Our Services
Explore our end-to-end solutions for secure, scalable, and responsible AI.
AI Safety and Security
We design and implement AI gateways that protect your data and GenAI deployments through centralized governance across all models in your enterprise. With support for any model endpoint, including Azure OpenAI, Amazon Bedrock on AWS, and Meta Llama 3, our AI Gateway allows you to tap into cutting-edge innovations through a single, powerful query interface which eliminates the need to maintain separate systems to manage your AI traffic. Enterprises can effortlessly switch between foundation and custom models, ensuring uninterrupted pipelines and simplified maintenance.
With the AI Gateway you can easily test and swap new models without requiring changes to your codebase. The gateway offers AI token usage control, tracking and allocation with role-based access control enforced to prevent sensitive data leaks through GenAI chatbots and AI agents. Additionally, with real-time spending insights, our AI Gateway keeps your finances in check and your operations streamlined.
AI Project Management
Navigating artificial intelligence projects requires specialized expertise. Our AI project managers bring precise oversight to complex innovation initiatives, ensuring successful implementation while maintaining strategic alignment with your business goals. From initial concept through development to deployment, we provide the leadership needed to transform AI potential into measurable business value.
We specialize in:
- AI strategy development: Crafting realistic roadmaps that align AI capabilities with business objectives.
- Cross-functional coordination: Bridging gaps between data scientists, developers, and business stakeholders.
- Technical risk management: Identifying and mitigating challenges unique to AI implementation.
- Resource optimization: Balancing computational needs, data requirements, and human expertise efficiently.
- Ethical governance: Ensuring AI applications adhere to regulatory standards and responsible AI principles.
- Performance tracking: Establishing meaningful metrics to validate AI project success and ROI.
Our AI project managers combine deep technical understanding with practical business acumen to guide your organization through the complexities of artificial intelligence adoption. We maintain strict budget control, create realistic timelines, and ensure continuous stakeholder alignment throughout the project lifecycle. With our expertise, your AI initiatives deliver tangible business outcomes while minimizing implementation risk
AI Applications Security and Performance Gateways
AI gateways provide a single point of control for your organization to access AI services via APIs in the public domain and broker secure connectivity between your different applications and third-party AI APIs both within and outside your organization’s infrastructure. It acts as the gatekeeper for data and instructions that flow between those components. The AI Gateway provides policies to centrally manage and control the use of AI APIs with your applications, as well as key analytics and insights to help you make decisions faster on Large Language Model (LLM) choices.
We architect and implement AI Gateways for our clients that are equipped with advanced capabilities tailored specifically to the unique complexities of LLM-based AI:
Our AI Gateway encompasses several key functionalities:
Security
Security
- Token-Based Rate Limiting: Controls the rate of requests to AI services, preventing abuse and managing resource utilization.
- Prompt Protection: Ensures that prompts sent to LLMs do not contain sensitive or inappropriate content, safeguarding against unintended data exposure.
- Content Moderation: Monitors and filters responses from AI models to prevent the dissemination of harmful or non-compliant information.
Observability
Observability
- Usage Tracking: Monitors token consumption and provides insights into how AI services are utilized, aiding in cost management and capacity planning.
- Logging and Auditing: Maintains detailed records of AI interactions, supporting compliance and facilitating troubleshooting.
- Real-time Monitoring: Tracks LLM response times, error rates, and API usage patterns to ensure optimal performance.
Prompt Engineering
Prompt Engineering
- Retrieval-Augmented Generation (RAG): Enhances prompts with relevant data to improve the quality and accuracy of AI responses.
- Prompt Decorators and Templates: Standardizes and enriches prompts to ensure consistency and effectiveness across different AI applications.
- Dynamic Context Injection: Automatically enhances user queries with contextual data to improve AI-generated responses.
Reliability
Reliability
- Multi-LLM Load Balancing: Distributes requests across multiple AI models to optimize performance and prevent overloading.
- Retry and Fallback Mechanisms: Implements strategies to handle AI service failures gracefully, ensuring uninterrupted user experiences.
- Traffic Prioritization: Routes high-priority requests to the most reliable AI services while deferring less critical tasks.
Model Context Protocol (MCP) Servers
MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.
The need for MCP servers arises from the challenges in managing massive volumes of data scattered across various sources. Enterprises often struggle with integrating and effectively using this data, especially when it’s siloed in different systems. MCP servers provide an effective solution for ensuring that LLMs get the right data at the right time, reducing the chances of AI hallucinations and other errors.
As enterprises increasingly adopt generative AI, the volume and variety of data these AI systems require can be overwhelming. Without a standard protocol, the necessity for custom integration with each new data source creates a significant scaling bottleneck.
The Model Context Protocol (MCP) offers a simple, open standard to establish secure, bi-directional communication between AI systems and the underlying data that they require. Data is made accessible via MCP servers, and AI apps (MCP clients) consume data through these MCP servers.
We work with you to setup and maintain your business’s MCP server to enable you to secure and monetize your business data through AI.
MCP Server Use Cases
Securely exposing enterprise databases (e.g., CRM, ERP, ecommerce, product catalogs)
Instead of AI apps directly accessing sensitive data, which poses security risks and requires complex, custom integrations, they connect to a single MCP server.
Federating access to multiple data silos
An MCP server can act as a semantic data layer across disparate systems and provide a unified interface.
Integrating with APIs and external services
MCP Servers can act as gateways to internal and external APIs (e.g., exchange rates, stock market data, geocoding).
Exposing domain-specific information
MCP Servers can provide access to curated datasets, enabling more accurate and contextual responses
Enabling access to AI tools and functions
E.g., trigger workflows in Salesforce, Workday, or SAP via an MCP endpoint
Ensure data privacy and compliance
Centralized governance ensures masking, tokenization, auditing, and access controls.