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.
projects completed
enterprise clients
of enterprise-grade solutions
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.
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.
The reasons AI projects fail are almost consistent across industries. Organizations spend months on AI pilots only to see them stall before reaching production.
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.
Without proper model governance, monitoring, drift detection, and retraining pipelines, model degradation can lead to unreliable outputs and agent behavior, and ultimately, compliance risks.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Don't see the service you were looking for? Our custom end-to-end AI development covers it all!
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Handle the bulk of routine queries and empower human agents with knowledge retrieval, recommendations, and conversation summaries. Intent detection can be used to divert messages and escalations to the right teams, and interaction insights help gauge customer satisfaction.
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.
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.
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.
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.
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.
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.
We build AI systems to the regulatory standards and guidelines that apply to your industry and region, including:
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
With delivery centers across North America, Latin America, the UK, and Asia, we work within your time zone, compliance environment, and preferred engagement model.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 study - TECHNOLOGY
A secure, serverless generative AI assistant on AWS giving distributed innovation teams instant access to program knowledge.
case study - TECHNOLOGY
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
Rebuilding a correctional communications platform on AWS to improve call quality, automate compliance, and secure access.
Case study - healthcare
Designing and building a HIPAA-aligned platform that pairs a patient app and clinician portal with a dual-pipeline clinical AI.
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We offer a comprehensive AI Readiness Assessment that evaluates your organization’s AI maturity across 5 key dimensions:
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.
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.
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.
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.
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.
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.
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.