While the AI delivers high-accuracy results, all outputs are configurable. Teams have full control to review, refine, and adjust AI-generated documents and plans before finalizing them. With multiple learning cycles, the accuracy of the output improves over time. Each AI agent in the Synapt SDLC squad is designed to collaborate with its human counterpart, enhancing productivity and ensuring high-quality outcomes.

The platform is built for ease of use. While minimal training is required, most teams find Synapt SDLC intuitive, especially since it integrates with familiar platforms like Jira, Confluence, GitHub Copilot, Visual Studio IDE, and Microsoft Copilot. Teams can stay and work within their usual tools while gaining insights and efficiencies delivered by Synapt SDLC—ensuring a smooth, disruption-free experience.

Absolutely. Synapt SDLC integrates with platforms you already use, like GitHub Copilot, Microsoft Visual Studio, Atlassian Jira, and Confluence, ensuring minimal disruption and easy adoption.

Yes, Synapt SDLC is designed to be scalable, accommodating projects of varying sizes and complexities across multiple codebases. Book a short consultation with our team to understand how Synapt SDLC can adapt to your specific project needs and growth plans.

Synapt SDLC functions by extracting knowledge from your codebase, utilizing the power of Large Language Models (LLMs) with RAG (Retrieval-Augmented Generation) extensions and programming language adapters.

It offers two deployment options to ensure security and flexibility:

1. Enterprise Cloud LLM Option– Here, an enterprise-grade LLM of your choice (like Azure OpenAI) is invoked via APIs. This setup is governed by the security infrastructure and confidentiality guarantees of the chosen LLM provider.

2. Grounded LLM Option (Synapt In-a-Box) – For organizations seeking tighter control, this option grounds and 'air gaps' the LLM within your premises. Your data and code never leave your enterprise boundaries, ensuring maximum security.

Our consultants are available to guide you through the pros and cons of each approach. Rest assured, SynaptSDLC is designed with world-class security and robustness at itscore.

Given Synapt SDLC's ability to map out and understand complex application codebases, it is particularly well-suited for brownfield projects. However, all the capabilities of Synapt SDLC—across personas—can also be utilized by greenfield or new product development teams for high productivity gains.

We would love to show you a demo of the greenfield and brownfield journeys on Synapt SDLC.

Absolutely. Synapt SDLC is designed to fit into Agile workflows, assisting with sprint planning, documentation, and quick iterative updates.

You can absolutely choose to use only the Context Generator. Synapt SDLC is designed with a modular approach, allowing you to adopt the components that best fit your needs.

Pricing for Synapt SDLC is customized based on your specific business needs, project complexity, and scale of implementation. To get a tailored quote, please contact us for a detailed discussion.

Datastreak not only moves data but also translates and optimizes code using its proprietary codebase context generator, which understands every nuance of the source code. The GenAI Code Maverick component then refactors the code into a leaner, more optimized version for the destination technology. This streamlines the complex, error-prone migration process, delivering transformative results for enterprises.

DM Pro offers real-time dashboards and automated validation tools for accuracy checks.

Yes, Code Maverick generates comprehensive test cases including edge-case scenarios.

Definitely—it’s built to tackle complexity in large enterprise data environments.

Yes, you can limit the transformation scope—Datastreak lets you select specific jobs, components, or modules.

Code Maverick auto-detects hidden interdependencies and surfaces them during the code analysis phase.

Yes, it works seamlessly with all major cloud platforms for migration.

Yes, Datastreak.AI lets you select your preferred LLM including Gemini, GPT, Mistral, or Llama2.

If the context (business and functional) is clear, accuracy is 100%. The combination of well-defined domain knowledge and LLM ensures precision.

We employ an automatic mechanism for building context using advanced indexing techniques.

• SME Review: Subject Matter Experts (SMEs) review and provide feedback.

• Feedback is used to enhance and refine the indexed data.

Yes, ASK supports connectivity with BigQuery, Snowflake, PostgreSQL, and SQL Server.

Finance, Real Estate, Retail, Healthcare, Telcos and more.

ASK deploys RBAC (Role-Based Access Control) to ensure data privacy and uses encrypted connections for database queries.

A typical implementation ranges from 4-8 weeks based on complexity.

