Agentic AI Solutions

Drive decision intelligence with autonomous AI Agents

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Enhance efficiency and speed with autonomous AI Agents

Modern enterprises are held back by fragmented workflows, static RPA tools, and decision bottlenecks. Traditional automation fails in dynamic environments where the complexity outpaces hardcoded rules. Sigmoid’s Agentic AI Services enable the creation of intelligent, goal-driven agents that operate autonomously across data ecosystems. These are autonomous software entities that make context-aware decisions, execute actions toward defined goals, and may incorporate feedback or learning mechanisms over time, paving the way for real-time optimization, intelligent decisioning, and adaptive workflows. Whether it's personalizing customer interactions, streamlining R&D, or autonomously managing supply chains, our agents move you from reactive automation to true AI-powered autonomy.

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What we offer: Enterprise-grade Agentic AI solutions

Consulting Services for Agentic AI

We help enterprises make informed decisions around Agentic AI adoption. Our consulting services span platform evaluation, automation strategy, and architecture planning. At the core is RAPID, our proprietary framework that provides the backbone to scale agents across business functions.

Key focus areas:

  • Evaluate agentic platforms to align with enterprise needs
  • Define automation and human-AI augmentation strategies
  • Design scalable AI systems with the RAPID framework

Productionizing AI Agents at Scale

Moving beyond PoCs, we specialize in operationalizing AI agents across real-world use cases. We help set up AgentOps (agent operations), implement governance structures, and enable continuous improvement of agentic systems, ensuring they remain adaptable and enterprise-ready.

Key focus areas:

  • Set up AIOps for monitoring, governance, and versioning
  • Scale modular agents across use cases
  • Enable PoC-to-production transition with low tech debt

Bespoke AI Agent Development

We build custom AI agents tailored to your domain, tasks, and operational environment. Whether it’s a co-pilot for sales, a research assistant, or a workflow optimizer, our development process ensures domain-aligned intelligence, adaptability, and ease of integration.

Key focus areas:

  • Build domain-specific, context-aware agents
  • Develop task-based agents for research, extraction, and interaction
  • Integrate with apps, APIs, and business workflows

Ready to deploy AI Agents

We offer pre-built, enterprise-grade AI agents that are production-ready and customizable. Designed to solve common business challenges, they accelerate deployment and minimize the need for ground-up development. Built on our RAPID framework, they’re modular, extensible, and integration-ready.

Key focus areas:

  • Plug-and-play agents for business needs
  • Quick customization for domain and workflows
  • Seamless integration with enterprise systems

AI Agents by Industry

Consumer Packaged Goods (CPG)

Product recommendation agent

Product recommendation agent

Recommends product ingredients based on consumer feedback, enabling quicker product development aligned with market demand.

Marketing Content at Scale

Marketing Content at Scale

Creates insight-driven marketing content at scale for campaigns across products with rapid content generation.

Product Claims Generation

Product Claims Generation

Generates compliant product claims from consumer feedback to support faster marketing approvals.

Social Sentiment Analysis

Social Sentiment Analysis

Analyzes social media data to extract sentiment, identify emerging trends, and surface product feedback in real-time.

Banking & Financial Services (BFS)

ESG goals & KPIs settings

Profitability Prediction

Predicts account-level profitability for loans, enabling targeted segmentation and pricing strategies.

LLM Validation

FS Model Validator

Validates and benchmarks large language models across financial services use cases, such as credit risk summarization and regulatory report drafting.

Bank Fair Lending Compliance

Bank Fair Lending Compliance

Agentic AI Chatbot trained to analyze HMDA data. The tool can provide insights on the HMDA reports and provide comparison on a 1:1 basis or among peer groups.

Lifesciences

Automated Marketing Content Generation

Automated Marketing Content Generation

Generates marketing content from technical and scientific documents to support campaign creation and knowledge transfer.

Intelligent Search for R&D

Intelligent Search for R&D

Enables semantic search across design history files and R&D documents to accelerate research and documentation workflows.

Label Extraction and Automation

Label Extraction and Automation

Extracts product attributes from labels to support regulatory reporting and compliance.

Clinical Trial Report Summarization

Clinical Trial Report Summarization

Converts lengthy trial documents into concise summaries for internal teams, external partners, and regulatory filings.

Sigmoid’s Agentic AI framework

Sigmoid’s Agentic AI framework is designed to support intelligent, goal-driven agents capable of operating autonomously across enterprise workflows. It comprises a robust agent execution engine, memory modules for maintaining both short- and long-term context, and seamless interfaces for interaction with other agents or human users.

Customer success stories

Insights and perspectives

Blog Data-driven revenue growth management

Rise of Agentic AI in supply chain networks for consumer goods

Blog Data modernization for CPG

Winning at influencer marketing with Agentic AI

blog On-shelf availability for CPGs

Building a smarter approach to evaluate the best AI Models for real business impact

Customer testimonials

Absence of a clear data strategy hurting your brand?

Build a robust strategy that infuses agility and resilience across the CPG value chain to maximize market impact.

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FAQs

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Agentic AI refers to autonomous systems that can break down high-level goals into actionable steps, prioritize tasks, reason through decisions, and execute them across multiple tools or environments. These agents use large language models (LLMs) for contextual understanding, vector databases for memory and recall, orchestration frameworks for stepwise reasoning, and APIs for tool use. To implement such agents, modern frameworks like LangGraph (by LangChain), Microsoft’s AutoGen, OpenAI’s Agents SDK, Semantic Kernel, and CrewAI offer robust capabilities for managing multi-step reasoning, tool integration, and inter-agent communication. Some advanced agents are designed with reflection mechanisms that allow them to assess outcomes, self-correct, and rerun workflows when goals are not met. While not all agents have this capability, it can significantly enhance effectiveness in complex workflows.

Enterprises often lack unified, task-specific datasets required for autonomous planning. Most business data is siloed across CRM, ERP, and communication tools, limiting contextual awareness. Additionally, integrating external APIs and internal tools into a secure and reliable execution environment is complex. Teams also struggle with aligning agent behaviors to business rules, handling exceptions, and avoiding hallucinations in decision-making. Governance, traceability, and human oversight become harder as agents scale. Finally, most enterprises lack modular frameworks and reusable logic libraries necessary to generalize Agentic AI across departments or use cases.

Traditional rule-based automation executes predefined if-then-else logic, making it brittle in edge cases or unexpected scenarios. It’s ideal for structured, repetitive tasks like invoice matching or data extraction. In contrast, Agentic AI can interpret ambiguous instructions, decide which tools to use, adapt in real time, and revise steps if outcomes deviate. For example, while rule-based bots might fail on a dynamic web form, Agentic AI can re-evaluate DOM structures, search documentation, or trigger support workflows. It can also chain multiple systems (e.g., CRM + email + knowledge base) autonomously to complete a broader business process.

Enterprises need semantically labeled datasets from systems like CRMs, ERPs, chat logs, and emails to provide agents with operational context. Vector databases (e.g., Pinecone, Weaviate) are essential for memory and semantic search. A secure, containerized environment is required to run LLMs, tools, and web agents in orchestration. Robust APIs and connectors must expose internal services. Event-driven architectures (e.g., Kafka, Pub/Sub) help trigger agent workflows. Observability tools are needed for logging, tracing, and human override. Fine-tuning or retrieval-augmented generation (RAG) pipelines should be set up to ensure domain-specific grounding and guardrail enforcement.