Artificial Intelligence Software Development Company
10Pearls is an artificial intelligence development company with over two decades of enterprise delivery experience. Our end-to-end AI expertise covers agentic and generative AI, data engineering, enablement, integration, AI-driven automations, MLOps, and governance.
Trusted by leading global enterprises
Our achievements
700+
projects completed
225+
enterprise clients
20+ years
of enterprise-grade solutions
10+
Industries with AI deployments
An artificial intelligence software development company designs, engineers, and integrates AI systems built around an organization’s data, infrastructure, and business goals. These services turn AI models into solutions that operate reliably inside enterprise workflows and establish the architecture needed to run, govern, and scale them.
FROM AI PILOTS TO GOVERNED PRODUCTION SYSTEMS
10Pearls builds production-grade AI systems, along with the engineering capabilities needed for
seamless adoption and evolution alongside your business needs. We identify high-value use cases, design AI systems around your constraints, and address the organizational change AI requires, so AI delivers measurable value instead of a promising demo.
As a governance-focused artificial intelligence software development company, we build to current AI
standards and regulatory expectations from day one, instead of retrofitting them later. Two decades of regulated industry experience informs our governance-first architectures, with auditability and observability controls built into AI systems from day one.
Why AI initiatives fail to deliver ROI
The reasons AI projects fail are almost consistent across industries. Organizations spend months on AI pilots only to see them stall before reaching production.
Disconnected data ecosystems
The quality of training data influences how good a model can be (among other things), and the same principle applies to data that AI systems run on. Low-quality, fragmented, and siloed data can result in unreliable outputs and misaligned agent actions.
Poor model governance
Without proper model governance, monitoring, drift detection, and retraining pipelines, model degradation can lead to unreliable outputs and agent behavior, and ultimately, compliance risks.
No production-ready architecture
AI pilots are run and tested in highly structured environments which may not faithfully reflect the traffic, latency, and constraints of the production stage. So many pilots, when they are taken live, fail because they can't handle the messy data and reliability demands of production.
Misalignment with business outcomes
A technology-first approach to AI initiatives that ignore business priorities may lead to AI systems that perform very well on technical benchmarks but fail to deliver any business value. Even the ones designed with organizational goals in mind may fail if they can't adapt when the goals change.
These failures are predictable, which also makes them avoidable - with the right development partner.
Our AI services
AI consulting services
We combine deep technical expertise with cross-industry experience to help you build a clear, executable AI strategy, aligned to your business goals, data realities, and risk tolerance.
- Identifying & prioritizing use cases
- End-to-end AI program management
- Model selection guidance with operational cost projections
- Modeling/projecting ROI & feasibility analysis
- AI adoption roadmap & AI readiness assessment
- Establishing autonomy & boundaries of agentic systems
AI training & workshops
We equip your leadership and key stakeholders with the knowledge, frameworks, and hands-on skills needed to evaluate, adopt, and govern AI confidently across your organization.
- Training programs for AI ethics & governance
- Safe agentic coding for development teams
- Technical upskilling for engineering teams
- Custom enterprise training programs
- Executive AI leadership programs
- AI use case discovery workshops
AI development services
End-to-end AI development services from engineering to architecture design through deployment, built on agile delivery principles, with full client visibility and security embedded at every stage.
- Conducting technical discovery & solution scoping
- AI solution architecture & design
- Prototype-to-production hardening
- Proof-of-concept & MVP validation
- Model training, testing & validation
- Post-launch monitoring & optimization
Generative AI development
Develop and deploy production-grade generative AI applications that go beyond demos, designed for enterprise reliability, security, and the scale your business actually operates at.
- Integrating & fine-tuning foundation models
- Multimodal AI applications
- Evaluation, groundedness & benchmarking
- Enterprise copilots & voice assistants
- RAG & graph RAG development
- GenAI for content & document workflows
Agentic AI development
Design, deploy, and integrate AI agents into your workflows to reason, plan, and act either autonomously or with human assistance for sensitive tasks. The agentic systems can use tools, coordinate across systems and domains, and orchestrate complex tasks to drive real operational outcomes.
