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How to Build a Private ChatGPT Using Open-Source Technology

I) Context and Background

At Lettria, our mission is to make artificial intelligence more accessible through open-source, specialized solutions. We aim to overcome key challenges in natural language processing to create AI systems that provide real business value while prioritizing transparency, privacy, and human collaboration.

In recent years, we've seen surging demand from enterprises seeking to leverage chatbots and virtual assistants to improve customer service, increase employee productivity, and gain insights from conversational data. AI-powered chatbots offer considerable potential benefits across an organization, from automating common customer support inquiries to providing 24/7 assistance for employees on policies and tools. Analyzing real-time dialogues can also reveal valuable trends and opportunities for improvement.

However, most organizations face critical challenges in adopting AI chatbots related to data privacy, security, and explainability. Privacy regulations like GDPR and data sensitivity around PII limit the ability to use off-the-shelf chatbot solutions built on external data. Instead, companies need customized chatbots developed on their own private data that ensure security, control, and compliance.

At Lettria, we’ve pioneered an approach that harnesses the power of open-source natural language processing models like LLaMA and BERT while avoiding the pitfalls of traditional NLP. Our methodology combines large language models (LLMs), a vector database we built to store and query document embeddings, and our own no-code machine learning platform. This enables us to develop secure, tailored chatbots that enhance human capabilities rather than competing with them.

Our solutions are designed to provide the accuracy, speed, and scale of AI while maximizing transparency, governance, and continuous human oversight. We leverage leading open-source AI technology along with our purpose-built database, linguists, and no-code tools. This allows us to deliver maximum value, customization, and data control to each client.

Over the last four years, we’ve navigated multiple AI revolutions, including the rise of transformer models and LLMs. Our team of NLP experts and engineers have delivered custom AI solutions for dozens of global clients, specializing in particularly sensitive contexts like healthcare, insurance, logistics and intellectual property. We’re dedicated to developing hybrid AI systems that combine cutting-edge techniques with time-tested linguistic approaches.

In this white paper, we’ll explore how an open-source, no-code methodology using LLMs and a vector database hosted on a private cloud can overcome key barriers to develop AI chatbots for the enterprise. We’ll highlight how our approach ensures the security, control, compliance, and human collaboration needed to drive value - especially when working with private data.

II) The Need: AI Chatbots for Customer Service and Productivity

AI-powered chatbots present considerable potential to improve customer service, increase employee productivity, and gain valuable insights for enterprises. However, serious barriers around data privacy and customization needs limit the ability to readily adopt off-the-shelf solutionxs.

Automating Support and Generating Insights

Some of the key benefits we see chatbots enabling across organizations include:

  • 24/7 Customer Support: Chatbots can provide automated assistance for common inquiries, reducing call volumes by 20-30%. This improves satisfaction with quick, accurate self-service.
  • Employee Productivity: An AI assistant can handle frequently asked questions on policies, tools, and procedures for employees. This enables faster answers so employees can stay focused.
  • Actionable Insights: Analyzing real-time dialogues and long-term trends can reveal pain points to address as well as new opportunities.

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The Need for Customized Solutions

However, serious barriers exist that prevent organizations from realizing these benefits with off-the-shelf chatbots:

  • Data Privacy Regulations: Restrictions around using customer PII and other external data limit adoption of public chatbot solutions.
  • Data Security: Highly sensitive internal data on IP, communications, and more requires chatbots to run on a private, secure cloud.
  • Customization: Chatbots need to be tailored to an organization's specific data, use cases, language, integrations, and objectives.
  • Control: Running on a company's own data with full oversight provides the control needed for business-critical applications.

A Specialized Focus on Privacy

That's why a custom-built chatbot solution leveraging open-source AI but deployed on an organization's own private cloud and data is often the ideal approach. This provides:

  • Data ownership and control: No external usage or storage of sensitive data.
  • Security: Private cloud, encryption, access controls, and other measures keep data ultra-secure.
  • Compliance: Adheres to regulations around data usage, localization requirements, and IP.
  • Accuracy: Tailored to company language and data for high relevance, precision, and recall.
  • Explainability: Full visibility into how responses are generated from the private data.

With privacy, accuracy, and control guaranteed by our specialized approach, enterprises can confidently use AI chatbots to drive real value.

III) An Open-Source Approach to Building Enterprise Chatbots

Leading organizations are increasingly exploring open-source, no-code solutions to develop customized chatbots that enhance customer and employee experiences. When implemented effectively, this approach can overcome key barriers around data privacy, security, and control.

A Modular, Cloud-Based Architecture

A modular architecture hosted on a private cloud provides the flexibility and control needed. Core components typically include:

  • API: Enables querying the system and returning responses to end users.
  • Text Generation Service: Generates natural language responses based on relevant data.
  • Embedding Service: Encodes text into semantic vector representations.
  • Vector Database: Stores and indexes document embeddings for retrieval.

