AI Development Services Built for Production
Enterprise AI systems designed, built, and operated for real production use — not a demo.
Trusted by 300+ clients worldwide
What Are AI Development Services?
AI development services turn a defined business problem or product opportunity into a working AI-enabled system. The work may include discovery, data preparation, architecture, model and platform selection, application development, integrations, evaluation, security, deployment, monitoring, and continued improvement.
A production AI system is more than a model. It must work with the organization’s data, users, permissions, applications, infrastructure, operating rules, and risk requirements. The AI development company is responsible for coordinating those parts into a usable and supportable solution.
Sphere is an enterprise AI development company that designs, builds, integrates, and operates AI systems around real business workflows. We combine AI engineering with the software, data, cloud, security, and delivery work required to move from a promising idea to a system people can use in production. Our AI development services include generative AI applications, enterprise RAG and knowledge systems, AI agents, predictive machine learning, computer vision, document intelligence, AI-powered products, and integrations with existing enterprise platforms.
We can take responsibility for the complete initiative or work alongside your internal team. The engagement begins with the business outcome, available data, operating environment, risk, and success criteria — not with a predetermined model or platform.
Which AI Development Services Does Sphere Provide?
AI application and product development
Design and build web, mobile, and enterprise applications with AI embedded into the user experience, workflow, and supporting software.
Explore this service →Generative AI and LLM development
Create applications that generate, summarize, classify, transform, and structure content using commercial, open-source, or privately hosted models.
Explore this service →Enterprise RAG and knowledge systems
Connect approved documents and systems to permission-aware retrieval, grounded answers, citations, and traceable knowledge workflows.
Explore this service →AI agents and workflow automation
Build agents that retrieve information, coordinate steps, call tools, prepare actions, and operate within defined permissions and human approval gates.
Explore this service →Predictive AI and machine learning
Develop models for forecasting, scoring, recommendation, optimization, anomaly detection, risk analysis, and decision support.
Explore this service →Computer vision and document intelligence
Extract, classify, validate, and analyze information from images, video, forms, drawings, records, and other unstructured content.
Explore this service →AI integration and modernization
Add AI capabilities to existing applications, data platforms, CRM, ERP, document systems, cloud environments, and operational software.
Explore this service →MLOps and continued improvement
Deploy, monitor, evaluate, secure, maintain, and improve models and AI applications after release.
Explore this service →Custom AI development
Build bespoke AI applications and models designed around your exact workflow, data, and constraints rather than a generic template — for teams that need a purpose-built system, not an off-the-shelf tool.
Explore this service →Enterprise AI development services
Deliver AI capabilities sized for enterprise scale — governed access, multi-team rollout, compliance requirements, and integration with the identity, security, and platform standards a larger organization already runs.
Explore this service →What Can an AI Development Company Build?
The correct solution is determined by the workflow, users, data, risk, and measurable outcome. Sphere can build a focused AI capability or the complete product and operating environment around it.
Enterprise knowledge assistants
Help employees find cited answers across approved policies, manuals, case files, tickets, records, and operational systems.
Customer and employee copilots
Support drafting, research, service, sales, onboarding, analysis, and other knowledge-heavy work inside existing applications.
Agentic workflow systems
Coordinate multi-step processes across tools while applying permissions, approval gates, logging, limits, and failure handling.
Predictive decision-support systems
Use historical and real-time data to forecast demand, prioritize work, identify risk, recommend actions, or optimize resources.
Intelligent document workflows
Extract and validate information from contracts, claims, invoices, orders, technical documents, forms, and regulated records.
AI-powered software products
Build customer-facing products where AI is part of the core experience, supported by accounts, workflows, APIs, administration, analytics, and billing.
How Does Sphere Develop an AI System?
Define the outcome
Clarify the workflow, users, current process, expected improvement, constraints, and measures that will determine whether the system is valuable.
Assess data and systems
Identify required data, source condition, access, permissions, integrations, infrastructure, security requirements, and technical dependencies.
Select the architecture
Choose the model, retrieval, agent, machine-learning, application, integration, and deployment approach that fits the requirement.
Build with real workflows
Develop the AI capability and the surrounding application, APIs, data pipelines, interfaces, controls, and enterprise connections.
Evaluate the system
Test output quality, retrieval, reliability, performance, security, failure conditions, and fitness for the intended users and decisions.