- Single & multi-agent system design
- Human-in-the-loop agent design
- Agent permissions & least-privilege access
- Tool use & API-connected agents
- Agent orchestration, evaluation & observability
- Custom MCP servers, connectors & skill creation
AI solutions
Purpose-built AI systems are tailored to your specific use case, data architecture, and digital infrastructure. They are natively aligned to how you operate and your regulatory requirements and offer extensive control over their governance and long-term evolution.
- Custom ML model & SLM development
- Predictive & decision support models
- Speech & voice AI systems
- Domain & industry-specific model fine-tuning
- Computer vision systems
- Document & information intelligence
AI product development
We help you take AI-powered products from concept to market with our product strategy, UX design, and engineering capabilities, delivering user-ready solutions that scale with your business.
- AI product strategy & road mapping
- AI Launchpad — 90-day proof of value
- Full product lifecycle delivery
- UX design for AI-powered interfaces
- MVP development & validation
- Scalable architecture for AI products
AI integration services
Our AI integration services help enterprises embed AI systems and capabilities into existing workflows and platforms without creating the performance bottlenecks that derail most integration projects.
- Data pipeline design & implementation
- Enterprise system AI augmentation
- Cloud-native AI deployment
- Optimization of latency & throughput for AI workloads
- AI gateway & centralized model access control
- Enterprise connectors & middleware (CRM, ERP, ticketing)
Legacy modernization
Modernize legacy systems, data architecture, and digital infrastructure to streamline AI adoption and integration into existing workflows instead of bolting AI onto platforms that can’t support it.
- Assessment of legacy systems & modernization planning
- Monolith decomposition for AI-ready architecture
- Data architecture modernization
- Custom API & integration development to extract legacy data
- AI-assisted refactoring to accelerate modernization
- Retrofitting access controls, audit trails & data lineage
MLOps and platform engineering
Build the MLOps infrastructure that manages the full lifecycle of AI systems in production, from data preparation and deployment to drift detection and retraining, on internal platforms and tooling designed to run AI reliably at scale.
- Developing infrastructure for model deployment & serving
- Built-in observability, tracing, & logging for model/agent calls
- Model registry, drift detection, and retraining pipelines
- Regression testing & AI evaluation harnesses
- Optimization of inference cost at scale
- AI platform operation & centralized model access
AI managed services
We handle day-to-day operations and maintenance of your AI systems with a dedicated team accountable for uptime, performance, and cost. We monitor and manage models, agents, and pipelines, against SLAs, and keep them aligned to changing business needs.
- 24/7 monitoring and incident response for AI systems
- Retraining & evaluation cycles as an ongoing service
- Guardrail and policy updates as regulations & models change
- Model and agent performance management against SLAs
- Inference spend tracked & reported against budget
- Dedicated AI operations teams
AI governance
Compliant, well-governed, and responsible AI deployments across complex, highly regulated environments and industries like healthcare and finance, with tailored AI governance and controls for fairness, accountability, and observability built in.
- AI risk & compliance assessment
- Regulatory alignment (EU AI Act, HIPAA, GDPR)
- Data privacy & PII governance
- Bias detection & fairness evaluation
- AI inventory & use-case registry
- Model explainability & audit frameworks
AI security services
Build security controls into and around existing AI systems as well as into the architecture of AI solutions being built, to safeguard against both conventional cybersecurity threats and AI-specific attacks like data poisoning, model theft, and excessive agency.
- Threat modeling & adversarial testing
- Agent action approval workflows & audit trails
- Prompt injection & jailbreak testing
- Data privacy & encryption
- AI Model guardrails & runtime monitoring
- AI incident response
AI engineering team
Embed AI engineers directly into your teams, aligned to your delivery goals, so they build, evolve, and integrate AI systems from within. From bridging the specific skill gap to scaling your internal development capabilities, our pre-vetted AI developers can rapidly meet your talent needs.
