Generative AI Integration Services
10Pearls in numbers
1,400+
experts across
100+ technologies
700+
solutions
deployed
225+
enterprise clients,
including Fortune 500
20+
years of
enterprise impact
OPERATIONALIZE GEN-AI ACROSS YOUR ENTERPRISE
Most GenAI initiatives fail not at the model layer, but where AI has to meet enterprise systems, data environments, and governance requirements. We bridge the GenAI deployment gaps through our legacy modernization experience, cloud and data platform expertise, and custom development capabilities. Guided by our regulated industry experience, this allows us to deliver secure, compliant, and resilient GenAI integrations into enterprise workflows that evolve alongside changing processes instead of becoming friction points.
Generative AI integration challenges we solve
Stalled GenAI pilots
GenAI solutions built and tested in isolation don’t scale and deliver in production, resulting in lost or reduced ROI, costly rework, and AI adoption delays.
Architectural alignment
Existing data architectures weren’t built for GenAI requirements of data availability and pre-processing, making alignment resource intensive.
Security & governance
GenAI solutions must perform within existing security and governance boundaries, or they introduce new vulnerabilities and compliance risk.
Misaligned initiatives
Narrowing alignment to model fine-tuning leads to misaligned solutions that underperform in production and increase hallucination risk.
Technical debt & cost
Stalled or misaligned GenAI deployments compound the cost and technical debt across multiple initiatives, discouraging further investment.
Our generative AI integration services
GenAI strategy & readiness assessment
Enable high-impact GenAI decisions by providing clarity on value alignment, adoption challenges, and a risk-aware integration strategy, with use-case prioritization.
LLM & foundation model integration
Support AI-enabled operations through context-aware, governed, architecturally-aligned, and token-efficient integration of both open source and proprietary models.
Workflow & process
automation
Enhance productivity and operational efficiency through complex AI-augmented automations integrated into the relevant process, permission, and data layers.
Data pipeline & analytics integration
Minimize hallucinations and enhance context awareness and governance of GenAI deployments by integrating secure, real-time data pipelines, vector retrieval, and lineage.
Conversational AI
integration
Enhance engagement quality and resolution rates of conversational AI agents by grounding their responses in enterprise knowledge and policies for deep context-awareness.
Agentic AI integration & alignment
Support complex agent orchestration and frictionless execution of chained agentic workflows through process alignment and adequate permissions at the integration layer.
Continuous monitoring, MLOps & support
Maintain model performance with automated drift detection and Observability across prompts, outputs, latency, and accuracy. We also offer outcome-focused GenAI support.
AI security & governance integration
Minimize GenAI adoption risk by implementing AI-specific security and governance controls, IAM permissions for agents, prompt filtration, and building explainability layers.
Legacy modernization for
AI
Reduce modernization risk and cost by preparing legacy enterprise systems for GenAI integration through custom middleware and APIs while preserving business continuity.
Why enterprises choose us for generative AI integration
Diverse platform expertise
Our certified AWS, GCP, Azure, Salesforce, Snowflake, and other platform experts support GenAI integrations through native tooling and connectors.
Enterprise-grade security
As an ISO 27001 certified partner, we take a security and governance-first approach to integration, API, and custom middleware development.
End-to-end AI capabilities
From fine-tuning to agentic AI orchestration, our end-to-end AI capabilities enable GenAI integrations that support and streamline AI adoption.
Domain-specific integrations
Our deep understanding of industry-specific operations and domain-specific processes enables nuanced, value-aligned, and compliant integrations.
Global delivery & support
With delivery centers across four continents, we offer multiple engagement models and time-zone aligned support for our integration services.
Modernization expertise
Our ability to handle complex generative AI integrations stems from our deep understanding of legacy systems and data architecture constraints.
We support GenAI integration across multiple enterprise platforms
Our GenAI integration tech stack
Foundation model providers
- OpenAI
- Anthropic
- Meta
- Mistral
- Cohere
- Amazon
- Microsoft
Data &
infrastructure
- Databricks
- Snowflake
- PostgreSQL
- MongoDB
- Kafka
- Dbt
- Airflow
- Kubernetes
- Docker
- Terraform
AI frameworks & tooling
- LangChain
- LlamaIndex
- Hugging Face
- CrewAI
- MCP
- Pinecone
- MLflow
- Vector DBs
Enterprise
applications
- Angular Salesforce
- ServiceNow
- Microsoft 365
- SAP
- Workday
Programming languages & engineering stack
- Python
- TypeScript
- Java
- .NET
- Node.js
- Go
Cloud
environments
- AWS
- Azure
- Google Cloud
- OC
Enabling enterprise AI integration across every industry
How we deliver AI integration services
AI readiness & integration strategy
We assess the GenAI solution or model and evaluate data ecosystems, target processes, digital architecture, and enterprise systems for integration readiness to develop an optimal strategy.