Deploy to production
Configure environments, access, release automation, observability, documentation, training, support, and operational ownership.
Monitor and improve
Track usage, quality, cost, incidents, model or data changes, user feedback, and business outcomes after launch.
What Makes an AI System Ready for Production?
A demonstration can work with selected data and controlled inputs. A production system must continue working with real users, real permissions, changing information, incomplete inputs, system failures, and accountable operating procedures.
Reliable data
The system needs governed sources, repeatable ingestion, quality controls, ownership, and a method for keeping information current.
Enterprise integration
The AI capability must connect safely with identity, applications, databases, document systems, APIs, and operating workflows.
Defined evaluation
Quality standards, representative test cases, failure analysis, regression testing, and acceptance thresholds must be established before release.
Security and governance
Access controls, data handling, auditability, human oversight, content safeguards, incident procedures, and risk-appropriate approvals must be built in.
Observability and cost control
Teams need visibility into usage, latency, errors, model behavior, agent actions, infrastructure, and cost by workflow or business unit.
Operational ownership
Named owners must know who monitors the system, handles exceptions, approves changes, supports users, and decides when the system requires improvement.
Production-first delivery, built around your workflow
Discuss Your AI Initiative
How Can an AI Development Engagement Be Structured?
Diagnose
Use the AI Opportunity Diagnostic when the business problem is important but the use case, data requirements, architecture, cost, risk, or production path is not yet defined. Sphere currently offers this as an $8,500 fixed-scope engagement.
Start With the AI Opportunity DiagnosticBuild
Use a defined project or milestone-based engagement when Sphere is responsible for architecture, data work, application development, integrations, evaluation, deployment, and production preparation.
Explore Sphere AI FoundryScale
Use an embedded team or senior delivery pod when the initiative already has internal ownership and needs sustained AI, data, software, platform, evaluation, or operational capacity.
Hire an AI Developer or TeamWhen Should You Work With an AI Development Company?
An outside AI development company is useful when the organization needs capabilities or delivery responsibility that cannot be assembled quickly enough through internal hiring alone.
- A valuable use case has been identified, but the internal team has not delivered a production AI system before.
- The initiative requires AI, data, application, integration, cloud, security, evaluation, and delivery skills at the same time.
- A pilot exists, but production data, permissions, MLOps, governance, or system integration is blocking release.
- The organization needs a defined result and accountable delivery owner rather than one additional technical contributor.
- The timeline cannot absorb a long recruiting, onboarding, and team-formation process.
- The project must work in a regulated, security-sensitive, legacy, or operationally complex environment.
- The internal team needs temporary capacity for a defined build, migration, launch, or scaling phase.
Internal hiring may be appropriate when the capability is central to the permanent operating model and the organization has time to recruit, onboard, and support the complete team. Sphere can also work with that internal team and transfer architecture, code, documentation, and operating knowledge as the engagement progresses.
Ready to Move From Idea to Production?
Tell us what the organization needs to improve and we’ll help define the right path forward.
Discuss Your AI Initiative →What Results Has Sphere Delivered With AI Development?
PetroLedger: $1.2M in Annual Savings
Sphere built a generative AI Digital Twin Knowledge Platform for PetroLedger, a nationwide energy-market consulting and outsourcing firm serving the oil and gas industry. New hires reached full productivity in 3–5 months instead of 8–12 months, and PetroLedger reported $1.2 million in annual savings from reduced onboarding costs and error-related rework.
Read the PetroLedger Case Study →
Tax Advisory: Six Hours to Seconds
Sphere built a jurisdiction-aware enterprise RAG system for an international US tax advisory firm in five weeks. The production system reduced document research from six hours per engagement to seconds and improved retrieval accuracy by 66% compared with the prior keyword-based search.
Read the Enterprise RAG Case Study →Life Sciences: 45 Minutes to 9 Seconds
Sphere developed a fully cited AI technician knowledge assistant for a life-sciences and GxP facilities provider. The system reduced the time required to find a verified technical answer from as much as 45 minutes to approximately nine seconds while preserving source traceability and supporting cloud or on-premise deployment.
Read the AI Technician Case Study →Monarch Air Group: 60x Faster Resolution
Sphere connected more than 35,000 operational documents across enterprise systems for Monarch Air Group. The resulting knowledge and support system delivered a 60x improvement in resolution time.
