All-in-One Gen AI Stack With Deployable Modules
Choose what to automate, analyze, or accelerate, Factspan’s FLUX offers pre-built modules across the Gen AI lifecycle. Covering use cases from SEO content and natural language queries to agentic lead scoring and LLM evaluation, each FLUX module is production-ready and built to scale.
Flux AI Studio – GenAI JumpStart Solution.
Flux AI Studio offers powerful accelerators that enhance data processing, content creation, customer support, and development workflows. These GenAI-powered tools using LLMs streamline operations, enabling businesses to integrate automation seamlessly and improve efficiency. It also serves as a GenAI playground, enabling enterprises to experiment with use cases, evaluate outcomes, and develop bespoke tools tailored to their workflows.
FactiLLM Copilot: Enterprise Insights Engine
FactiLLM Copilot offers accelerators that empower organizations to extract, analyze, and act on business insights. These LLM-powered copilots enhance decision-making by automating analytics, forecasting, and compliance, driving smarter business intelligence and workflow optimization.
FLUX LLM Evaluate: Gen AI Benchmarking Suite
FLUX LLM Evaluate helps teams compare, optimize, and productionize large language models across business scenarios. Whether you're templating prompts, running experiments, or benchmarking outputs, Evaluate gives you the structure and tools to confidently select the right model for your enterprise use case.
FLUX Agentic AI: Purpose-Built Agents Tuned for Enterprise Scale
FLUX Agentic AI delivers autonomous agents tailored for high-impact enterprise functions. These agents combine domain expertise, data integration, and LLM intelligence to act, adapt, and deliver insights in real time, reducing manual effort and accelerating decision cycles.
Success Stories
Success Stories
Frequently Asked Questions
Find answers to common questions about our FLUX AI products and services
1. Can Flux AI Studio modules be integrated with existing enterprise platforms?
Yes, each tool is designed with APIs or plug-ins that allow seamless integration with CRMs, data warehouses, and support systems. This enables enterprises to embed GenAI capabilities into their current workflows without overhaul.
2. How customizable are the outputs of tools like WriteRight or Dialogue Digest?
The outputs can be tuned using prompt templates and custom vocabularies. Enterprises can define tone, format, and language preferences for brand consistency.
3. Do tools like DataDecoder and MoodMetrics AI support multilingual data?
Yes, several tools in Flux AI Studio are built to handle multilingual inputs and can be extended to support language-specific tokenization and summarization. This makes them usable across global datasets.
4. What level of user permissions or governance is available?
Flux AI Studio supports role-based access control, audit logs, and usage analytics. These features help teams manage access, track tool usage, and ensure compliance with internal policies.
1. Can FactiLLM Copilots be fine-tuned on company-specific data?
Yes, copilots can be adapted using internal datasets and ontologies to improve accuracy and relevance. Fine-tuning ensures outputs align with domain-specific context and terminology.
2. How frequently are these copilots updated with new models or data patterns?
Copilots are updated periodically based on model advancements and evolving business needs. They also support continuous learning from user feedback and system logs.
3. Is it possible to run copilots on-premise for data-sensitive industries?
Yes, enterprises in finance, legal, or healthcare can deploy copilots in secure, on-premise environments. Deployment flexibility ensures compliance with data governance regulations.
4 .How do these copilots handle conflicting or incomplete data?
They apply reasoning techniques and fallback logic using LLMs to resolve ambiguity. Outputs can also include confidence scores or alternate interpretations when data is inconclusive.
1. Does LLM Evaluate support commercial as well as open-source models?
Yes, it’s model-agnostic and supports commercial APIs like OpenAI, Anthropic, as well as open models like Mistral or Llama2. This allows side-by-side testing across a diverse model set.
2. Can we benchmark models using real enterprise data rather than canned prompts?
Absolutely. Teams can upload domain-specific prompts, documents, and tasks to evaluate model performance under realistic workloads.
3. Is there a way to version control prompts and evaluation metrics?
Yes, Evaluate includes built-in support for prompt versioning, test case tracking, and metric comparison over time. This enables auditability and continuous improvement.
4. How are evaluation results visualized or shared across teams?
Results can be exported or viewed in dashboards showing latency, accuracy, cost, and other key metrics. These can be shared via links or embedded into internal portals.
1. How do FLUX Agents connect with real-time enterprise data sources?
Agents can connect via APIs, message queues, or direct data pipelines to ingest and act on live data streams. This enables real-time decision-making and autonomous action.
2. Are FLUX Agents able to trigger downstream workflows or alerts?
Yes, agents can initiate actions like sending alerts, creating tickets, or updating dashboards. Their behavior can be configured via workflows or rules-based orchestration.
3. Can multiple agents collaborate or hand off tasks to each other?
Agents can be orchestrated to work in tandem, e.g., a Marketing Agent generating leads, which are then qualified by a Sales Agent. This agentic chaining helps build powerful workflows.
4. How is trust, safety, and escalation managed in agentic actions?
Agents include guardrails, approval checkpoints, and fallback mechanisms. Sensitive tasks can be routed for human review before execution.