AI Development Services
Our AI development services span the design, build, and scaling of GenAI applications, agentic systems, and ML models, with MLOps for production reliability, backed by 20+ years of enterprise delivery across regulated industries.
Artificial intelligence development services are engineering services that help organisations build, evaluate, integrate, deploy, and govern AI systems within their operations, covering data pipelines, model selection, training, and MLOps that keep AI systems reliable after launch.
Production AI that fits how you operate
Our AI development services
From generative and agentic AI systems to data engineering and MLOps, our AI/ML development services cover every stage from build to production operations.
Custom AI development
Build AI around your business needs with models selected for your data, cost, and performance requirements, and architecture designed for production.
Generative AI & LLM applications
Reduce time spent searching, drafting, and summarising with generative AI applications grounded in your enterprise knowledge, with citations and output controls.
AI agents & multi-agent systems
Scale complex operations with AI agents that coordinate tasks across systems, using defined permissions and human approvals to control higher-risk actions.
Conversational & voice AI
Minimise service queues with conversational and voice AI that handles routine requests, supports multiple languages, and routes complex cases to your teams.
Document intelligence
Reduce manual document processing and data-entry errors with AI that extracts, classifies, and validates information, routing exceptions for human review.
Machine learning & predictive models
Strategically transform your business with purpose-built ML models trained on your proprietary data and embedded with robust explainability and data-governance controls.
Computer vision engineering
Automate critical visual inspection tasks, extract data and insights from images or videos with AI’s computer vision capabilities to enhance business functions like security.
AI workflow automation
Reduce manual work and processing errors with AI automation that handles routine tasks, flags exceptions, and routes approvals within your existing workflows.
AI data engineering
Improve AI reliability with data pipelines that clean, structure, and govern training and operational data, with quality checks, lineage, and access controls.
MLOps, LLMOps & observability
Lower AI risk with observability and governance controls built into the architecture. Streamline continuous improvement with MLOps designed for evolving AI needs.
AI evaluation, testing & assurance
Validate and assure AI systems against business, quality, security, and compliance requirements with continuous testing to improve performance and reduce risk.
AI platforms & AI-as-a-Service
Expand access to AI with platforms that make models available through governed APIs, with access controls and usage monitoring across teams or customers.
Outcomes we have delivered
From weeks to hours
reduction in claims processing time via an AI-powered claims intelligence system on AWS
95% reduction
in the cost of outbound calls through an AI-powered calling agent with serverless architecture.
2,250 hours saved
every month with an AI knowledge assistant and RAG architecture.
94% matching accuracy
and a 4-minute processing time achieved with an ML-based genealogical matching platform.
Our AI development process
Our AI development is flexible and adaptable to client needs, goals, and resources.
Outcome, feasibility & risk alignment
Our AI development process starts with aligning AI to your business goals, comprehensive feasibility analysis, use case identification, and risk modelling across both AI governance and industry-compliance dimensions.
What you get: A feasibility and risk assessment with prioritised use cases, success metrics, and a compliance risk register.
Design data, architecture & controls
We identify and evaluate data sources for completeness, develop pipelines for batch and real-time availability, reconfigure existing data architecture for AI based on gap analysis, and design data and compliance controls.
What you get: A target architecture and data readiness report.
Build, evaluate & assure
AI solutions are developed using retrained or fine-tuned foundation models, or purpose-built custom models. Each solution is then evaluated and tested for performance, scalability, reliability, and operational integration.
What you get: A tested AI system with an evaluation report.
Deploy into enterprise workflows
Deployment takes place within the target environment, with AI integrated into existing systems, processes and workflows while minimising disruption and removing potential performance bottlenecks.
What you get: A production release with runbooks and a rollback plan.
Govern, monitor & improve
Post-deployment, we monitor the model for concept and data drift and optimise AI systems as the original parameters and requirements change. This enables continuous improvement, governed operations, and business-aligned evolution.
What you get: Monitoring dashboards tailored to your operations and scheduled performance reviews.
Regulations & frameworks we align to
We deliver AI development services aligned to regional, industry, and AI-specific guidelines and regulatory standards, including:
Regulatory
- HIPAA
- GDPR and UK GDPR
- EU AI Act
Standards & frameworks
- NIST AI Risk Management Framework
- ISO/IEC 42001 controls
- SOC 2 Trust Services Criteria
Certifications & recognitions
Ready to operationalise AI for your business with secure and governed AI systems?
We take you from idea and use case discovery to AI systems embedded into
your processes and aligned to your business priorities.
