AI Implementation Services

What AI Implementation Really Means

AI implementation is about making artificial intelligence work inside your organization – not only as a pilot, but as a practical part of daily operations. Sphere’s role is to make this transition fast, predictable, and secure.

Why it matters:

  • You stop experimenting and start producing measurable outcomes.
  • Your data becomes usable in real time, not locked in silos.
  • Teams gain clear workflows, not another side project.
  • AI integrates into what already works, but does not replace it.

Two Paths to AI Implementation

AI implementation can take two different directions depending on where you are in your transformation journey. Both lead to measurable outcomes – one accelerates how you develop, the other transforms how you operate.

1. AI-Assisted Development

Embed AI directly into your software delivery workflow. Empower developers with intelligent tools that write, test, and optimize code faster – without changing your existing stack.
2. Implementation of AI Solutions

Build and scale production-ready AI systems that automate processes, connect data, and create tangible business value.

AI-Assisted Development

AI-Driven Development Tools

Our team integrates modern LLMs and copilots directly into your development environments, helping engineers code, refactor, and optimize with greater speed and consistency.
AI-Powered Testing & QA

By building generative testing pipelines, we automate case creation, validation, and reporting—reducing manual QA time and improving release confidence.
Knowledge Assistants for Developers

We design private AI assistants trained on your repositories, documentation, and tickets to make knowledge instantly available across the team.
AI Pipelines for CI/CD

Through automated review, documentation, and deployment flows, we shorten delivery cycles and improve traceability throughout the release process.
Developer Enablement & Training

Beyond setup, we guide your developers in applying AI tools responsibly, interpreting model outputs, and maintaining full control of every AI-assisted task.
AI Vulnerability Detection

We integrate AI-powered security scanners that analyze code, dependencies, and configurations to detect vulnerabilities earlier, reduce risk exposure, and support continuous secure development.

Benefits of AI-Assisted Development

icon 30–50 % faster delivery cycles across teams
icon Consistent code quality and reduced human error
icon Fewer context switches, smoother collaboration
icon Immediate productivity gains without re-architecture
icon Works with your current development stack and tools

Implementation of AI Solutions

AI Prototyping / Proof of Concept (PoC)

Our experts design and deliver focused prototypes that validate your AI ideas in weeks, helping you measure feasibility and business impact before full-scale investment.
AI Accelerator Programs

Our experts design and deliver focused prototypes that validate your AI ideas in weeks, helping you measure feasibility and business impact before full-scale investment.
RAG / LLM + Data Integration

By connecting generative AI to your verified enterprise data, we eliminate hallucinations, improve accuracy, and enable models that truly reflect your organization’s knowledge.
Data & AI Readiness

We assess the quality, structure, and compliance of your data, building the policies and pipelines needed for scalable, secure AI adoption.
End-to-End Deployment & Support

From model selection to ongoing monitoring, we handle the full lifecycle of AI implementation—ensuring stable performance and measurable outcomes.
MLOps & Optimization

We set up the infrastructure, automation pipelines, and monitoring needed to keep models accurate, scalable, and reliable in production environments.

Benefits of AI Implementation with Sphere

icon Launch your first AI agent or product in under 90 days
icon Reduce model risk with data-grounded, verifiable systems
icon Turn isolated pilots into unified, scalable AI workflows
icon Automate insights and minimize manual review time
icon Achieve measurable ROI without increasing infrastructure costs

The 5 Essentials for Enterprise AI

Explore the core architectural and operational principles that keep AI accurate, secure, and production-ready.

Why Choose Sphere for AI Implementation

Sphere combines AI strategy, engineering, and data expertise to help organizations operationalize intelligence. Our core differences from others:

  • Cross-disciplinary teams: AI architects, data engineers, ML ops, and app developers.
  • Experience across manufacturing, BFSI, SaaS, healthcare, and retail.
  • Proven frameworks for fast prototyping and secure scaling.
  • Flexible engagement – augment your team or outsource end-to-end delivery.

Hear From Our Clients

Sphere Partners
Selah Ben-Haim VP of Engineering at Prominence Advisors

Our experience with Sphere and their team has been and continues to be fantastic. We keep throwing new projects at them, and they keep knocking them out of the park (including the rescue of a project that was previously bungled by another vendor).

Sphere Partners
Ben Crawford Senior Product Manager at Enova Financial

I would expect to be delighted. It’s been a really positive experience, working with Sphere, and I would expect you to have the same.

Sphere Partners
Mark Friedgan CEO at CreditNinja

Sphere consistently prioritizes the needs of their clients, demonstrating both agility and teamwork. They bring innovative and well-considered solutions, consistently surpassing my expectations.

Sphere Partners
René Pfitzner Co-Founder at Experify

Sphere provided excellent full-stack development manpower to augment our team and work with us.