Together, these microservices enable securely querying relevant data and generating tailored, natural language responses to users' questions.

Preprocessing and Encoding Private Data

The first step is ingesting and preparing an organization's proprietary documents, knowledge base articles, product specs, FAQs, and other data sources the chatbot can leverage.

Key tasks include cleaning the data, dividing it into coherent chunks, removing redundant information, and encoding the chunks into vector embeddings optimized for semantic search.

Retrieving Relevant Content

When a user asks a question, the API queries the vector database to find the chunks most relevant to the question based on semantic similarity comparisons of their vector embeddings.

Hybrid search combining embeddings and metadata filters further improves the retrieval of pertinent content.

Constructing Natural Language Responses

The text generation service reviews the retrieved documents and extracts key facts and passages useful for answering the user's question. This extracted information is synthesized into a final response in natural language.

The system is improved over time by training on real user queries and feedback. Humans refine model-generated responses to ensure quality conversations.

The Benefits of an Open, No-Code Approach

This modular, cloud-based architecture leverages leading open-source AI while remaining tailored to each organization's data, objectives, and constraints. With proper implementation, it provides:

  • Data privacy through reliance on private data sources only.
  • Customization to an organization's specific use cases, language, integrations, and goals.
  • Security via private cloud hosting, end-to-end encryption, access controls, and more.
  • Compliance with regulations around data handling and IP.
  • Accuracy from training on proprietary data vs. external corpora.
  • Explainability with visibility into data sourcing for each response.
  • Ongoing improvement via continuous retraining, monitoring, and human oversight.

With the right expertise, an open, no-code approach can overcome key barriers to deliver enterprise-grade conversational AI.

IV) Ensuring Data Privacy, Security, and Continuous Improvement

Developing responsible AI requires maintaining trust through robust data privacy, security, governance, collaboration, and continuous human oversight.

Complete Data Control Through Open Standards

An open-source methodology provides complete control and ownership over sensitive proprietary data and intellectual property used to develop AI systems. Relying on open standards and avoiding proprietary vendor lock-in ensures organizations retain full autonomy over their data.

All training data remains hosted on the organization's own secure servers rather than external systems. There is no usage or storage of private data outside the organization's control. Open technologies also grant the flexibility to fully audit algorithms and integrate innovative new techniques. This future-proofs against obsolescence, allowing the continuous integration of the latest AI advances.

Owning the full intellectual property of customized models, software, and tooling also provides a lasting competitive advantage that would be lost relying on external proprietary systems. Robust open standards maximize control, autonomy, and strategic agility.

Rigorous Cybersecurity Layers for Defense-in-Depth

Maintaining rigorous cybersecurity is critically important when working with private sensitive data like customer information, employee records, financial data, intellectual property, or other regulated information. A defense-in-depth security approach employs multiple layered controls for comprehensive protection:

  • Private cloud deployment isolates systems and data from external threats. Internal infrastructure or trusted public cloud options provide secure hosting environments.
  • End-to-end encryption secures all data in transit and at rest using organization-controlled keys. Data remains encrypted even during processing.
  • Strict access controls, VPNs, and two-factor authentication prevent any unauthorized access. Only those with validated business need can access systems.
  • Granular permission policies enforce least privilege principles, limiting data access to only what is required for each user's role.
  • Comprehensive monitoring detects threats across infrastructure and enables quick security team response. Anomalous activity is flagged for investigation.

Advanced safeguards like differential privacy, federated learning, and homomorphic encryption provide additional data protections where needed. Rigorous cybersecurity controls defend against threats while supporting innovation.

Ethical Data Governance Policies and Controls

In addition to security, responsible data governance practices are crucial for maintaining trust and upholding ethics when leveraging AI technology. Policies and controls should enforce:

  • Transparency around data usage, so consumers understand how their information is being used.
  • Explicit consent where required, allowing people to opt into certain types of data usage.
  • Limited retention periods to minimize the personal data stored long-term. Data is discarded when no longer necessary.
  • Anonymization and aggregation techniques that protect privacy when working with certain statistical data.
  • Secure international data transfers that comply with regulations around data localization.
  • Responsible incident response plans and breach notification policies to rapidly address any data exposures.

Regular internal and third-party audits should verify all policies, procedures, and controls are rigorously followed across teams and systems. Ethical data practices maintain trust while supporting innovation.

Democratized Collaboration for Continuous Improvement

A no-code AI development environment fosters continuous progress through democratized collaboration. It allows both technical and non-technical teams throughout the organization to easily participate in improving AI systems without coding expertise needed:

  • Stakeholders can review conversational logs, performance dashboards, and analytics to provide qualitative feedback based on their domain expertise.
  • Subject matter experts can submit new training data from their area to enhance model capabilities.
  • Linguists and trainers can rapidly iterate on tweaking model responses and behavior based on tests and monitoring.
  • Changes to dialog structure, integrations, responses, and more can be made quickly without engineering overheads.