Why choose 10Pearls for enterprise AI development services
Outcome-driven AI development
Build AI systems aligned to your business value that reduce risk, optimise cost, and deliver measurable results tied to specific business KPIs.
AI-accelerated engineering & QA
Improve time to value with agentic software development, automated QA and testing, and accelerated delivery cycles without compromising on quality.
Built for enterprise workflows
Design AI systems for the operational complexity and scale of enterprise environments, backed by 20+ years of enterprise delivery experience.
Security & compliance embedded
Embed security and governance controls into AI systems’ architecture, making them observable, auditable, and policy-controlled.
Continuous AI operations & optimisation
Maintain performance, quality, and enable scalability of AI systems as business needs evolve. Minimise drift through continuous monitoring, governance controls, and ongoing improvement.
AI architecture informed by risk modelling
Reduce AI adoption, governance, and business-alignment risks with architectures built using information obtained by comprehensive risk modelling.
10Pearls in numbers
700+
projects completed
225+
enterprise clients
20+ years
of enterprise-grade solutions
10+
Industries with AI deployments
1400+
people across four continents
4.9/5
Clutch rating
You are in great company
How much AI development costs?
AI development cost is influenced by a number of factors, the most significant of which are:
Scope & autonomy
The more a system does on its own, from answering questions to taking actions across systems, the more time, people, and guardrails it needs.
Data readiness
Scattered or unstructured data and strict availability requirements add preparation and architecture work before the AI build begins.
Integrations
Each system the AI reads from or writes to adds build and testing effort, with custom work for some legacy and closed systems.
Model approach
Fine-tuning existing models or training custom ones costs more than adapting foundation models with RAG.
Compliance requirements
Governance controls, audit trails, test records, and documentation each have to be designed, tested, and evidenced.
| Project stage | Typical range |
|---|---|
| Proof of value | $25,000 to $100,000 |
| Production AI system | $50,000 to $500,000 |
| Enterprise AI programme | $250,000 to $2,000,000 |
Disclaimer: These are tentative ranges mostly based on the current market data. The exact estimates are provided once we scope the project. These do not include the infrastructure, compute, and licensing costs.
Ongoing operating costs
Inference, hosting, and monitoring costs depend on usage, model choice, and operational requirements. We estimate these during scoping so you can plan for both development and ongoing operations.
Become an AI-native enterprise with 10Pearls ValuePath
Move beyond isolated AI initiatives with a strategic framework that transforms
how your organisation operates, scales, and creates value with AI.
Industries we support
AI tech stack
Model platforms
- Amazon Bedrock
- Amazon SageMaker AI
- Microsoft Foundry
- Google Vertex AI
- Oracle OCI Generative AI
- NVIDIA AI Enterprise
- Hugging Face Inference
Foundation models
- Claude
- GPT
- Gemini
- Llama
- Mistral
- Amazon Nova
- Cohere
MLOps & observability
- MLflow
- Azure Machine Learning
- Amazon SageMaker AI
- OpenTelemetry
- Weights & Biases
- LangSmith
- Langfuse
- Arize Phoenix
- Evidently
- Grafana
Retrieval & vector stores
- RAG
- Graph RAG
- Hybrid search
- pgvector
- Pinecone
- Qdrant
- Azure AI Search
- Amazon OpenSearch
- Databricks Vector Search
- Weaviate
- Chroma
- FAISS
Data platforms
- Databricks
- Snowflake
- Apache Spark
- Apache Kafka
- Apache Airflow
- dbt
- Microsoft Fabric
- Google BigQuery
- Amazon Redshift
- Delta Lake
Orchestration & agents
- MCP servers and connectors
- LangChain
- LangGraph
- OpenAI Agents SDK
- Claude Agent SDK
- Google Agent Development Kit
- Microsoft Copilot Studio
- Microsoft Agent Framework
- Redis
- LiveKit
- Temporal
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
Governance & security
- SHAP
- Amazon Bedrock Guardrails
- Azure AI Content Safety
- NVIDIA NeMo Guardrails
- Guardrails AI
- Microsoft Presidio
- Microsoft Purview
Case studies
From AI agents that improve existing business processes to complete platforms, our custom AI development services align to a wide range
of use cases across multiple industries.
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.
- Personalized care with dual-pipeline clinical AI
- Safer clinical decisions with human-in-the-loop
case study - artificial intelligence
Agentic AI Financial Assistant
Optimising financial operations with an AI-powered smart assistant designed to automate workflow and enhance decision-making.
- Improved scalability
- Increased operational efficiency
case study - artificial intelligence
Automated Object Detection System
Streamlining property appraisals with an AI-driven platform that automates image analysis and object detection for greater speed and precision.