Sphere Partners
Bruce Burdick Chief Information Officer at Integra Credit

We've been working with Sphere and its excellent consultants since our founding. Their combination of offshore talent, pricing, and shift offsetting is hard to beat. They provide crucial augmentation to our in-house team. We simply couldn't achieve our production ambitions without their service.

Sphere Partners
Jemal Swoboda CEO at Dabble

The resources and developers that Sphere Software provides are skilled and have the required technical expertise to complete their tasks successfully, with the team easily scaled in either direction. The deliverables are always high-quality.

Sphere Partners
Arthur Tretyak Founder and CEO at IntegraCredit

With Sphere, we were able to migrate in half the time it would take to train an additional FTE…

Sphere Partners
Lee Ebreo VP of Engineering at Credit Ninja

These things would not have been achievable if we did not build our own in-house system. We augmented our development team capabilities using Sphere’s developer, who works very well with our Dev Lead in Chicago. Sphere’s developer was an expert in the new system, and continues to be an expert as we evolve it.

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20

Years of Experience

4.9*

Clutch.co Review Score

97%

Client retention rate

1600+

Completed Projects

Join 350+ Satisfied Clients

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Frequently asked question

AI implementation in business is the process of integrating artificial intelligence into real workflows, systems, and products so it delivers measurable outcomes, not just lab demos. It covers everything from AI-assisted software development to production AI solutions like agents, copilots, and predictive models running on your live data.

Start with an AI readiness and use case assessment. Identify 1–3 high-value processes, review data quality and access, define success metrics, then run a focused PoC or accelerator program. A partner like Sphere can guide you from discovery and prototyping to production rollout and support.

Typical use cases include customer support automation, AI copilots for employees, document processing, fraud detection, supply chain optimization, predictive maintenance, and AI-assisted software development. Many companies also implement RAG-based knowledge assistants to answer questions on top of their internal documents.

AI-assisted development means embedding LLMs and copilots into your existing dev tools and CI/CD pipelines. Engineers get help generating code, writing tests, reviewing pull requests, documenting changes, and searching across repositories and tickets. Sphere designs this so your current stack, security model, and workflows stay intact.

AI accelerates coding, test creation, and code review, and makes documentation and knowledge search much faster. With AI-powered testing, vulnerability scanning, and knowledge assistants, teams ship more frequently, reduce regressions, and spend less time on repetitive work. This typically leads to 30–50% faster delivery cycles when implemented correctly.

No. You need data that is accessible and good enough to start. A key part of any AI implementation is a Data & AI readiness phase that cleans, structures, and connects your data, and sets the right governance policies. Sphere includes this step so your models are grounded in reliable, compliant data.

Timelines depend on scope, but focused PoCs usually take a few weeks, and first production AI agents or solutions often go live in under 90 days. Larger programs that touch multiple business units unfold in phases, with incremental value delivered at each stage rather than a single big-bang release.

Costs vary based on complexity, data work, and integration needs. Small PoCs are typically scoped as fixed-price projects, while broader initiatives use a mix of dedicated teams and milestone-based budgets. Sphere usually starts with a short discovery engagement to estimate effort, infrastructure impact, and expected ROI.

RAG connects large language models to your verified enterprise data instead of relying only on what the base model was trained on. This reduces hallucinations, improves answer accuracy, and ensures responses reflect your policies, documentation, and records. Sphere designs RAG / LLM + data integration layers as a core part of enterprise-grade AI.

MLOps is the set of tools and practices that keep your AI models deployable, monitored, and up to date. It covers versioning, CI/CD for models, observability, retraining pipelines, and rollback strategies. Without MLOps, AI projects remain fragile pilots; with it, they become reliable production systems.

Sphere designs AI architectures with security and compliance from day one: private data stores, role-based access, audit trails, encryption, and policies aligned to industry standards (such as SOC 2, HIPAA, PCI, or local regulations where needed). We implement AI vulnerability detection in the SDLC and apply strict data minimization and governance.

Yes. Most AI projects at Sphere integrate with existing CRMs, ERPs, data warehouses, and apps through APIs and event streams. The goal is to extend what already works with AI capabilities like smart routing, recommendations, copilots, or analytics rather than forcing a full re-architecture on day one.

Manufacturing, financial services, SaaS, healthcare, and retail are among the fastest adopters. They use AI for risk scoring, demand forecasting, personalization, automation of back-office workflows, and smarter decision support. Sphere has case experience across these sectors and adapts patterns to each client’s regulatory and operational context.

ROI is tracked through concrete metrics defined upfront: time saved per task, reduction in errors, faster release cycles, increased conversion rates, lower support costs, or reduced fraud and risk exposure. Sphere builds dashboards and monitoring into the solution so you can see and share the impact with stakeholders.

AI implementation services focus on integrating and operationalizing AI within your current business and tech landscape. Building an AI product from scratch is about creating a standalone solution with its own roadmap and market. Sphere can do both, but AI implementation usually starts with your existing systems, data, and teams to deliver value quickly.

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