- Dedicated AI developers and engineering teams in your delivery
- Nearshore and offshore alignment across time zones
- Knowledge transfer and in-house capability building
- Specialist roles (ML, generative AI, agentic, data, MLOps)
- Rapid scaling up and down as per your AI development needs
- Embedded engineers backed by our delivery and governance standards
Don't see the service you were looking for? Our custom end-to-end AI development covers it all!
Core AI capabilities we engineer
Foundation models
We build with a wide range of foundation models, including both proprietary and open-source/open-weight models. Our engineers select the optimal model based on your needs and fine-tune and configure it for your security, performance, and scalability requirements.
Agentic systems
With expertise across agent permissions, policy and governance-driven architecture, contextual grounding, and orchestration, we build agentic systems that serve your existing needs and evolve alongside your changing business requirements and constraints.
Data engineering
Our data engineering capabilities span from cleaning retraining data to building, maintaining, and scaling pipelines for data that AI systems ingest, enabling a robust data foundation for reliable AI outputs and agentic behavior.
Data governance & compliance
We make enterprise data usable and accountable through lineage records, access policies, quality thresholds, and bias checks. Our engineers define controls once at the data layer instead of rebuilding them for every project, streamlining regulatory alignment.
AI security
We have expanded our conventional cybersecurity capabilities to cover AI-specific threats, including prompt injection and jailbreaking, and build both dedicated AI security systems and security controls for existing AI-native and AI-augmented systems.
Production operations
Our cloud and on-premises capabilities let us size serving infrastructure to enterprise workloads, governed and optimized through native controls. The evaluation and testing pipelines we build detect failures early and guide remediation, keeping AI reliable after launch.
How does AI software development work
Define AI priorities & roadmap
Structured discovery sessions guided by an enterprise’s AI development requirements and goals produce a clear, actionable roadmap. It covers priority use cases, AI priorities, autonomy scope, and the business and technical KPIs the system will be measured against.
Prepare data & architecture
We evaluate your data and architecture to identify gaps in preparation, accessibility, and governance. This includes the data your ML models train on, foundation models fine-tune on, and AI systems ingest at run-time.
Design for user & workflow
AI systems, their interfaces, and agentic architecture and behavior are designed for a business’s workflows, processes, and user expectations, for improved user adoption. The architecture of various AI systems is influenced by data, tech stack, and compliance constraints they will be operating in.
Develop, test, & integrate
Our development process is both transparent and flexible to accommodate any level of oversight or involvement the client might require. This is also the stage where security controls, guardrails, access permissions, and integrations are built into the system. Continuous evaluations test model outputs and agent behavior to ensure that they are consistent, accurate, stable, and most importantly, aligned with both business and technical KPIs.
Deploy, govern, & improve
The deployments are planned and often phased to minimize disruption to ongoing operations. The monitoring and governance controls are active from the beginning and ensure that things like data drift, model performance, output quality, agent behavior, and usage are being continuously tracked. So if there is any deviation from the defined thresholds or if business needs evolve, decisions like retraining, guardrail changes, and workflow adjustments are grounded in data.
AI Launchpad
Zero to proof-of-value in 90 days
Move beyond fragmented pilots and turn artificial intelligence into a structured, scalable capability. Our AI Launchpad helps enterprises define where AI creates real business value—and builds the engineering foundation to deliver it.
Our AI launchpad helps you:
- Align AI efforts to measurable business outcomes
- Validate use cases and ROI potential
- Map user journeys to ensure adoption and value
- Build a foundation that supports AI adoption
- Define a clear execution roadmap
Enterprise AI use cases we deliver
We don’t sell AI as a concept. As a leading artificial intelligence software development company, we deliver working systems tied to specific business outcomes. These are some of the use cases where enterprises put AI to work most often.
Customer service & support
Handle the bulk of routine queries and empower human agents with knowledge retrieval, recommendations, an conversation summaries. Intent detection can be used to divert messages and escalations to the right teams, and interaction insights help gauge customer satisfaction.
