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Your Data. Your Infra. Our Intelligence.

Stop renting your intelligence. ANVEN AI is a sovereign LLM deployed strictly behind your firewall — turning proprietary data into an absolute competitive business advantage.

Sovereign AI · Local LLM hosting · Zero data exfiltration
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Stop renting your intelligence

Public LLM APIs mean your data leaves your walls. ANVEN AI flips that.

A private, on-prem model that turns your own data into a lasting business advantage.

Sovereign & Private

Deployed strictly behind your firewall. Zero data exfiltration and full ownership of model weights.

Efficient by Design

Quantized (Int4/BF16) models cut operating cost by up to 90% versus baseline inference.

Grounded & Reliable

RAG-native architecture reports data as absent rather than fabricating an answer.

90%Cost reduction via quantization
95%Deterministic, fully-cited accuracy
400+App integrations via LangChain
0Data exfiltration
The sovereign architecture

A digital fortress — not a rented API.

Run powerful AI inside your existing infrastructure perimeter. Deploy across AWS (PrivateLink), Azure (VNet) and GCP to avoid vendor lock-in and satisfy jurisdictional data mandates.

Corporate Network ANVEN CORE VPC · Private Subnets · No Internet Gateway mTLSKMS EnvelopeIAM/RBACWORM immutable audit trails Public InternetBLOCKED

Core concept

Run powerful AI within your existing infrastructure perimeter with zero data exfiltration.

Network isolation

VPC deployment with private subnets and no internet gateway. Supports AWS PrivateLink, Azure VNet and GCP.

Security stack

mTLS in transit, KMS envelope encryption at rest, IAM/RBAC with no long-lived keys, and WORM immutable audit trails.

One model family

Many capabilities

ANVEN-1 · Efficiency

1B/3B parameters, native BF16 and quantized Int4. General text generation and instruction following, optimised for mobile, edge, web apps and ERPs via ExecuTorch.

ANVEN-1.1 · Capability

Adds visual identification, image logic and captioning via late-fusion multimodal processing — ideal for automated document auditing.

Agentic Workflows

Orchestrated through LangChain: autonomous tool selection across 400+ apps (Slack, SAP and more), retrieval-augmented reasoning and persisted memory.

Under the hood

Engineering guardrailed intelligence

The process

Queries are converted to vector embeddings and matched via similarity search against private corporate data.

Strict grounding

The model is forced to check internal knowledge bases before generating a response.

Eliminating hallucinations

If the requested data does not exist in the vector DB, ANVEN AI reports the absence rather than fabricating a result — with verified output and citations.

The mechanism

ANVEN AI + QLoRA ingests proprietary linguistic patterns, bypassing full-parameter retraining.

Business impact

Token-overhead mitigation significantly reduces input token count — yielding up to a 60% reduction in operational costs.

Structural clarity

Niche enterprise nomenclature is injected directly into structural weights for precise data flow and logical integrity.

Quantization funnel

SpinQuant and QAT + LoRA compress a 109B-parameter, ~800GB (5 GPU) baseline to ~55GB on a single GPU with under 1% accuracy degradation.

TCO reduction drivers

Request batching for maximum GPU saturation, Flash Attention for throughput, semantic caching to bypass redundancy and spot instances for async processing.

Up to 90% lower cost

Efficiency engineering that keeps enterprise AI economically sustainable.

The framework

Optimised for PyTorch ExecuTorch — high-performance inference directly on Android and iOS via the XNNPack backend.

Quantization methods

SpinQuant post-training quantization with outlier suppression; QAT + LoRA for ultra-low latency.

The result

Int4 weight / Int8 activation — drastically reduced memory and power with near-native accuracy.

Image input & analysis

ANVEN AI receives visual data; visual logic analyses image content and structure.

Structured output

Structured text is generated from the analysis and persisted for durability.

Visual Q&A & tagging

Interactive visual question answering and automatic asset tagging with metadata and categories.

Architecture of trust

Eradicating AI technical debt.

Rushing to deploy public APIs creates compounding interest in bugs, drift and security exposure. ANVEN AI replaces probabilistic chaos with a deterministic, governed asset.

  • Application evaluation — unit testing for LLMs via the ANVEN AI API against deterministic ground truth
  • LLM-as-a-Judge — automated pipelines measuring accuracy and helpfulness before production
  • Rollback & observability — immutable WORM logs and deep OpenTelemetry integration
Query: supplier contract renewal termsRAG
Answer with 3 citations from private vector storeCited
Query: 2019 audit findings (not in knowledge base)Absent
“No data found” — no fabricated answerSafe
Migration framework

From legacy APIs to ANVEN AI — in four gates

01

Assessment

Audit API patterns, map token throughput and establish p99 latency baselines.

02

Proof of Concept

Select an inference provider and pilot with ANVEN Prompt Ops to adapt prompt chains automatically.

03

Incremental

Canary deployments, traffic splitting and validation using pre-configured Terraform modules.

04

Optimisation

Full transition with dynamic request batching and Int4 quantization.

Own your intelligence

Across every regulated industry

Healthcare

Multimodal reading of medical imaging alongside patient records — privacy-first.

Finance & Banking

Fraud detection and PII-safe cross-table relational data discovery.

Government & Defence

Air-gapped compliance for critical infrastructure and classified telemetry.

Enterprise SaaS / HR

Near-zero-latency intent classification and automated data extraction into ERPs.

Own your intelligence

Book an ANVEN AI briefing and see how sovereign AI can run inside your perimeter.