- Accuracy in image detection reached
- Reduced approval times & manual review
Case study - legal services
Accelerating Critical Business Workflows with AI Automation
Improving speed, consistency, and risk control in a high-volume legal workflow through AI-powered automation.
- 5 hours saved per run for each attorney
- 4-min runtime from intake to delivery
case study - ARTIFICIAL INTELLIGENCE
Healthcare Support Platform with Agentic AI
10Pearls developed an agentic AI virtual assistant designed to enhance caregiver support through multilingual support, sentiment analysis, and a sophisticated knowledge management system.
- Enhanced UX with AI
- Improved accessibility
- Increased scalability
- 1 / 5
- 2 / 5
- 3 / 5
- 4 / 5
- 5 / 5
What our clients say
Insights
AI/ML
AI Readiness Assessment: A 6-Dimension Checklist
AI agents act with insider access at machine speed. Why that breaks traditional security models, and seven controls for engineering...
AI/ML
Securing AI Agents: 4 New Risks and 7 Controls
AI agents act with insider access at machine speed. Why that breaks traditional security models, and seven controls for engineering...
AI/ML
Why Corporate AI Implementation Fails
Why Corporate AI Implementation Fails & How to Fix It 10Pearls Editorial Team 13 min read Summary Corporate AI implementation...
AI/ML
How to Choose an AI Development Partner: Red Flags to Avoid
An AI project can impress in a demo and still fall apart on real data and real users. How to...
FAQs
What are AI development services?
AI development covers the design, build, testing, and deployment of AI systems for business use cases. The services span from data preparation, selecting and adapting the right models, developing AI applications and agentic systems, connecting them into the existing ecosystem, and monitoring to support performance consistency after deployment.
How much do AI development services cost?
The cost of an AI development project may range from $25,000 to over a million, depending on scope, data readiness, model choice, compliance requirements, and a number of other factors. The range can be further divided into cost tiers, with pilots requiring a smaller amount and complex enterprise platforms needing more.
How long does an AI development project take?
An AI development project may take anywhere from a few weeks to over a year, with proof of concept and pilot projects typically taking weeks to complete and enterprise programmes running from several months to over a year. Larger programmes are usually completed in phases. The actual timeline is influenced by factors like data preparation requirements, integration complexity, evaluation, client approvals, and regulatory requirements.
What is the difference between AI development and AI consulting?
AI consulting helps determine where AI can create value and what to pursue. AI development builds and deploys the systems needed to deliver it. If your use case and requirements are already clear, development can begin without a separate consulting engagement.
Do you build custom AI models or use pre-trained ones?
We use both and even a combination of both, depending on your requirements. Existing models are easier and faster to use and can be grounded in your enterprise knowledge with RAG and adapted through fine-tuning. Custom models can be useful for tasks like predictive scoring, anomaly detection, or specialised image analysis. The exact choice is driven by several factors, including AI use case, data, compliance requirements, privacy, and cost.
Who is typically involved in an AI development project?
An AI development project typically includes both AI and data engineers, a solution architect, experts related to MLOps and evaluation, and a delivery lead. From your side, the business case owner and subject matter experts help across the project lifecycle from defining requirements to assessing results and providing approvals. In some cases, security and compliance teams get involved as well. The exact mix depends upon the scope of the project.
How do you ensure AI systems keep working after launch?
By building monitoring and evaluation into the architecture of the AI systems. They help us track output quality, drift, latency, and cost in production. We use regression testing to identify problems when the model, prompt, or data changes. The support agreement and scope cover monitoring, maintenance, and scaling of AI systems.
Can you integrate AI into our existing systems?
Yes. We connect AI to enterprise platforms, including CRMs, ERPs, EHRs, and internal tools, with data access, permissions, and performance treated as important considerations for architecture design. For projects focused on connecting AI to existing platforms, explore our AI integration services.
How do you handle data security and compliance?
We build security and governance controls into AI system architectures and integrations. This includes encryption, access controls, audit trails, and built-in safeguards for sensitive data. As an ISO 27001-certified organisation, 10Pearls adheres to relevant security and compliance regulations, frameworks, and guidelines related to your industry, region, and use case.
How do you choose an AI development company?
Look for production experience supported by case studies and measured results. Ask how the company evaluates AI, protects data, and supports systems after deployment. Check relevant industry experience, verify certifications, and clarify ownership of code, models, and documentation before agreeing to an engagement.
Let’s discuss your AI development goals
We map out a clear path for you – from AI adoption to production-ready AI systems in a discovery session.