Power your business with accurate and explainable Conversational AI

What is Conversational AI?

Conversational AI are technologies such as virtual agents and chatbots that enable machines to engage in human-like interactions by understanding, processing, and generating natural language. It goes beyond traditional rule-based chatbots by leveraging advanced capabilities, such as LLMs, to deliver intelligent, contextual, and dynamic conversations.

Core functions of Conversational AI

• Natural Language Understanding (NLU): Interprets user input by identifying intents, extracting entities, and deciphering meaning, forming the foundation for accurate and meaningful responses.
• Natural Language Generation (NLG): Crafts contextually relevant and human-like replies, ensuring fluid and engaging interactions.
• Multi-turn dialogue management: Maintains conversational flow and coherence, allowing for clarification or follow-up even with incomplete or ambiguous input.
• Context retention and personalization: Tracks and remembers user preferences, past interactions, and conversational context to provide tailored and relevant responses.
• API integration and automation: Seamlessly integrates with external systems, enabling tasks like retrieving documents, accessing databases, or executing workflows in real time.

Challenges in building Conversational AI

1. Biases and hallucinations

LLMs are trained on vast datasets that may contain biases, stereotypes, or misinformation. These biases can inadvertently surface in generated responses, leading to reputational risks and ethical concerns, particularly in highly regulated industries like healthcare, finance, and law. Additionally, LLMs can produce “hallucinations”— generating incorrect or non-factual information—compromising trust in the system.

2. Complex workflows

Developing and deploying Conversational AI often requires orchestrating multiple tools and technologies, from natural language processing engines and dialogue managers to API integrations and user interfaces. This fragmented ecosystem complicates development, slows time-to-market, and creates challenges in ensuring smooth integration with existing business systems and workflows.

3. High costs

Crafting high-quality Conversational AI involves substantial investments in several areas:

• Fine-tuning models: Tailoring LLMs to specific industries or use cases requires significant compute and expertise.
• Infrastructure: Hosting and scaling models require powerful hardware, such as GPUs or TPUs, and reliable cloud services.
• Maintenance and updates: Keeping the AI relevant and functional requires continuous monitoring, retraining, and updating based on user feedback and evolving requirements.

Building trusted Conversational AI with SeekrFlow

SeekrFlow provides enterprises with an effortless way to tailor AI models to their unique needs by infusing information such as industry regulations, company guidelines, organizational values, etc. directly into the model. It works by ingesting and structuring user-supplied data and instructions into a dataset the model can process, enhancing understanding through self-critiquing, question generation, and incorporating iterative feedback from human experts to refine accuracy. This workflow produces high-quality, aligned data ready for fine-tuning, resulting in models that improve their relevance to specific business needs by 6x. To ensure transparency and model optimization, SeekrFlow provides rich explainability tools including token-level confidence and side-by-side comparisons.

SeekrFlow provides greater control over model behavior through its input parameters. These features enable fine-tuning of outputs by balancing creativity, accuracy, and response length, ensuring every interaction is aligned with intended outcomes. This level of customization allows for the creation of responses that consistently meet defined standards and use cases.

stream = client.chat.completions.create(
    model=ft_deploy.id,
    messages=[
        {"role": "system", "content": "You are an AI assistant trained on airline policies, ready to answer questions related to flight operations, customer service, and travel regulations."},
        {"role": "user", "content": "What are the baggage restrictions for international flights?"}
    ],
    stream=True,
    max_tokens=1024,
)
 
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="")

A practical example of Conversational AI

We fine-tuned a conversational AI model using an airline’s policy guidelines to serve as a customer service chatbot and then evaluated its performance against a base model. The fine-tuned model (left) produced an accurate, policy-compliant response tailored to the airline’s specific needs, whereas the base model (right) generated inaccurate, higher token count response, demonstrating a lack of alignment with the required guidelines.

Vertical-specific use cases

From enhancing customer service to streamlining workflows, Conversational AI solves unique challenges across industries. Here are a few examples of how it applies to specific verticals.

1. Healthcare

Personalized treatment plans
Combine patient data, research insights, and clinical guidelines to create tailored treatment recommendations and improving care quality.

2. Financial Services

Risk and compliance agent
Analyze complex regulatory documents and answers questions on compliance, reducing legal risk and ensuring adherence to the latest industry standards and policies.

3. Government

Regulatory compliance expert
Interpret and explain complex government policies and regulations, helping teams stay informed and compliant with the latest standards.

4. Legal

Research assistant
Support legal professionals to access and interpret relevant case law, statutes, and regulations, saving time in research and improving the efficiency of case preparation.

Case Study: How OneValley used conversational AI to enhance user experience and grow revenue

OneValley, a global platform supporting entrepreneurs, used SeekrFlow to build, validate, and deploy Haystack, a custom AI-powered product recommendation chat assistant that reduced the time it took entrepreneurs to find the right tools to scale their business.

Why choose SeekrFlow?

Enhance accuracy and trust

Build AI models that align with your organizational guidelines, values, and goals. Models trained with SeekrFlow result in results in 3x more accurate responses, ensuring reliability in AI-driven interactions.

Reduce development complexity

Simplify AI development by integrating seamlessly into existing workflows through a single API or SDK, eliminating the need for disjointed tools and processes.

Prioritize data security

SeekrFlow safeguards your data through strict compliance with industry best practices. Flexible deployment options allow it to run wherever your data is safest—whether on a commercial cloud, private cloud, or on-premises infrastructure.