Finance & back-office operations
AI can help streamline financial tasks including invoice processing, reconciliation, approvals, and exception handling. It’s a step above the former rule-based automation with better contextual understanding. Finance copilots assist with generating reports, strategic insights, and financial projections.
Risk, fraud, & compliance
Identify transaction and behavior anomalies and other suspicious patterns that may indicate fraud, while forestalling compliance issues with AI-driven real-time monitoring and risk analysis. AI systems also streamline the process of manual reviews with audit trails, supporting evidence, & prioritization.
Sales & marketing
Personalize product recommendations, content, and promotions around customer needs and behavior, with pricing recommendations guided by business rules. AI supports campaign planning, outreach, and proposal drafting within defined brand standards and approved claims.
Supply chain & operations
Forecasting is an established AI strength and can be applied to demand forecasting and maintenance planning to reduce or at least reduce the impact of supply chain and operational disruptions. AI agents can improve upon it by recommending substitutions and coordinating supplier follow-ups.
Engineering & IT
Streamline software delivery and reduce the workload of engineering teams with agent-driven development, testing, and code modernization. The review requirements and CI/CD controls are built into the development workflows, and custom APIs can simplify AI integration to legacy systems.
Industries we support
AI solutions we build
As an Artificial Intelligence software development company with end-to-end capabilities, we build and deploy a wide range of AI solutions for enterprises, including:
Generative AI & LLMs
We build generative AI solutions around enterprise knowledge, workflows, and content. The selected use case, performance requirements, and data sensitivity guide which model is selected and fine-tuned (if needed) and how retrieval-augmented generation (RAG) is built.
- Natural-language interfaces for enterprise data queries
- Product descriptions and localized content generation
- Enterprise knowledge assistants and contextual search
- Copilots embedded in business applications
- Content and document generation with brand and policy controls
- Summarization and drafting for research and review workflows
Agentic AI
We build agentic AI solutions around your workflows for efficiency, growth, and automation requirements, while helping you to scale your operations without adding headcount. The permissions, boundaries, and tool access of these systems are carefully designed around your regulatory requirements and internal policies to reduce compliance, security, or abnormal usage risk.
- Customer service and case-resolution agents
- Research and analysis agents that gather and reconcile information
- Operations agents that coordinate work across systems
- Voice agents for intake, follow-ups, and service requests
- Software engineering agents for development, testing, and maintenance
- Procurement agents for supplier research and purchase coordination
Predictive AI & machine learning
We design and build predictive models for demand, risk, and to identify both usual (at scale) and unusual patterns in operational and market data. The forecasts and scores these models generate can be integrated into existing workflows to support decision-making.
- Customer churn and retention prediction
- Transaction fraud and anomaly detection
- Predictive maintenance and equipment failure prediction
- Customer segmentation and lifetime value (LTV) modeling
- Lead scoring and predicting customers' purchasing behavior
- Demand, revenue, and capacity forecasting
- Credit risk, underwriting, and claims scoring
Computer vision
We develop computer vision (CV) solutions for accurate interpretation of images and videos, which can be used for identification and inspection purposes. Factors including image quality, camera hardware, conditions where the image/video is captured, and confidence thresholds guide how the solutions are built and configured, and when they may escalate something for human review.
- Content validation and image classification
- Object and defect detection for quality inspections
- Facial detection and image matching
- Video analytics for operational monitoring
- Visual search and product matching across image catalogs
Speech & audio AI
We build speech and audio solutions to support verbal interactions, make conversations searchable, and generate insights for the service and operational teams. These systems are designed around the languages and accents of the intended audience, background noise in the environment, and quality of signal and audio stream.
- Transcription with speaker identification
- Call analytics for quality assurance and compliance review
- Speech recognition and synthesis for voice interfaces
- Multilingual transcription and speech translation
- Field equipment monitoring via acoustic anomaly detection
Document & information intelligence
We design systems for extraction, classification, and validation of information from a range of documents and enterprise knowledge sources, including messages and business records. OCR and language models support varied formats and layouts, reducing cases escalated for human review. Business rules, flagged sources, and other exception guidelines escalate what needs to be reviewed before a human or model acts on it.
