastructure and regulated workflows.
Zero Transparency. Users have no clear view into what data trained the model or how it was validated. Ongoing lawsuits from publishers, authors, and artists highlight the legal risk of unauthorized training data. Outputs can’t be traced back to authoritative sources.
No Vendor Accountability. Cloud providers explicitly disclaim liability for AI outputs. No one “stands behind” the answers, which makes it impossible to certify for regulated use cases where professionals risk their licenses on every decision.
Privacy Violations. Sensitive data must be sent to third-party clouds. Many offerings cannot guarantee fully on-premise execution, running into HIPAA, SOC2, and other regulatory barriers. Enterprise data is exposed to external model providers and jurisdictions.
Unsustainable Costs. AI agent companies routinely pay tens to hundreds of dollars per user per month to external LLM APIs. GPU usage scales linearly with users, leaving no path to healthy long-term margins or large-scale, cost-predictable deployment.

AskSLM’s proprietary value

AskSLM’s proprietary value lies in its integrated expert-trained Small Language Model infrastructure a defensible combination of a C++-optimized inference engine, transparent and auditable training pipeline, hardware-bound encrypted execution, and a vendor-driven AI marketplace that no other platform provides.

The system uniquely enables enterprises, professionals, and government agencies to train, certify, and deploy specialized SLMs entirely on-premise through Trusted Execution Environments, encrypted model lifecycles, and multi-model concurrency designed for regulated and security-critical environments.

By combining vendor-certified accuracy, provenance-tracked training data, true local execution, and a marketplace flywheel, AskSLM owns a proprietary architecture and distribution model that cloud LLM providers cannot replicate forming a durable competitive most built on privacy, accountability, and domain-specific expertise.

Competitive Positioning

Feature	AskSLM	OpenAI / Anthropic	Hugging Face	Ollama
Vendor Accountability	✓	✗	✗	✗
True On-Premise	✓	✗	✗	✓
Training Platform	✓	✗	✗	✗
Marketplace	✓	✗	Limited	✗
5–20× Cost Reduction	✓	✗	Partial	✓
Enterprise-Ready	✓	Partial	✗	✗

Secure SLM Platform

A comprehensive on-premise AI platform that combines a high-performance AI Inference Engine with a secure model marketplace, designed for organizations that cannot compromise on data privacy or compliance.

AI on Your Infrastructure

Run advanced language models entirely inside your own environment. Sensitive data never leaves your controlled infrastructure, making audits and compliance straightforward.

Secure SLM Marketplace

Access curated, domain-specific AI models from trusted vendors or internal developers via a marketplace that never exposes your data or vendor IP.

Built for Regulated Industries

Designed for sectors like legal, healthcare, finance, and government, where confidentiality, governance, and auditability are mandatory.

AI Inference Engine

Real-time, on-premise intelligence that runs on standard hardware.

At the core of the Secure SLM Platform is its AI Inference Engine a high-performance “brain” that runs multiple AI models locally in real time. Users enjoy instant responses without cloud calls, even for complex analyses.

Runs on standard hardware no need for expensive GPU clusters or specialized cloud infrastructure.
Multi-model support keep specialized models (legal, medical, analytics, etc.) active in parallel.
Real-time performance local execution means zero network latency and no cold starts.
Always-on availability models are kept “warm” in memory and respond instantly.

Like an Operating System for AI

The Secure SLM Inference Engine acts like a local operating system for AI able to run many AI “apps” simultaneously to support various business functions, all within your own environment.

Document analysis Customer interactions Knowledge search Compliance checks

SLM Marketplace

A secure ecosystem for discovering, deploying, and using AI models.

The SLM Marketplace functions as an enterprise app store for AI models. Vendors publish pre-trained models, and customers deploy them with confidence without exposing data or model internals.

Trust broker architecture the marketplace coordinates access but never sees your data.
End-to-end encrypted connections between your on-premise engine and vendor model environments.
Two-way protection your data remains private and vendor model IP stays secure.
Simplified commercial model subscription and usage-based plans; deploy with a few clicks.

Talk about models

Example Marketplace Models

All accessed through secure, encrypted pipelines without transferring raw data or model weights.

User-Friendly Training & Knowledge Updates

Keep your AI aligned with your latest policies, cases, and guidelines without needing ML engineers.

No-Code Training Engine

Allow domain experts lawyers, clinicians, compliance officers to customize AI models through a visual, guided interface. No coding required.

