AI, On Your Terms

Enterprise-grade privacy and performance with self-hosted open-source models, intelligent RAG, fine-tuning, and agents—deployed on client infrastructure.

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WHY

Privacy as a Strategic Pillar

IP, customer data, and proprietary processes must be protected from third-party training and opaque data handling.

Risk Mitigation

Public AI introduces data-egress, vendor lock-in, and limited observability that conflict with sovereignty and compliance at scale.

Production Imperative

Enterprises are moving from pilots to production and require verifiable governance and auditability to capture durable value.

Data Sovereignty

Data, prompts, logs, and model weights remain inside the organization with full auditability and policy-aligned governance.

WHAT

Self-Hosted AI Infrastructure

We deploy and operate open-weight LLMs on client cloud or on-prem, with encryption and network isolation.

AI Architecture

Complete In-Network Systems

Retrieval, vector/graph stores, model gateways, and observability live inside the client's VPC or data center with governed connectors to documents, data warehouses, and line-of-business systems.

  • Encrypted memory with Nitro Enclaves
  • Remote attestation and measured boot
  • Role-based access and retention controls

Auditability

Prompt, context, retrieval, and output logs are captured with role-based access controls.

Confidential Compute

Support for CPU/GPU TEEs and confidential accelerators on major clouds.

Compliance

Aligns to sectoral regimes such as GDPR/HIPAA expectations.

HOW

Open-Source, Enterprise-Ready

We use top open-weights like Qwen 3, DeepSeek V3, GLM 4.5, and Kimi K2, then tailor with fine-tuning, RAG, and agents.

Private RAG

Hybrid or graph retrieval with citation enforcement and runtime hallucination detection.

Secure Fine-Tuning

PEFT/LoRA pipelines run inside client environments to align outputs with domain terminology.

Agentic Workflows

Integrate with ERP/CRM and internal systems under policy controls.

Routing Policy

Models selected by sensitivity, performance, and cost with uniform logging.

Model Performance Benchmarks

Our open-weight models deliver frontier-grade capability without proprietary APIs.

Reasoning 93%
Coding 89%
Multilingual 87%
Qwen 3
0.6B to 235B-A22B
DeepSeek V3
671B MoE
GLM 4.5
Text & VL variants
Kimi K2
~32B activated
IMPACT

Measurable Outcomes

Faster, safer decisions and lower unit costs with citable outputs and confidential inference.

Financial Services

40–60% AHT reduction and higher first-contact resolution for RAG voice agents.

Problem: High AHT, compliance logging gaps, and fragmented knowledge across systems.

Solution: RAG-enabled call-center and knowledge agents with conversation-level logging and redaction.

Enterprise Operations

Faster cycles and fewer errors with traceable automations.

Problem: Manual RFQs, reporting, and back-office processes across ERP/CRM cause delays and errors.

Solution: Agent workflows with policy controls and granular logs.

Healthcare

Higher answer quality with traceable references suitable for clinical governance.

Problem: Need for citable, safe clinical answers with provenance.

Solution: Radiology-validated RAG patterns with source citation and audit trails.

Knowledge Search

Improved accuracy and decision speed with citable outputs.

Problem: Stale or non-citable responses undermine trust.

Solution: Hybrid/graph retrieval with evaluation harnesses.

SECURITY

Architecture & Confidentiality

Verifiable isolation and encrypted memory with Nitro Enclaves and H100 Confidential Computing.

Reference Stack

  • Model Gateway
  • Retrieval Tier
  • Vector/Graph Store
  • Policy Engine
  • Observability Stack

Platform Options

  • In-VPC/on-prem deployment
  • CPU/GPU TEEs
  • Confidential accelerators
  • Remote attestation
  • Measured boot

Trust Badges

No Data Leaves Your Infrastructure
Confidential Inference Enabled
Citations On by Default
MODELS

Open-Source Weights We Deploy

Models are selected by sensitivity, performance, and cost, with uniform logging and guardrails.

Q3

Qwen 3

Family of dense and MoE open-weights from 0.6B to 235B-A22B with thinking/non-thinking modes.

Strong coding Math Multilingual Expert tool-use
DS

DeepSeek V3

671B MoE with about 37B activated per token, MLA attention, and multi-token prediction.

Competitive reasoning Coding SFT/RL stages
GLM

GLM 4.5

Text and vision-language variants with reasoning features and strong multimodal performance.

Image understanding Document analysis GUI understanding
KK

Kimi K2

Open-weight trillion-param MoE with around 32B activated per token.

Agentic Coding Reasoning
PLAN

90-Day Implementation

Precision/coverage/latency harnesses with regression testing for updates to retrieval, fine-tuning, or guardrails.

Weeks 0-2

Discovery & Risk Map

Prioritize 1-2 workflows, define KPIs (accuracy, latency, unit cost, citation compliance).

Weeks 2-6

Sovereign LLM + RAG

Deploy in-VPC with citations and hallucination detection, wire to governed connectors.

Weeks 6-12

First Agent Workflow

Integrate with ERP/CRM, enforce policy controls, demonstrate measurable improvements.

Ready to deploy AI on your terms?

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