- Invoice, statement, and form processing
- Contract review and clause extraction
- Regulatory document analysis and review support
- Document classification, routing, and record reconciliation
- Cross-document comparison and discrepancy detection
- Entity and relationship extraction from business records
Optimization & decision intelligence
We develop decision-support systems that evaluate available actions against business objectives and operational constraints. Forecasts, optimization methods, and scenario analysis help teams compare tradeoffs and select actions within defined limits for cost, capacity, service levels, and risk.
- Routing, scheduling, and dispatch optimization
- Inventory and replenishment planning
- Pricing and revenue optimization
- Workforce allocation and capacity planning
- Next-best-action recommendations for customer engagement
AI capabilities evolve rapidly...and our development keep pace.
Our AI expertise spans far beyond these solutions and AI capabilities, so don’t
be discouraged if you don’t see the specific solution you were looking for.
AI built to enterprise security & compliance standards
As a governance-first artificial intelligence software development company, we build security, compliance, and AI governance controls like explainability and observability into the architecture of the AI systems we design and their integrations.
Certifications & recognitions
Frameworks we design and build to
We build AI systems to the regulatory standards and guidelines that apply to your industry and region, including:
Regulatory
- HIPAA
- GDPR and UK GDPR
- EU AI Act
Standards & frameworks
- NIST AI Risk Management Framework
- ISO/IEC 42001
- SOC 2
Our platform partners
We design and build AI systems within the cloud environments and platforms enterprises rely on for their digital operations. We leverage the AI tooling, frameworks, and capabilities of these platforms and cloud environments for minimal operational disruption and integration overhead.
AI implementation challenges we help overcome
AI projects rarely fail at the pilot stage; they fail in execution. Here are the most common implementation challenges enterprises face and how we solve them.
Data quality & retrieval accuracy
Poor or siloed data, whether it’s for training or feeding live AI systems, can result in unreliable outputs. We fix this at the source by building end-to-end data pipelines for cleaning, structuring, and governing data before it reaches the model for training or use.
Model drift in production
Models often degrade as the production data changes. We solve this with continuous monitoring for performance and drift detection, along with retraining pipelines. The retraining kicks in well before model output quality weakens enough to harm decisions and agent behavior.
Scaling beyond the pilot stage
Prototypes tend to fail when handling production-stage load, latency, and inference costs. Our cloud-native AI systems use auto-scaling, distributed inference, and managed services across AWS, Azure, GCP, and other hyperscalers to sustain performance.
Governance & audit gaps
Without governance controls in place, AI systems become a compliance risk. We build in access controls, explainability, and audit logs for day one, with regulatory alignment like HIPAA, GDPR, and EU AI Act wherever applicable.
Integration complexity
Legacy systems and fragmented data complicate AI adoption. Our AI developers use API-first, modular designs, with MCP servers and connectors linking AI systems to the existing infrastructure without fully rebuilding it for AI.
Low user trust & adoption
AI initiatives can fail when users don't trust or understand them and work around them. We drive adoption by involving users early, by designing interfaces aligned to existing workflows, providing training, and building long-term confidence.
Why choose 10Pearls as your AI software development company
As an artificial intelligence software development company, 10Pearls brings over two decades of enterprise delivery experience with deep data expertise to deliver production-ready AI systems. We help you safely add AI capabilities into your operations, aligning every AI initiative with your business strategy, governance requirements, and growth goals.
Domain expertise
Our software delivery experience spans multiple industries and operational environments. We carry this knowledge to the AI systems we build, so they are aligned with requirements, constraints, and regulations.
Governance-first mindset
We build safe and auditable AI systems by integrating the right guardrails, compliance, and security controls into the AI architecture, aligning them with frameworks including HIPAA, GDPR, and ISO 27001.
End-to-end project ownership
We serve as a single, accountable partner throughout the AI development lifecycle - from use case identification to deployment and monitoring, simplifying coordination and ownership for our clients.