Integrated Knowledge Updates (RAG)

Connect your internal knowledge bases and document repositories. Retrieval-augmented generation (RAG) pulls in the latest information at query time, keeping answers in sync with evolving data and rules without full retraining.

Domain Expertise on Demand

A single base model can be adapted into a “legal expert,” “medical assistant,” or “compliance advisor” using small, domain-specific specializations all controlled within your secure environment.

On-Premise Deployment, Simplified

Deploy Secure SLM on your existing infrastructure or use a dedicated SLM appliance for plug-and-play AI no ML engineering required.

Deploy on Existing Hardware

Turn your current servers, PCs, or high-end laptops into a private AI cluster. Use virtual machine images or direct installations to integrate seamlessly with your IT stack.

Runs on modern Mac, Linux, or Windows machines.
Leverages hardware you already own.
Ideal for data centers and secure enterprise networks.

SLM Appliance AI in a Box

A compact hardware device pre-loaded with the inference engine and models. Plug it into your office or home network and start using private AI immediately.

Zero cloud setup or provisioning.
Perfect for branch offices or remote work.
Energy-efficient and cost-effective.

In both deployment modes, all AI processing stays on-premise: no data ever leaves your location.

Security & Compliance by Design

Robust, multilayered protections secure both your data and your AI models, delivering the assurance regulators and compliance teams require to operate with confidence.

Data Protection

Zero cloud usage no data is sent to third-party clouds during inference.
Data stays on-site all processing occurs within your hardware or SLM appliance.
End-to-end encryption for any external model interactions coordinated by the marketplace.
Role-based access controls to ensure only authorized users and systems interact with models.
Segregated model and data storage to prevent cross-environment leakage

Secure Execution & Governance

Hardware-level encryption with Trusted Execution Environments (secure enclaves).
Policy enforcement to block unauthorized data flows.
Audit trails logging model access and usage within your environment.
Continuous updates aligned with evolving standards and regulations.
Model provenance tracking that records every version, dataset source, and change.
Automated compliance checks to ensure workloads align with internal and external policies.

Model Integrity & Provenance

Verified model provenance
Versioning and lineage tracking
Integrity checks before execution
Cryptographic signing of model builds
Model isolation to prevent contamination
Transparent documentation of training sources

Performance Optimization & Cost Efficiency

Efficient on-prem inference optimized for CPU/GPU/FPGA
Scheduling and resource allocation
Minimization of compute overhead
Transparent, controllable cost structure
No hyperscale dependency

Integration & Interoperability

Secure API gateways
Private connectors to enterprise databases
Document ingestion pipelines
ERP/CRM/EHR integrations
Local RAG indexing services

Risk Management & Compliance Assurance

Threat detection & anomaly monitoring
Vulnerability scanning & hardening
Regulatory alignment frameworks
Incident response readiness
Environmental isolation & sandboxing

Solutions for Regulated Industries

Secure SLM unlocks AI for organizations that have been held back by privacy, security, and compliance concerns.

Legal

On-premise document review and contract analysis.
Secure e-discovery and case research.
Real-time support during client meetings and negotiations.
Supports attorney–client privilege and confidentiality rules.

Healthcare

Summarizes patient histories and lab results.
Assists clinicians with diagnostic suggestions.
Supports coding and billing workflows.
Helps meet HIPAA and hospital IT policies.

Banking & Financial Services

Automate KYC/AML document intake and review.
Summarize portfolio, risk, and compliance reports.
Power secure internal copilots for relationship managers.
Assist compliance teams with evolving regulations and policies.

Mortgage Origination & Servicing

Extract income, assets, and key data from borrower documents.
Run consistency checks across full loan packages.
Generate compliant borrower communications and disclosures.
Accelerate underwriting decisions with AI-assisted risk flags.

Architecture & Engineering Firms

Search and interpret building codes and technical standards.
Assist with RFP responses, proposals, and design narratives.
Maintain a project knowledge base across drawings and specs.
Create safety, QA, and compliance checklists from project docs.

Educational & Research Institutions

Deploy private tutor and knowledge assistants on campus content.
Summarize research papers, grant proposals, and lab reports.
Help admin teams process applications, forms, and policies.
Protect student and PI data with on-premise, air-gapped AI.

Life Insurance Carriers & Brokers

Automate application triage and questionnaire analysis.
Summarize medical records for underwriting and risk scoring.
Generate clear, personalized policy explanations for clients.
Continuously monitor policy language for regulatory alignment.

