Private Cloud Deployment

The Sovereign
Enterprise Brain.

Off-the-shelf AI leaks your IP. We engineer Custom, Fine-Tuned LLMs that live exclusively on your servers.

Train on your proprietary data. Own the weights. Control the infrastructure.

DEPLOY ON:
AWS Bedrock Azure OpenAI On-Premise
TRAINING_CLUSTER_01
FINE-TUNING ACTIVE
DATA SOURCES
SharePoint Salesforce Jira
Custom Model Weights Llama-3-70B-Instruct (Quantized)
SECURE OUTPUT
Enterprise Knowledge API
128k Context Window
0.00% Data Leakage
System Architectures

Beyond Standard
Chatbots.

We don't just "prompt" models. We engineer complex cognitive architectures. Choose the build that fits your operational bottleneck.

SYS_01: KNOWLEDGE_RETRIEVAL

The Corporate Brain (RAG)

Connects LLMs to your unstructured data (PDFs, SharePoint, Wikis). It cites sources and never hallucinates outside your corpus.

Vector Embeddings (Pinecone/Weaviate)
Semantic Search & Re-ranking
Citation-Backed Answers
SYS_02: MULTI_AGENT_SWARM

The Analyst Swarm

A team of specialized agents (Researcher, Writer, Reviewer) that collaborate to produce complex reports or codebases.

Hierarchical Task Delegation
Self-Reflection & Error Correction
Async Parallel Processing
SYS_03: ACTION_MODEL

The Ops Controller

An agent granted permission to do things. It connects to your ERP/CRM APIs to execute trades, update records, or book logistics.

Function Calling (Tool Use)
Human-in-the-Loop Approval
API Authentication Handling
CI/CD Pipeline

From Raw Data to
Sovereign Model.

We don't just "wrap" an API. We curate your unstructured data, fine-tune open weights (Llama/Mistral), and deliver a Dockerized container that runs on your metal.

BUILT ON:
PyTorch Python Docker HuggingFace
01

Data Hygiene & Sanitization

We ingest your PDFs, SQL dumps, and emails. Our scripts scrub PII and format the data into .jsonl pairs for training.

02

Supervised Fine-Tuning (SFT)

We train the model on your specific domain language. This turns a generic model into a specialist that understands your acronyms.

03

Red Teaming (RLHF)

Our engineers attack the model to find exploits. We use Reinforcement Learning to align the model against toxic or hallucinatory outputs.

Air-Gapped Handoff

We deliver the final model weights and inference engine as a Docker image. You deploy it on your private servers. We leave.

Deployment Scenarios

Don't Rent Intelligence.
Own It.

Generic models are "Jack of all trades, master of none." We fine-tune smaller, efficient models (7B-70B params) to outperform GPT-4 on your specific vertical tasks.

R&D Acceleration

Ingest decades of internal PDFs, patents, and lab reports to create a "Scientist Assistant" that creates new hypotheses.

Public AI: Hallucinates formulas; leaks IP.
Custom Build: Trained ONLY on validated internal data. Air-gapped.

Internal DevOps Copilot

A code-generation model fine-tuned on your legacy codebase, ensuring it follows your strict linting and security standards.

Public AI: Writes generic, insecure code.
Custom Build: Understands your private repos & libraries.

The "Super-Agent"

Automate Tier 1 & 2 support with a model that knows your inventory, refund policies, and user history in real-time.

Public AI: Generic empathy; zero system access.
Custom Build: Action-enabled to process refunds via API.

The Scale Advantage

At 1M+ requests/month, owning a fine-tuned model is 60% cheaper than paying OpenAI API tokens.

See Cost Analysis →
CTO / CIO FAQ

Technical
Due Diligence.

Answers regarding infrastructure, SLAs, model licensing, and post-deployment support.

Reference Architecture

Download our AWS/Azure deployment topology diagram.

Get PDF Spec Sheet →

Require an Air-Gapped demo?

Contact Solutions Architect
1. Can we deploy this in our own AWS/Azure VPC?

Yes (BYOC Model). We support "Bring Your Own Cloud." We deliver the model weights and inference containers (Docker/Kubernetes) to your private VPC. No data ever leaves your perimeter.

2. Do you offer "Air-Gapped" (Offline) deployment?

Yes. For Defense and Banking clients, we deploy models on local hardware (e.g., NVIDIA DGX or H100 clusters) with zero internet connectivity required for inference.

3. Who owns the fine-tuned model weights?

You do. Unlike OpenAI fine-tuning (where you rent the model), we hand over the `.pt` or `.safetensors` weight files. It is your Intellectual Property.

4. How do you prevent the model from "leaking" data?

We use RAG with strict permissioning (ACLs). The model does not "memorize" your documents during training; it retrieves them at runtime based on the user's existing access level (e.g., a Junior Analyst cannot query CEO-only documents).

5. What happens when Llama 4 or GPT-5 comes out?

Our architecture is Model Agnostic. We build the pipeline (Data Cleaning -> Vector DB -> Prompting) to be modular. Swapping the underlying LLM (e.g., Llama 3 to Llama 4) typically takes less than 48 hours.

6. Do you provide SLAs and Maintenance?

Yes. We offer Enterprise Support packages with 99.9% Uptime SLAs, 1-hour critical response times, and quarterly model re-training/calibration.

7. How do you handle "Hallucinations" in critical workflows?

We implement a "Verification Layer." For critical tasks (like code generation or financial logic), the model generates a solution, and a secondary "Critic Agent" or deterministic code interpreter verifies the output before showing it to the user.

Build your sovereign brain.

Stop renting intelligence. Start building assets. Book a technical discovery call with our Lead AI Engineer.

Initialize Project Non-Disclosure Agreement (NDA) available upon request.
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