Future-ready engineering
We build with current AI technologies, including agentic AI, MCP integration, and RAG, to ensure that our AI systems deliver optimal value, and we update the stack as technologies evolve.
Client-first transparency
Our delivery models allow you to be as involved with the project as you want, with visibility into progress, technical decisions, and results at every engagement stage.
Global reach, local alignment
With delivery centers across North America, Latin America, the UK, and Asia, we work within your time zone, compliance environment, and preferred engagement model.
AI technologies and models we work with
We work with the leading platforms, frameworks, and tools across the full AI development stack, selecting the right combination for each use case rather than defaulting to a single vendor or approach.
Model platforms
- Amazon Bedrock
- Amazon SageMaker AI
- Azure AI Foundry
- Google Vertex AI
- NVIDIA AI Enterprise
- Oracle OCI Generative AI
- Hugging Face Inference
Foundation models
- Claude
- GPT
- Gemini
- Llama
- Mistral
- Amazon Nova
- Cohere
Orchestration & agents
- MCP servers and connectors
- LangChain
- LangGraph
- OpenAI Agents SDK
- Claude Agent SDK
- Google Agent Development Kit
- Microsoft Copilot Studio
- Semantic Kernel
- LiveKit
- Temporal
Retrieval & vector stores
- RAG
- Graph RAG
- Hybrid search
- Pgvector
- Pinecone
- Qdrant
- Azure AI Search
- Amazon OpenSearch
- Databricks Vector Search
- Weaviate
- Chroma
- FAISS
- Neo4j
Data platforms
- Databricks
- Snowflake
- Microsoft Fabric
- Google BigQuery
- Amazon Redshift
- Apache Spark
- Kafka
- Airflow
- dbt
- Delta Lake
MLOps & observability
- MLflow
- Azure Machine Learning
- Amazon SageMaker AI
- Open Telemetry
- Weights & Biases
- Lang Smith
- Langfuse
- Arize Phoenix
- Evidently
- Kubeflow
- Grafana
Vision, speech & language
- OpenCV
- PyTorch
- TensorFlow
- Amazon Transcribe
- Amazon Polly
- Amazon Translate
- Amazon Rekognition
- Hugging Face Transformers
- Whisper
- Azure Document Intelligence
- YOLO
- Google Cloud Vision
- Azure AI Vision
- Azure AI Vision
- Stability AI
- DALL.E
- Squirro
- spaCy
- scikit-learn
Frameworks & libraries
- PyTorch
- TensorFlow
- Hugging Face Transformers
- OpenCV
- spaCy
- scikit-learn
Vision, speech & language
- Amazon Transcribe
- Amazon Polly
- Amazon Translate
- Amazon Rekognition
- Azure Document Intelligence
- Azure AI Vision
- Whisper
- Google Cloud Vision
- YOLO
- Stability AI (Image generation)
- DALL.E (Image generation)
Governance & security
- Fairlearn
- SHAP
- AI Fairness 360
- Amazon Bedrock Guardrails
- Azure AI Content Safety
- NVIDIA NeMo Guardrails
- Guardrails AI
- Microsoft Presidio
- Microsoft Purview
- Open Policy Agent
- Garak
How we accelerate enterprise AI
We are a seasoned artificial intelligence development company with diverse experience handling everything from enablement support to cutting-edge agentic deployments that redefine existing processes. This has inspired structured pathways we use to accelerate AI adoption and initiatives for enterprises.
Executive AI leadership program
Leadership alignment and readiness
The Executive AI Leadership Program is a workshop series for CxOs and heads of strategy, risk, legal, compliance, and HR. Sessions unify leadership around a shared AI vision, clear priorities, and defined risk boundaries. Leaders work through AI risks, ethical considerations, regulatory expectations, and organizational readiness, and leave with the guardrails and decision structures an enterprise AI program depends on.
Agent Studio
Governed delivery acceleration
Agent Studio is a governed agentic delivery framework with two modular agent ecosystems, one each for development and testing. Agents have clearly defined responsibilities, permissions, and human checkpoints, connected through a shared governance and orchestration model. Agent Studio can be integrated into existing delivery environments, with flexible model choices and visibility into agentic work cost.