Government / Public Sector Agencies

Assist staff with policy interpretation and regulation lookup.
Summarize citizen correspondence, case files, and hearings.
Power cross-agency knowledge assistants on internal data.
Support secure, air-gapped deployments for sensitive workloads.

Real-World Use Scenarios

See how AskSLM delivers private, compliant AI to regulated industries and to every sector that demands secure, sovereign intelligence

Legal Document Review & Analysis

A law firm deploys a legal domain AI model from the marketplace on their on-premise engine to perform large-scale document reviews.

Summarizes key points across thousands of documents
Finds relevant case precedents and inconsistencies
Reduces manual review time dramatically
All confidential client data remains on the firm’s servers

Patient Data Processing & Clinical Support

A healthcare provider integrates Secure SLM into its hospital network to help doctors and researchers.

Summarizes patient histories before consultations
Flags potential issues and lab anomalies
Clinicians securely query diagnostic models through encrypted, on-premise connections
Supports compliance with healthcare privacy laws and IT policies

Banking & Financial Services Risk Analysis

A financial institution deploys Secure SLM inside its protected network to support analysts and compliance officers.

Reviews lengthy financial filings and risk disclosures
Flags irregular transactions or AML/KYC concerns
Analysts query models privately through on-premise execution
All sensitive financial data stays within the bank’s controlled environment

Insurance & Reinsurance Claims Intelligence

A carrier or reinsurer uses Secure SLM to analyze claims, underwriting files, and high-cost medical cases.

Summarizes complex claims packets instantly
Identifies fraud signals, cost drivers, or missing documentation
Underwriters query specialized models during case evaluations
All PHI and claim data remain within the insurer’s secure infrastructure

Government & Public Sector Case Processing

A government agency deploys Secure SLM inside its secure data center to support caseworkers and analysts.

Summarizes case files, reports, and investigative documents
Highlights policy conflicts or procedural gaps
Staff interact with models through encrypted, internal endpoints
No citizen data ever leaves the agency’s controlled environment

Education & Academic Research Assistance

A university integrates Secure SLM into its local IT environment for faculty and research teams.

Processes large research collections and academic materials
Identifies themes, citations, and connections across datasets
Scholars query models without exposing institutional IP
Student and research data remain inside campus-owned systems

Manufacturing & Operational Productivity

A manufacturer deploys Secure SLM inside its plant network to support engineering, safety, and operations teams.

Summarizes equipment logs and maintenance documentation
Flags anomalies or early indicators of operational risk
Engineers interact with specialized models through local servers
Proprietary formulas and production data never leave facility networks

Enterprise IT & Internal Knowledge Management

A large enterprise uses Secure SLM to index internal documentation and support IT teams.

Summarizes architecture diagrams, runbooks, and service docs
Identifies configuration gaps or policy violations
Staff query models privately through secure on-prem interfaces
All internal knowledge assets stay within corporate infrastructure

Retail Inventory & Customer Insights

A major retailer deploys Secure SLM inside its private network to support merchandising and operations teams.

Summarizes product performance and customer feedback
Highlights demand trends and inventory gaps
Teams query models locally through encrypted endpoints
All sales and customer data remain within the retailer’s environment

Energy Sector Monitoring & Compliance

An energy provider uses Secure SLM to analyze field reports, regulatory filings, and maintenance records.

Summarizes inspection logs and operational data
Flags safety risks and regulatory concerns
Engineers query models through secure, on-premise execution
Critical infrastructure data never leaves the utility’s control

Telecom Network Analysis & Support

A telecom operator integrates Secure SLM into its internal IT systems to support engineers and customer operations.

Summarizes network events and service tickets
Identifies outage patterns and system bottlenecks
Staff query models through local, encrypted interfaces
Subscriber and network data remain inside telecom-owned systems

Cybersecurity Threat Review & Triage

A security operations team runs Secure SLM on local appliances to support analysts and incident responders.

Summarizes threat reports and SIEM event logs
Flags suspicious patterns and attack indicators
Analysts query models through secure, isolated execution
No sensitive security telemetry is ever sent to third-party clouds

Logistics & Supply Chain Optimization

A logistics provider deploys Secure SLM inside its private network for routing, planning, and operations.

Summarizes shipping reports and fleet data
Identifies delays, inefficiencies, or route risks
Teams query models from secure, internal systems
All operational data remains within the company’s infrastructure

Real Estate & Property Insights

A real estate firm uses Secure SLM to analyze documents, listings, and investment summaries.