Ongoing human oversight and interaction identify areas for continuous optimization and new possibilities for innovation. Retraining machine learning models on this real user data consistently improves systems over time.

Responsible open-source AI balances cutting-edge innovation with strong governance, collaboration, and human oversight. This creates securely evolving AI that maintains trust while unlocking new potential.

V) Expertise, Experience, and Pricing

Specialized Team Bridging AI Research and Business Impact

Lettria has assembled an experienced, cross-functional team of AI and NLP experts, engineers, linguists, product specialists, and business leaders. This multidisciplinary group enables us to translate cutting-edge research into tangible business value.

Our data scientists and ML engineers architect robust pipelines for data processing. They build highly scalable infrastructure for training and deploying models in production. Our experts also continuously evaluate model performance using rigorous statistical techniques to optimize accuracy, speed, and capabilities.

Linguists augment the technical team. They ensure our natural language models achieve high quality conversational abilities, understand nuance, and produce linguistically sound responses. The linguistics team plays a key role in training models to achieve the level of polish and precision enterprise applications require.

On the product side, experts focus on integrating seamless AI experiences into client workflows. They identify where automation and augmentation can provide the most value for different business processes and use cases. The product team also gathers feedback to enhance our platform’s usability and intuitive collaboration features.

Together, these specialists enable us to deliver impactful AI solutions tailored to each client's strategic objectives and constraints. Their expertise bridges the gap between exploratory research and deploying transformative yet practical real-world applications.

Years of Experience Deploying Enterprise AI at Scale

Over the last 6 years, Lettria has gained extensive experience helping global companies across industries deploy customized natural language solutions at scale. Some examples include:

  • Public Sector: Structured vast amounts of unstructured physician narrations into analyzable health records using NLP and knowledge graphs for the largest university hospital system in Europe. This improved clinical productivity and care.
  • Financial Services: Developed a virtual assistant handling 50,000 daily employee policy questions for a top 10 multinational bank. The AI assistant boosted HR productivity.
  • Telecommunications: Built multilingual customer service chatbots that improved satisfaction scores by 30% for leading North American telecom providers by automating common inquiries.
  • Retail: Uncovered emerging pain points and growth opportunities by analyzing millions of customer support interactions for a Fortune 500 retailer using NLP techniques.

These examples demonstrate our experience implementing transformative AI solutions tailored to clients' industries, use cases, and constraints at the enterprise scale.

Flexible Licensing for Accessible Cutting-Edge AI

To make our technology's capabilities accessible to organizations of all sizes, Lettria offers flexible software licensing options. Our licensing model aligns pricing predictably to each client's usage needs:

  • Usage-Based: License fees scale incrementally based on number of users and volume of text processed.
  • Predictable Pricing: Fixed monthly costs with no surprise overage fees.
  • Pay-As-You-Go: Seamlessly add licenses as needs expand.
  • Continuous Innovation: Access the latest model and platform updates.

This licensing model provides affordable access to best-in-class, enterprise-grade AI while meeting modern business needs for scalability, flexibility, and cost efficiency. Lettria democratizes the transformative capabilities of natural language technology.

VI) Conclusion

This paper has explored how an open-source, no-code approach can enable organizations to develop secure, tailored conversational AI that drives real business value.

As demand grows for AI chatbots and virtual assistants, serious barriers around data privacy, security, compliance, and control persist, especially when working with sensitive customer data, employee information, intellectual property, and other proprietary sources. Off-the-shelf chatbot solutions built on external data simply won't suffice.

That's why a methodology leveraging leading open-source natural language processing models along with purpose-built technologies hosted on an organization's own private cloud offers such a compelling path forward. With the proper implementation expertise, this approach provides the accuracy, security, and exclusivity needed while avoiding vendor lock-in.

Lettria's holistic solution methodology combines the best of open-source AI and human collaboration through our technologies and services:

  • Fine-tuning powerful models like Falcon and Flan-T5 exclusively on a client's data optimizes relevance while maintaining complete data control.
  • Our vector database and APIs enable ultra-efficient document retrieval to construct responses using a company's own knowledge.
  • Our no-code platform democratizes usage while ensuring oversight, governance, and continuous human guidance.
  • Ongoing monitoring, feedback loops, and retraining drive continuous enhancement.

We believe AI should aim to empower employees and customers, not replace them. Our solutions augment human capabilities and leverage expertise through trusted partnerships. We make AI not just cutting-edge but also ethical, useful, and human-centric.

Organizations seeking to implement secure, trusted conversational AI tailored to their strategic goals should connect with Lettria. Our team of experts looks forward to collaborating with you on building customized natural language solutions that drive real returns on investment through the responsible use of data and AI.

Please reach out for a personalized demo of our no-code platform and to discuss how we can help you overcome key barriers to build your own open-source ChatGPT and deploy it on your private cloud.


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