10Pearls ValuePath
End-to-end AI transformation
ValuePath is our organizational transformation framework for enterprises moving from legacy operations to AI-led ones. It brings business leaders, end users, and technical teams together from the start to identify where AI should automate, augment, redesign, create, or eliminate work. ValuePath then carries initiatives through build, adoption, and governed scale, starting from an assessment, a governance review, or a production MVP depending on your maturity.
Accelerate AI adoption with consulting tailored to your AI requirements, constraints, and operational needs.
Testimonials
What enterprises get from AI built properly
AI’s value isn’t measured in models deployed or data processed; it’s measured in outcomes your business can see and quantify. Here are some of the benefits enterprises get when AI is built right.
Lower cost of ownership
AI systems can improve the efficiency of both individual processes and entire platforms significantly, reducing the cost of ownership. We helped a specialty health insurance company achieve that by lowering their prior authorization decision-making time from months to weeks, and reducing ownership cost by 5x.
Faster processing cycles
While traditional automation can accelerate processes, AI-driven automation can be far more comprehensive and applicable to a much wider range of processes. We built an AI automation solution for a highly sensitive patent correspondence process in a law firm, reducing a five-hour run to 4 minutes end-to-end.
Increased output without adding headcount
The right AI systems can help organizations scale certain operations without adding headcount, increasing their operational capacity without an increase in payroll expenses. We helped a global video creation and distribution platform rapidly release financial videos through an AI pipeline, with a 96% reduction in production time compared to manual production.
Reduced operational cost
AI systems can significantly reduce operational costs by automating complex and time-consuming manual tasks while simultaneously increasing throughput. We implemented an AI system for a digital transformation consultancy that reduced the cost of manual follow-up calls by 95% and improved customer relationship tracking.
Rapid access to institutional knowledge
Institutional knowledge often lives in silos or with senior staff, making access relatively complex and time-consuming for both junior staffers seeking information and seniors dispensing it. We built a generative AI-based knowledge assistant that saved the team 2,250 hours each month and reduced query time from 30 minutes to 30 seconds.
Accuracy at machine scale
AI technologies like computer vision extend their capabilities to the physical world to automate processes like property appraisals, completing them at a fraction of the manual time with incredible accuracy. We built an AI-driven property appraisal solution that reduced the approval time from days to hours and detected objects with 99% accuracy.
Case studies
case study - TECHNOLOGY
Accelerating Knowledge Access with a Generative AI Assistant
Rebuilding a correctional communications platform on AWS to improve call quality, automate compliance, and secure access.
case study - TECHNOLOGY
Automating Client Follow-Up with an AI Calling Agent
An AWS-native AI voice agent that runs post-project client check-ins, flags escalation risk, and auto-generates follow-up reports.
case study - TECHNOLOGY
Modernizing Inmate Communications with Secure Cloud AI
A secure, serverless generative AI assistant on AWS giving distributed innovation teams instant access to program knowledge.
Case study - healthcare
Building a Clinical-Grade AI Platform for GLP-1 Weight Care
Designing and building a HIPAA-aligned platform that pairs a patient app and clinician portal with a dual-pipeline clinical AI.
Awards and recognitions
AI insights
AI/ML
AI Pilot Failure: Why Enterprise AI Pilots Don’t Scale
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Understanding the uses of AI in energy sector
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AI in Banking Use Cases: What Works and What Stalls
A guide to AI in banking use cases, from fraud detection to document processing, the risks that stall them, and...
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Integrating AI with Legacy Systems: Enterprise Guide
Four in five enterprises are struggling to connect AI to the systems they already run. Four proven strategies for bridging...
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AI-powered DevOps in healthcare
AI-powered DevOps in healthcare accelerates release cycles with smarter code review, self-healing test automation, and proactive monitoring, without risking compliance.
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Agentic AI in the Telecom Industry
The telecom industry is embracing agentic AI for multiple operational and customer-facing use cases, while navigating legacy systems, integration, and...