Summarizes contracts, inspections, and property histories
Highlights risks, valuation signals, or key deal terms
Agents and analysts query models from local servers
Client and financial data stay entirely within firm-owned systems

HR & Workforce Management Support

A large enterprise deploys Secure SLM internally to streamline HR, compliance, and talent operations.

Summarizes policies, employee files, and workforce reports
Identifies compliance risks or training gaps
HR teams query models through private, internal endpoints
Employee data never leaves the organization’s secured environment

Whether you are a financial institution, government agency, or multinational enterprise, Secure SLM brings AI to your data not the other way around.

Team

AskSLM is built by a battle-tested founding team and a high-velocity engineering group focused on turning cutting-edge AI into secure, deployable systems.

Clint House

Co-Founder & CEO

Previously founded and sold an AI/virtual wellness tech company, bringing deep experience in building scalable architectures and high-velocity product teams. A veteran tech entrepreneur with 20+ years of experience and multiple exits, Clint has led and scaled ventures across healthcare and deep tech, consistently commercializing breakthrough technologies in highly regulated environments. Now focused on building the decentralized AI infrastructure platform for secure, private AI.

Zurab Tutberidze

Co-Founder & CTO

A world-class systems engineer and runtime architect with deep expertise in C++, model optimization, and high-performance computing. Over his 20+ year career, Zurab has built nation-scale secure systems and ultra-low-latency execution engines, leading engineering teams in mission-critical environments. At AskSLM he owns the full inference stack, driving firmware, runtime architecture, and hardware–software co-design to deliver faster, safer, more efficient enterprise-grade AI systems.

Mariam Mekarishvili

Fullstack Developer

Fullstack engineer working across React, Node.js, and UI/UX. Mariam owns the consoles and workflows that make complex on-premise AI feel simple for enterprise users.

Giorgi Levidze

Research & PM

Leads research, project management, and technical documentation. Giorgi turns vision and architecture into clear roadmaps, specs, and artifacts the whole team can execute on.

George Goglodze

DevOps Engineer

DevOps engineer responsible for secure, reproducible deployments across on-prem and appliance environments, keeping the inference stack fast, observable, and resilient.

Thomas Struntz

C++ Engineer

C++ engineer focused on low-level performance, concurrency, and memory optimization in the inference engine and runtime.

Alejandro Barkasz

C++ Engineer

C++ engineer working on model execution pipelines, hardware integration, and cross-platform runtime stability for demanding enterprise workloads.

Saksham Adhikari

ML Engineer

ML engineer responsible for training pipelines, model evaluation, and optimization of small language models tailored to regulated-industry workloads.

Development Philosophy

We ship enterprise-grade AI through disciplined Agile execution, tight CEO/CTO collaboration, and an obsessive focus on real-world performance.

Our development philosophy centers on disciplined Agile execution with tight sprint cycles and direct leadership involvement to ensure high-impact prioritization.

We deliver in tight, shippable increments, breaking work into clear epics → stories → tasks to maintain high velocity and alignment. Core IP the inference engine, runtime, training pipeline, and SLM architecture is built entirely in-house, while open-source components are used only for non-strategic layers like auth, billing, and UI.

We optimize for rapid iteration, real-world performance, and continuous delivery over theoretical perfection. This lets us consistently outperform the market and move products from concept to enterprise-grade deployment with remarkable efficiency.

How We Build

Disciplined Agile sprints with direct CEO/CTO collaboration.
Clear epics → stories → tasks for ruthless prioritization.
All core IP (engine, runtime, training, SLMs) developed in-house.
Open source limited to non-strategic layers (auth, billing, UI).
Performance benchmarks tied to real-world workloads, not synthetic tests.
Continuous delivery and hardening toward enterprise-grade deployments.

Bring the Future of Secure, Local AI Into Your Organization

Ready to see how to deliver AI with the governance, privacy, and assurance modern enterprises demand? Request a demo to experience the difference and evaluate its impact firsthand.

Contact

Prefer email? Reach out to our team to discuss your requirements, security posture, and deployment options.

Email: [email protected]

Location: Austin, TX

Founders: Clint House & Zurab Tutberidze

Why Teams Choose Secure SLM

Full ownership of data and models.
Designed for strict regulatory environments.
Predictable, cost-efficient AI deployments.