Services
Development of highly customized AI copilots that integrate deeply with proprietary, bespoke internal software systems, acting as intelligent overlays on legacy databases and custom ERPs. Sub-services include custom enterprise copilot development, AI sidebars for legacy ERP systems, conversational interfaces for proprietary data warehouses, and secure internal AI assistant deployment.
Development of AI copilots that integrate directly into proprietary, bespoke ERP systems like SAP, Oracle, or custom-built platforms, enabling natural language querying of complex business logic and data without requiring a full system migration.
Engineering of AI-powered conversational layers for proprietary data warehouses (e.g., Snowflake, BigQuery, Teradata) that allow business users to query complex datasets in plain English, generating SQL and visualizations on-demand.
Creation of intelligent AI overlays for custom, in-house software applications, adding copilot functionality to legacy or niche systems where off-the-shelf AI solutions cannot integrate, preserving the core codebase.
End-to-end deployment of secure, air-gapped AI assistants for internal enterprise use, ensuring all data, models, and inference remain within the corporate network to meet strict data sovereignty and IP protection requirements.
Integration of AI copilots with enterprise knowledge management systems (e.g., Confluence, SharePoint, proprietary wikis) to create a single, conversational point of access for tribal knowledge, policies, and procedural documentation.
Design and implementation of AI copilots that automate and orchestrate complex, multi-step internal business processes by interacting with multiple enterprise APIs and databases, reducing manual handoffs.
Building of highly specialized AI assistants trained on niche, proprietary corporate data (e.g., pharmaceutical research, aerospace engineering) to provide expert-level guidance and reduce reliance on scarce subject matter experts.
Development of enterprise copilots that process and reason across text, images, diagrams, and scanned documents from internal systems, enabling comprehensive analysis of mixed-format corporate data.
Architecture of unified AI copilot systems that provide a consistent assistant experience across disparate enterprise platforms (web, desktop, mobile) while maintaining a single, coherent context and memory.
Implementation of advanced AI-powered search across all internal data silos—from databases and file shares to emails and chat logs—using semantic search and RAG to deliver precise, context-aware answers.
Engineering of AI copilots that analyze internal data streams, historical trends, and external signals to provide real-time, data-driven recommendations for strategic business decisions, from pricing to resource allocation.
Building of specialized copilots for data analysts and scientists that can understand intent, generate and debug complex code (SQL, Python), and automate data cleaning and visualization pipelines.
Fine-tuning and continuous training of AI models (e.g., GPT, Llama) on an organization's unique internal data, jargon, and processes to create copilots with unparalleled accuracy and domain relevance.
Specialized deployment of AI copilots for finance, healthcare, and government sectors, with built-in audit trails, compliance checks (HIPAA, FINRA), and human-in-the-loop controls to meet stringent regulatory mandates.
Integration of voice interaction capabilities into enterprise AI assistants, enabling hands-free operation in industrial settings, call centers, or for accessibility, with secure, on-premises speech processing.
Development of internal platforms that allow non-technical business teams to build, customize, and deploy their own department-specific AI copilots using a governed, secure low-code framework.
Augmentation of existing BI platforms (Tableau, Power BI) with AI copilots that can explain charts, suggest new analyses, and automatically generate narrative insights from dashboards.
Embedding of AI copilots into collaborative work hubs (Microsoft Teams, Slack, custom platforms) to summarize discussions, assign action items, and retrieve relevant documents during live meetings and chats.
Strategic use of AI copilot interfaces as a modernization layer for legacy mainframe or client-server applications, extending their lifespan and usability without costly, risky re-platforming projects.
Engineering of robust, scalable backend integrations that connect AI copilots to hundreds of internal and external REST, GraphQL, and SOAP APIs, enabling them to take actionable steps within enterprise ecosystems.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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