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AI Adoption Challenges and How Enterprises Can Solve Them
Explore the key enterprise AI adoption challenges businesses face, from data and governance to talent and strategy, and discover practical...
AI/ML
What is Alternative Data in AI Credit Underwriting
The CFPB dropped disparate impact from ECOA in April 2026. Here is what that changed for AI credit underwriting, and...
AI/ML
Generative AI implementation roadmap for enterprise
Learn how to build a generative AI strategy that aligns investment with business priorities, reduces implementation risk, and creates a...
AI/ML
How AI Fraud Detection Works and Where It Still Fails
How AI fraud detection works in real time, which use cases scale first, and where models still fail against AI-powered...
FAQs
How do I check if my company is ready for AI adoption?
We offer a comprehensive AI Readiness Assessment that evaluates your organization’s AI maturity across 5 key dimensions:
- Strategy & vision
- Data readiness
- Technology infrastructure
- Talent & skills
- Governance & ethics
Can I partner with 10Pearls if I don't have in-house AI talent?
Yes. As an end-to-end AI software development company, we have the resources and expertise to support you throughout any stage of the AI software development life cycle – from strategy and ideation to deployment and post-launch support.
Does 10Pearls offer services worldwide?
Yes. 10Pearls is a global AI development company headquartered in Vienna, VA (Washington metropolitan area) with delivery centers in the UK, Latin America, and Asia, while also offering a range of AI development services remotely to clients around the world.
How does 10Pearls align AI with my business goals and KPIs?
Our comprehensive discovery sessions are a fundamental part of every project we take on. This is how we determine your goals, KPIs, technical requirements, expected timelines, and what success looks like for you.
For clients that are in the ideation phase, we offer a structured program called a Lean Product Accelerator (LPA), which is a series of guided discovery and strategy workshops conducted over 2-4 weeks to develop a detailed product strategy and MVP roadmap.
Will my data quality impact AI development success?
Yes. Success of any AI application or initiative depends heavily on data quality but with important caveats. Pre-trained models come with minimal data burden – only what’s needed for fine-tuning. Custom models require large quantities of accurate, standardized, relevant, and balanced data available for model training. However, adequate data quality must be maintained for the successful operation of AI systems.
How do you ensure safety and compliance?
We adhere to industry and domain-relevant compliance, governance, ethical, and security best practices when developing AI. This includes compliance mapping in the design phase governance and ethical guardrails embedded into the AI product during development. For compliance and governance, we adhere to all relevant regulatory requirements and guidelines – industry, domain, consumer protection, AI use, etc. As for security, we assess everything from dependencies to attack surface and vectors to ensure our AI development is as structurally sound as possible.
What industries does 10Pearls have experience developing AI for?
10Pearls has developed AI software and solutions for clients from a broad range of industries, including fintech, healthcare, telecom, education, real estate, and retail.
How long does it take to complete a development project?
It’s impossible to present an average timeline since many variables influence the completion time of an AI project. This includes solution complexity, model choice (custom or pre-trained), size of training/retraining/fine-tuning data set, digital infrastructure limitations, governance measures, etc.
What is the difference between pre-trained and custom AI models?
Custom models can be costlier and require longer to train but offer full control over optimization, governance, and capabilities. Whereas pre-trained models are trained on massive and broad data sets, that require explainability added as an additional layer but can be rapidly fine-tuned and deployed.
Does AI adoption pose any privacy challenges to my business?
This depends on how you are adopting AI and who you have selected for development and deployment. If your development partner is deferring compliance and governance tasks to you, then your collaboration structure and developmental oversight should ensure that there are no privacy and compliance vulnerabilities. You can significantly reduce this risk by partnering with an AI development company that is equipped with a strong industry and compliance understanding as well as development expertise.
How do you choose an artificial intelligence development company?
Look at production track record rather than pilot count. Ask for named references where an AI system is live and monitored, how the team handles evaluation and model governance, what the data security and compliance posture is, and which engineers you will actually work with.
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