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Enterprise AI,
On Your Terms

Bring GenAI into production

without risking your data, compliance, or IP.

FlashQuery is the control plane that lets product and engineering teams deploy copilots, RAG pipelines, and automation within your own environment; on-prem, in a private cloud, or with secure open-weight models like Llama 4.

Built for enterprises that can’t risk public cloud LLMs.

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With security, accuracy, and governance built in from day one, FlashQuery makes GenAI deployment scalable, compliant, and fully under your control.

From Prototype to Production — Without the Expensive Roadblocks

95% of enterprise GenAI projects never make it to production.

Half fail before they ever leave the lab, not because the models don’t work, but because deploying them into production is the hard part. Most teams hit the same wall:

Data can’t leave the firewall

This blocks deployment when sensitive information is involved.

Compliance stops projects cold

Without built-in governance and auditability, production isn’t possible.

Performance breaks under real workloads

Models that look great in demos often fail to scale in real-world conditions

Infrastructure doesn’t scale

Brittle pipelines and manual workarounds make deployment costly and slow.

FlashQuery removes those barriers

It’s the control plane for deploying secure, production-grade GenAI inside the software you build, without compromising data security, compliance, or flexibility.

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Core Features

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Deploy Anywhere

Run on-prem or in a private cloud so sensitive data never leaves your environment.

Security & Compliance Built In

Governance, observability, and audit-ready controls from day one.

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Model-Agnostic Architecture

Use open-weight or commercial models and switch anytime without lock-in.

From POC to Production, Fast

No brittle integrations. No re-architecture. Just launch in weeks, not months.

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Why Enterprise Teams Trust FlashQuery to Power GenAI

Deploy Fast, Without DevOps Drag

Launch assistants in hours—not weeks. FlashQuery spins up quickly and integrates with your stack.

Used for fast-moving POCs and pilot deployments.

Purpose-Built for RAG & Assistants

Manage knowledge bases, prompts, and retrieval logic in one place.

Power internal documentation bots, support agents, or product features with your own data.

Enterprise-Ready from Day One

SSO, audit logging, tenant isolation, and prompt versioning come built-in.

Used in security-first environments like finance, healthcare, and regulated SaaS.

One Unified Layer for Your AI Stack

FlashQuery sits between your apps, your data, and your models—so you can focus on results, not infrastructure.

Simplifies delivery of AI across multiple apps or business units.

Built for Enterprise AI That’s Auditable, Contained, and Ready to Scale

Designed for teams building real products—not just demos. For those who care about control, observability, and getting to production.

Deployment & Infrastructure

Containerized deployment

MCP support

Secure user and tenant-level data isolation

Security & Access Control

Role-based access control

SSO authentication

Audit logs for all actions

Data Management & Retrieval

Private vector database support

Retrieval-ready data ingestion

AI & Orchestration

Multi-LLM orchestration

Prompt versioning with history

Customization & Extensibility

Custom routing logic

Plugin framework for extensions

What People Are Saying About GenAI Challenges

Generative AI’s potential is undeniable, but the path to enterprise production is full of hurdles. FlashQuery exists to help teams solve the hard problems of trust, governance, and scale.

If we can't fully customize or govern the AI to fit our needs, it feels like we're letting someone else decide how our business should run. That's a level of risk I'm not comfortable with.

OpenAI is our brain, but tomorrow Google may come up with something — or another company. If we go all-in with GPT enterprise, or go with Co-pilot, we'll be locked into one ecosystem. DON'T WANT THIS!

Foundational models are not suitable for enterprise use, says Brian Demitros, innovation lead for data and technology at advertising network Dentsu. “They’re trained on the internet and often contain innacurate or misleading information,” he says. “You can’t simply use them out of the box and expect a level of accuracy needed to support critical decision-making. Customization is critical to get value out of them.

Seven in 10 executives believe enterprise use of generative AI means exposing company data to new security risks.

Organizations are seeing a dramatic rise in informal adoption of gen AI – tools and platforms used without official sanctioning.

Many organizations considering AI implementations are concerned about the potential loss of corporate data, a risk that weighs heavily on the minds of buyers.

More than three-quarters of CIOs in our survey reported they are concerned about the security risks of AI.

IT leaders can expect data issues, compliance hurdles and technology coordination chores when scaling generative AI.

There are a number of issues to solve before you go from pilot to scale... It's important to just set the frame and say, 'We see the honeymoon phase being over.’

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