Integration architecture & enablement
Workflow & operational alignment
From misaligned workflows to inappropriate IAM permissions, several integration challenges can emerge between business processes and operating environments that we identify and address.
Generative AI Integration & deployment
We integrate generative AI solutions and models after testing them for performance, permissions, and other critical operational parameters, making real-time adjustments as needed.
Governance, monitoring & optimization
We build governance controls and observability into the GenAI integrations to monitor for drift and performance, while continuously optimizing for cost, resource efficiency, and alignment.
Our GenAI case studies
case study - fintech
De-Risking Legacy Modernization with AI-Driven Engineering
Establishing a risk-controlled foundation for legacy modernization through AI-assisted analysis, structured methodology, and validated execution patterns.
Case study - artificial intelligence
Improving Patient Care with Conversational AI
Automating routine pre- and post-operative engagement to ease clinician workload, improve responsiveness, and enhance patient experience.
Case study - artificial intelligence
Enhancing Claims Intelligence with AI-Driven Analytics
Transforming claims processing into a scalable, near real-time system that delivers accurate insights and reduces operational delays.
20 questions to gauge your AI maturity & guide your next steps in adoption!
AI Readiness Assessment
Insights
AI/ML
Shifting teams to orchestrate AI-first software delivery
AI shifts development bottlenecks and introduces context engineering, enabling smarter workflows, leaner teams, and improved productivity.
Cloud
10Pearls Achieves AWS AI Services Competency
10Pearls has achieved AWS AI Services Competency, recognizing our expertise in helping organizations build resilient cloud environments on AWS. The...
Company news
10Pearls announces strategic partnership with Novacare
10Pearls formed a strategic partnership with Novacare Hospital aiding them in AI, cloud, data, analytics, and EHR implementation to build...
AI/ML
Enterprise AI Agent Security: Lessons from the OpenAI Incident
The recent OpenAI testing incident offers a timely reminder that AI agent security needs to go beyond the model itself....
FAQs about our software development services
What are generative AI integration services?
Generative AI or Gen AI integration services cover the end-to-end lifecycle of integrating generative AI tools and models into business processes, digital architectures, data ecosystems, and enterprise platforms. 10Pearls generative AI integration services span from readiness assessments and model alignment to custom integration engineering, MLOps, and continuous monitoring.
How long does a GenAI integration project take?
A GenAI integration may take anywhere from a few weeks to a few months based on the complexity of GenAI solutions and models, legacy constraints, data quality, process architecture, and a few other factors. 10Pearls has agile processes and mature playbooks in place to expedite integration projects while maintaining strict governance.
Which enterprise platforms can you integrate GenAI with?
From hyperscalers like AWS, Azure, and GCP to CRMs like Salesforce and data platforms like Snowflake, 10Pearls can integrate GenAI with a wide range of enterprise platforms. We leverage both native integration tooling, connectors, and custom middleware and APIs for these integrations.
Is generative AI integration secure and compliant?
Yes, provided it’s handled by an AI-first engineering partner like 10Pearls that brings legacy modernization experience, platform expertise, deep regulatory knowledge, and end-to-end AI capabilities to the table.
What is the difference between GenAI development and GenAI integration?
GenAI development focuses on building solutions and applications around GenAI capabilities to meet business needs and goals. GenAI integration focuses on the deployment of these solutions and their integration with the existing digital architecture, data ecosystems, relevant enterprise systems, and business processes. As an engineering partner offering both capabilities, 10Pearls can handle significant complexity within both project types.
Can you integrate agentic AI systems into our workflows?
Yes. 10Pearls offers not just AI agent integration services, but a comprehensive range of agentic AI services, including agent development, orchestration, and AgentOps. We can integrate agentic AI systems into your workflows directly, with custom middleware, or, if needed, by modifying the agentic AI systems as per the workflow integration requirements.
How do you measure ROI on enterprise AI integration?
We evaluate ROI as the relationship between business value created, total cost of ownership, and how quickly value gets realized. We measure across three layers: business outcomes (time saved, cost avoided, risk reduced, throughput gained), full-program cost (engineering, token spend, infrastructure, governance, change management), and adoption depth. Baselines for all of this are set with you before integration begins, so the value the work produces can be measured against where you actually started.