Enterprise AI solutions & cloud pipelines
Deploy private, self-hosted LLM clusters, encrypted vector databases, and custom high-throughput enterprise processing pipelines.
The bottleneck vs. The ScrapAX solution
Public cloud AI APIs introduce data privacy and cost unpredictability
Sending proprietary customer data to third-party public AI providers creates compliance risks under GDPR, HIPAA, and corporate governance rules. Additionally, usage-based token pricing creates unpredictable monthly operating expenses as volume grows.
Private, sovereign AI pipelines with deterministic boundaries
ScrapAX designs and deploys air-gapped or private VPC AI infrastructure. We deploy open-weights models and private vector stores that run entirely within your secure infrastructure boundaries, ensuring complete data sovereignty and zero external leaks.
What the architecture delivers
Private VPC LLM deployments
Host dedicated open-weights models with zero external telemetry or third-party training usage.
Air-gapped vector infrastructure
Self-hosted vector stores ensuring internal documents and customer PII never touch public clouds.
Deterministic guardrails
Enforce strict structured outputs, schema verification, and automated compliance auditing.
High-throughput batch queues
Scalable asynchronous worker pools capable of processing millions of records without throttling.
ENGINEERED OPERATIONAL OUTCOMES
Guaranteed compliance with regulatory and data privacy frameworks
Predictable fixed compute costs instead of runaway per-token API fees
Absolute data isolation and enterprise-grade SLA reliability
Industry deployment scenarios
Illustrative architectures mapped to real business operational requirements.
Analyze sensitive patient documentation and clinical notes within HIPAA-compliant private environments.
Automate portfolio reporting, transaction classification, and compliance reviews without external exposure.
Extract obligations, liabilities, and critical covenants across thousands of proprietary enterprise agreements.
Simulate Enterprise AI in action
Security assessment
VPC model setup
Vector pipeline sync
Production cluster
Illustrative simulation. No websites are scanned and no live data is processed.
How we take enterprise ai to production
Discover & Audit
Pinpoint friction & evaluate feasibilityWe analyze your team’s repetitive workflows, assess existing API endpoints and data sources, and define clear measurable ROI targets before writing a single line of code.
Architect & Blueprint
Define data models & error boundariesMap every trigger, AI decision boundary, and fallback condition. We design human-in-the-loop checkpoints so your team always retains control over high-consequence actions.
Engineer & Test
Build resilient pipelines with test suitesDevelop the automation scripts, fine-tune prompts against actual edge cases, and execute rigorous load tests. All workflows are stress-tested against messy real-world data.
Deploy & Monitor
Continuous uptime & ongoing optimizationSeamless deployment into your environment with full documentation, team onboarding, automated health-checks, and proactive monitoring to ensure 99.9% reliability.
Technical runbooks & implementation standards
Define the job before choosing the technology
A useful enterprise ai solutions project starts with a clear description of the work. Identify who owns the input, what a good output looks like, and where that output will go next. Bring a few representative examples, including a difficult one. A simple written scope makes it easier to see whether the proposed solution actually addresses the problem. It also exposes assumptions before they turn into implementation choices. The first conversation should establish what matters to your team, not promise an outcome that has not been measured.
Choose a small, reviewable starting point
Begin with one process, one source group or one set of customer questions rather than trying to automate everything at once. A limited scope lets the team compare outputs with the current approach and inspect mistakes. Decide which cases are in scope and which should remain manual. Record any access requirements and identify the person who can approve changes. If the underlying information is incomplete, improving it may be more useful than adding another tool. Expand only when the initial solution has a clear purpose and an agreed way to evaluate it.
Make exceptions part of the design
Real inputs rarely follow the perfect example. A required field can be missing, a connected tool can change, or a request can be outside the expected category. Write down how those cases should behave. Some should stop and ask for review; others can be retried through a controlled process. Someone needs to be able to understand what happened without guessing. For sensitive or consequential steps, an explicit human approval is preferable to an invisible assumption. Exceptions are not a reason to avoid automation; they are a reason to design its boundaries carefully.
Review quality before extending coverage
Use the agreed examples to check the result. Look for completeness, consistency and whether the output reaches the right person or system. Record any limitations in plain language. Do not treat a successful demonstration as proof that every possible input will work. Review unfamiliar cases separately, and keep a path for someone to report a problem. The useful measures depend on the workflow: they might include valid fields, correct routing or supported answers. Compare against your own baseline rather than borrowing an impressive statistic from another business.
Plan the handover and ongoing responsibility
Before launch, agree who owns the connected accounts, where the workflow is documented and who reviews changes. Clarify which support and maintenance tasks are included in the project. A change to a source page, knowledge article or integration can alter the behavior of the system. Your team should know how to request an update and where to send unresolved questions. Commercial scope, data handling and support terms should be confirmed with a person at ScrapAX. The published pricing is a starting point for that conversation, not a substitute for a written agreement.
Frequently asked questions
What open-weights models do you deploy?
We deploy state-of-the-art open-weights models including Llama 3, Mistral, Qwen, and custom fine-tuned domain models tailored to your requirements.
Where is the infrastructure hosted?
Models and databases can be deployed directly into your existing AWS, Google Cloud, Azure VPC, or on-premises GPU hardware.
Does our data ever train third-party models?
Never. Our enterprise architectures guarantee that your data remains strictly isolated within your private infrastructure boundaries.
What compute resources are required?
We size GPU and CPU compute instances based on your throughput and latency targets, optimizing with quantization and vLLM acceleration.
How do we arrange an NDA and security review?
Contact our enterprise team directly. We routinely execute mutual NDAs and provide detailed architectural documentation for security compliance reviews.
Let’s scope your enterprise ai architecture.
Bring the problem. We’ll help map the next step.