Multi-language support is available for specific use cases and can be enabled based on requirements.

Implementation time depends on the complexity and size of your data sources but can typically be completed within a few weeks with minimal disruption to operations.

No. Synapt Search is designed to connect with existing platforms and data sources with minimal setup. It can analyze and organize data in its current state without extensive preparation.

Synapt Search continuously updates and indexes data in real-time, ensuring that the most recent information is always accessible without manual intervention.

Yes, Synapt Search offers flexible deployment options. It can be deployed on-premises or in the cloud, depending on your security and infrastructure requirements.

Synapt Search uses contextual retrieval powered by AI to understand the intent behind queries, ensuring it provides relevant results even when the input is incomplete or vague.

Yes, Synapt Search supports customizable, role-based access controls to ensure that sensitive data is only accessible to authorized users.

Yes, Synapt Search supports multi-language capabilities, making it suitable for global teams and organizations with diverse language requirements.

Synapt Search is designed for flexibility and can be extended to integrate with new data sources as required. Our team can assist with custom integrations to ensure seamless data connectivity.

Pricing for Synapt Search is customized based on factors such as data volume, integration complexity, and deployment preferences. Contact us to get a personalized quote.

Churn Agent is a solution built on ServiceNow that helps businesses identify and prevent customer churn by analyzing customer data across multiple channels. It provides valuable insights and drives human-in-loop autonomous actions to improve customer retention.

Churn Agent utilizes predictive analytics to examine customer behavior, interactions, and trends, allowing businesses to identify customers who are likely to churn before it happens.

Churn Agent gathers and analyzes data from various customer touchpoints, such as escalation history, customer service interactions, SLA breach, and engagement patterns, and feedback, offering a 360-degree view of the customer.

Retaining existing customers is often more cost-effective than acquiring new ones. By reducing churn, businesses can save on acquisition costs and ensure long-term customer loyalty, which supports revenue growth.

Churn Agent delivers real-time, data-driven recommendations to businesses, helping them send emails to engage at-risk customers. These insights ensure timely interventions to minimize churn.

Yes, Churn Agent is built on ServiceNow, which allows easy integration with other enterprise systems like CRM, customer support, and marketing platforms, making it an adaptable solution for any organization.

Absolutely! Whether you're a small business or a large enterprise, Churn Agent is designed to scale and support any organization looking to improve customer retention and reduce churn.

Churn Agent stands out by offering cross-channel data analysis by tapping customer and service data on one platform (ServiceNow). Unlike reactive solutions, Churn Agent helps businesses address churn before it occurs, using predictive analytics and real-time insights.

Businesses can start seeing results relatively quickly after implementing Churn Agent. The platform provides early insights into at-risk customers, enabling teams to take immediate actions to prevent churn, often within the first few months.

Churn Agent is designed to be user-friendly and can be quickly adopted by teams with minimal training and a short implementation cycle. Additionally, ServiceNow’s integration means teams familiar with the platform will find it easy to implement and use.

Churn Agent aggregates data from the ServiceNow single data platform, ensuring a holistic view of each customer’s journey. This cross-channel data helps predict churn more accurately.

It uses large language models to generate and transform code across languages, frameworks, and databases using plain English or uploaded files.

It automates repetitive tasks and speeds up development with accurate, context-aware code generation and conversion.

Yes. It supports Angular to React, Python to Java, and more, while preserving structure and explaining changes.

Yes. It converts queries between Oracle, Snowflake, and others with syntax adjustments and function mapping.

Yes. You can connect your own LLM via API or use supported models like GPT 4, Gemini, or Claude.

Yes. It works with both brownfield and greenfield systems and adapts to enterprise scale environments.

It provides inline comments and highlights changes in logic, syntax, or structure during code conversion.

It supports Python, Java, JavaScript, React, SQL, C Sharp, Angular, and major database dialects.

Yes. It is compliant with GDPR, SOC 2, HIPAA and includes role-based access and data guardrails.

Developers, architects, analysts, and QA teams across engineering and modernization workflows.

AI-powered software testing leverages machine learning and generative AI to automate test case generation, predict defects, perform root cause analysis, and streamline the end-to-end software testing lifecycle (STLC).