Explore Support Intelligence →An AI Development Company With Enterprise Delivery Depth
AI and software engineering together
We build the AI capability and the surrounding application, data, integrations, infrastructure, testing, and operational controls needed to use it.
21 years of delivery experience
Our work draws on more than two decades of software, data, cloud, integration, modernization, and enterprise delivery experience.
More than 100 professionals
We can assemble the AI, data, application, platform, quality, security, design, and delivery capabilities required by the engagement.
Production-first architecture
We account for permissions, evaluation, observability, governance, failure handling, support, and continued operation before release.
Platform-agnostic implementation
We select commercial, cloud, open-source, and privately hosted technology according to the requirement instead of forcing one model or platform.
Client ownership
The architecture, code, documentation, and operating knowledge are designed so the client can own and continue the system without dependence on a proprietary black box.
AI-accelerated delivery
Our Precision-Driven Engineering methodology uses AI-assisted and agentic development workflows under senior engineering oversight to accelerate delivery while preserving accountability.
Independent client validation
Sphere maintains a 4.9/5 average across 32 verified Clutch reviews. Client feedback repeatedly identifies work quality, communication, delivery management, and technical depth as strengths.
How Should You Compare AI Development Options?
The right option depends on how much internal capability already exists and who must own the delivery outcome.
Questions to ask an AI development company
- Which comparable systems have reached production?
- Who is responsible for data, integrations, application development, evaluation, security, and operations?
- How will success and output quality be measured?
- How are model, platform, and cloud choices made?
- Who owns the code, architecture, data, and intellectual property?
- What is included after deployment?
What Kind of AI Support Do You Need?
Frequently Asked Questions About AI Development Services
What are AI development services?
AI development services cover the work required to design, build, integrate, deploy, and operate an AI-enabled system. Depending on the initiative, that can include discovery, data preparation, architecture, generative AI or machine learning, application development, integrations, evaluation, security, governance, MLOps, and continued support.
What types of AI systems can Sphere build?
We build generative AI applications, enterprise RAG and knowledge systems, AI agents, predictive models, recommendation and optimization systems, document intelligence, computer-vision solutions, AI-powered products, and AI capabilities integrated into existing enterprise software.
How long does AI development take?
The timeline depends on the use case, data condition, integrations, application scope, security requirements, and approval process. A focused validation may take several weeks. A production system may require several months, while larger programs should be delivered in controlled stages.
How much do AI development services cost?
Cost depends on the business workflow, data readiness, integrations, application requirements, expected reliability, security, governance, usage volume, and continued support. We separate one-time implementation costs from recurring model, infrastructure, monitoring, and maintenance expenses.
Do we need clean data before starting?
No. The data does not need to be perfect before the first conversation. We do need to identify the priority sources, ownership, access, permissions, condition, and gaps so the required preparation can be included in the architecture, scope, and estimate.
What is the difference between a proof of concept and a production AI system?
A proof of concept tests whether an approach may work under limited conditions. A production system must work with real users, data, permissions, integrations, security controls, monitoring, evaluation, support, and defined failure handling.
Can Sphere integrate AI with our existing systems?
Yes. We can integrate AI with existing applications, databases, document repositories, identity providers, CRM, ERP, operational software, cloud services, and external APIs. The integration scope is defined according to the workflow and the condition of each system.
Will we be locked into one AI model or cloud provider?
No. We select models, platforms, and infrastructure according to the use case, security, performance, cost, and operating requirements. We can work across commercial APIs, cloud AI services, open-source models, private cloud, and on-premise environments.
Who owns the code and intellectual property?
Sphere structures delivery so the client can own the resulting code, architecture, documentation, and operating knowledge according to the engagement agreement. We do not require the system to remain dependent on a proprietary black box that only Sphere can maintain.
Can Sphere support the system after launch?
Yes. We can provide monitoring, evaluation, model and prompt updates, data-pipeline maintenance, integration support, performance and cost optimization, security updates, incident response, user feedback cycles, and continued product development.
Build the AI System the Business Can Actually Use
Tell us what the organization needs to improve, what data and systems are involved, and where the initiative stands today. We will help define the appropriate path — diagnostic, controlled build, complete production delivery, or additional team capacity — and identify what is required to move forward responsibly.