10Pearls

10pearls.com

10Pearls is a technology consulting and delivery provider rather than a research publisher or software-license vendor. Buyers can engage it for architecture and planning, defined implementation projects, dedicated development teams, or ongoing technical services. Its AI offering includes separately documented advisory and implementation services: opportunity prioritization and governance planning are distinct from building models, integrating assistants, and operating deployed systems. The software practice also covers conventional applications, cloud infrastructure, and data platforms that do not inherently involve AI. ^[1] ^[2] ^[3] ^[4] ^[5]

Services and delivery

AI strategy, readiness, and governance advisory

Advisory. Assesses AI readiness and prioritizes use cases by value, effort, and risk. Deliverables include foundation-gap analysis, operating-model assessment, KPI-linked business cases, adoption plans, model-selection guidance, and an MVP-to-scale delivery plan. These are planning services, not deployed systems. ^[3]

Custom machine-learning development and MLOps

Implementation. Builds customer-specific models for churn, segmentation, recommendations, forecasting, predictive maintenance, fraud scoring, document processing, and computer vision. Delivery includes platform integration and MLOps pipelines for deployment, monitoring, versioning, and governance. ^[4]

Generative-AI integration and agent development

Implementation. Integrates foundation models with enterprise data, retrieval pipelines, and existing applications. Scope includes knowledge-grounded conversational agents, agent orchestration, middleware and API preparation, permissions, prompt filtering, and governance controls. ^[5]

Generative-AI monitoring and support

Managed services. Supports deployed generative-AI systems through model-performance monitoring, automated drift detection, and observability of prompts, outputs, latency, and accuracy. Contract-specific support coverage and service levels require confirmation. ^[5]

Custom application development

Implementation. Develops enterprise applications, web portals, multi-tenant SaaS, mobile applications, and API integrations, with requirements analysis, architecture, testing, and support. Supported application technologies include React, Angular, Vue, React Native, and Flutter. ^[1]

Cloud application and deployment engineering

Implementation. Builds cloud applications and deployment infrastructure using AWS, Azure, or Google Cloud. Documented technologies include Kubernetes, infrastructure as code, and CI/CD. ^[1]

Data platform and pipeline engineering

Implementation. Implements Snowflake data platforms, Databricks lakehouses, ETL/ELT and streaming pipelines, data governance, and business-intelligence models. This offering concerns data infrastructure and analytics; it does not by itself establish AI delivery. ^[1]

Software architecture and modernization advisory

Advisory. Provides enterprise and application architecture strategy, phased modernization and re-platforming plans, cloud architecture, API integration strategy, and DevSecOps design. Outputs are designs and roadmaps; implementation requires a separately defined scope. ^[2]

Cybersecurity assessment and advisory

Advisory. Assesses security environments and identifies vulnerabilities across applications and cloud systems. Buyers should distinguish assessment findings and recommendations from contracted remediation work. ^[6]

Security engineering and remediation

Implementation. Implements vulnerability remediation, cloud and application security, DevSecOps controls, identity controls, and custom security integrations. ^[6]

Managed cybersecurity

Managed services. Offers ongoing security services, including incident monitoring and response, with scope ranging from task-specific support to fully managed arrangements. ^[6]

Appian application and workflow implementation

Implementation. Builds Appian applications and workflows with system and API integrations. Delivery includes testing, phased deployment, rollback planning, and post-go-live support. ^[7]

ServiceNow ITSM and CSM implementation

Implementation. A documented healthcare engagement supports ServiceNow ITSM modernization, custom CSM implementation, and Agent Workspace optimization to unify agent case workflows. This evidence establishes platform implementation, not AI delivery. ^[8]

Company details

Technology and platforms

The generative-AI service discusses integration with Amazon Bedrock, Azure’s Copilot ecosystem, Google’s AI stack, Oracle Fusion workflows, Salesforce, ServiceNow, Snowflake, and Databricks. These describe integration contexts, not evidence that every platform is included in every engagement. ^[5]

Engagement and delivery

The software-development page distinguishes client-led dedicated teams from project-based delivery, where 10Pearls is accountable for agreed deliverables. End-to-end engagements use shared ownership with 10Pearls accountable for outcomes. Buyers should specify decision rights, dependencies, acceptance criteria, and operational handover in the contract. ^[1]

Track record

For Related Faces, 10Pearls reports developing facial-recognition matching using machine learning, metadata comparisons, and cosine similarity. It reports 94% matching accuracy and processing time reduced to four minutes. The supplied evidence does not establish the evaluation dataset or testing protocol. ^[9]

For an unnamed clinical-education organization, 10Pearls built a white-label RAG assistant using AWS, OpenAI models and embeddings, Pinecone, LangGraph, Redis, and Langfuse. It reports 75% faster access to transplant information, relevance above 90%, and more than ten hospital instances. These are firm-reported access and relevance outcomes, not independently validated clinical efficacy. ^[10]

For an unnamed hospital network, 10Pearls implemented pre- and post-operative patient outreach using Amazon Connect, Amazon Lex, and Amazon Bedrock. The system collected symptoms, supported triage, and escalated high-risk responses to clinicians. Reported workload and response-time improvements are qualitative. ^[11]

Scope and limitations

This profile uses only the supplied research notes. They establish commercial service scope, not binding pricing, staffing commitments, delivery timelines, or service levels.
Some case-study customers are unnamed, and reported outcomes lack independent validation in the supplied evidence. Reference access and measurement methods require verification.
The evidence supports custom AI delivery but does not establish a separately licensed proprietary AI product.
The Google Cloud service page contained mismatched introductory text in the supplied research. Those passages were excluded; cloud capability here rests on the software-development evidence.
The supplied notes do not establish current office locations, certifications, or leadership details.

Questions to ask

Which delivery model applies, and who owns architecture decisions, acceptance testing, dependencies, and final outcomes?
Can you provide references for comparable production deployments and explain the datasets and evaluation methods behind reported results?
Who owns source code, trained models, prompts, data pipelines, and deployment assets, and which third-party licenses create recurring costs?
What human-review, access-control, escalation, and audit requirements will apply to AI outputs and agent actions?
What post-launch monitoring, incident response, model maintenance, and support service levels are included?
Which platform versions and integrations are in scope, and what documentation and handover will support a future provider transition?

Overview