AQuA.ai uses modular AI agents and Synapt’s proprietary context engine to create accurate, context-rich test cases in minutes. Its plug-and-play setup reduces manual efforts, maintenance, and ramp-up time, accelerating QA timelines by over 80%.

Yes. AQuA.ai integrates seamlessly with tools like Jira, Confluence, and enterprise CI/CD pipelines. It supports greenfield and brownfield environments with zero disruption.

Unlike other tools, AQuA.ai offers contextual, no-code test case generation, full end-to-end traceability, root cause analysis, and multi-agent modularity—all with minimal setup and high compatibility with your current infrastructure.

No. AQuA.ai is built for business and QA users alike. You can create, manage, and regenerate test cases using plain English in a no-code interface.

Absolutely. AQuA.ai adapts to complex, large-scale legacy systems as well as new development projects. Its modular agents and flexible architecture make it deployment-ready across any project maturity level.

With its LLM-driven engine, AQuA.ai understands context from requirements, code, and configurations to generate precise, protocol-specific test cases—ensuring 100% accuracy and comprehensive test coverage.

AQuA.ai is enterprise-grade and complies with major standards like PII, GDPR, HIPAA, and PCI DSS, ensuring full data security and regulatory alignment.

AQuA.ai supports functional, non-functional, regression, integration, and system testing, with capabilities like scenario mapping, test strategy planning, and auto-generation of reusable test cases.

GenAI-based helpdesk automation leverages Generative AI to understand employee requests in natural language, automate workflows, resolve tickets instantly, and optimize internal support operations across departments like IT, HR, Finance, and Admin.

Unlike static ticketing tools, Xolve uses a multi-agent AI framework to auto-detect, auto-triage, route, resolve, and close tickets—all with full context from your proprietary data, with no manual scripting required.

Yes. Xolve integrates seamlessly with tools like Jira, Slack, Teams, Intune, JAMF, Confluence, and more. It's built for plug-and-play compatibility with your current infrastructure.

Xolve is built for all internal teams—IT, HR, Finance, Procurement, and Admin. It automates routine processes and empowers employees with self-service or instant resolution.

Xolve is enterprise-grade and fully compliant with global standards such as PII, GDPR, HIPAA, and SOC 2. It has security guardrails built-in and supports role-based access control (RBAC).

No. Xolve offers a no-code, conversational UI that lets teams create agents or workflows using plain English, without requiring any engineering effort or LLM training.

Xolve handles requests like password resets, software installation, vendor onboarding, leave balance queries, bill claims, system access requests, and more—across departments.

Xolve can be deployed in 6 weeks with your existing tech stack. Modular agents can be custom-built in minutes.

Enterprises have reported:

1. 85% drop in email-based support time

2. 45% ticket auto-resolution

3. 92% reduction in triage load

4. 3X productivity across support teams

Absolutely. Xolve’s modular framework and LLM context engine allow full customization per department, use case, and proprietary datasets—giving you a tailored automation engine.

Luna IVR uses AI-powered natural conversations to automate tier-1 queries like password resets and outage checks, resolving 50% of issues without agent involvement. Its 98%+ intent recognition ensures accurate self-service, minimizing agent intervention.

Luna IVR is an AI-powered interactive voice response system that automates tier-1 queries using Natural Language Processing and contextual understanding, unlike traditional menu-based IVRs.

Yes, Luna is designed for seamless integration with systems like Salesforce, ServiceNow, Zendesk, and custom CRMs, enabling automated ticket creation and resolution updates.

Under 6 weeks, Luna IVR can be set up for your enterprise. Enterprises see measurable results in 6 months, starting with AI call routing and scaling to 85% tier-1 automation.

Luna is built on an agentic modular framework and can be tailored for multiple industries with domain specific workflows. Luna excels for field service (utilities, telecom), high-volume contact centers (banking, healthcare), customers (E-commerce support) and enterprises with outdated IVRs seeking phased AI adoption.

Luna can handle high-volume, repetitive field service queries such as password resets, outage updates, service ETA notifications, and troubleshooting—without escalating to human agents.

Luna supports multi-language interactions and can be trained in regional dialects to improve accessibility and localization for diverse customer bases.

Luna adheres to enterprise-grade security protocols and is compliant with standards like GDPR, HIPAA, and PCI DSS, ensuring data integrity and privacy.