Agentic AI Delivered in Your Environment

Get a production agent that reads your data, takes actions across your systems, and routes decisions to your team when approval is required.

It’s Time to Move to Agentic AI

Companies move from AI that answers questions to AI that runs parts of operations. This is where most systems break – and where we focus delivery.

FROM

TO

FROM

Our pilots don’t survive production.

TO

A working agent workflow running in your environment, owned by a delivery team that deploys, monitors, and improves it as part of live operations.

FROM

We can’t connect agents to real systems safely.

TO

An agent connected to your CRM, ERP, data platforms, and internal tools, with scoped permissions, logged system calls, and approval points for high-risk actions.

FROM

We can’t see what the agent actually did.

TO

An operations dashboard with full workflow traces, decision logs, and an audit trail across every tool and handoff.

FROM

Accuracy breaks when workflows get complex.

TO

Agents grounded in your enterprise data, with automated tests and ongoing evaluation that track performance across real workflow paths.

FROM

We don’t want to be locked into one vendor.

TO

A portable system design that supports multiple models, APIs, and internal platforms, so you can change providers without rebuilding the workflow.

A Production Agent, Running Inside Your Operation

How We Work

Sphere AI Foundry embeds cross-functional AI engineering pods to build, train, and deploy accurate, secure, governed, production-ready systems. 

Phase 1 (Week 0–1): Workflow + risk framing

We map the workflow, define tool access, decide human control points, and set success metrics.

Phase 2 (Week 2–4): Build the agent workflow

We implement orchestration, grounding where needed, and the first deployable increment.

Phase 3 (Weeks 4–6): Prove reliability

Evaluation harness, regression checks, monitoring, and fallbacks before expansion. 

Phase 4 (Weeks 6+): Scale responsibly

Add more tasks, integrate more systems, expand autonomy only where the controls prove stable.

Pod composition:

AI/ML Engineers, Data & Platform Engineers, AI Product Engineers, MLOps Engineers, RAG & Agentic AI Architects

Operational cadence:

Weekly exec-ready visibility, milestone plan, decisions surfaced early.

Built-In Guardrails for Enterprise-Safe Agents

When an agent can update records, send messages, or trigger transactions, it becomes part of your operation. Sphere builds guardrails so you always know who approved what, what system was touched, and why an action happened.

  • Human approval where it matters
    Agents prepare actions. Your team approves or escalates high-impact steps.
  • Scoped system access
    Agents only see and use the tools and data you allow.
  • Clear activity record
    Every action, decision, and system call is logged and searchable.
  • Ongoing performance checks
    Behavior is tested as workflows and models change.

Let’s build an agent for your case.

Get in Touch

Where agents help first

Companies move from AI that answers questions to AI that runs parts of operations. This is where most systems break – and where we focus delivery.

Customer Operations — Case Resolution

Problem

Your team spends time reading, sorting, and routing cases instead of solving them. Response times grow and quality varies by agent.

Agent

The agent reads incoming requests, classifies the issue, drafts a response, opens or updates the case in your CRM, and routes it to the right team when confidence is low.

Result

Customers get faster responses, agents focus on resolution, and managers see consistent quality with a full case history and audit trail.

Problem

Invoice matching and exception handling slow down the close and increase errors.

Agent

The agent extracts data from invoices and payments, matches them to POs and ledger entries, flags exceptions, and prepares approval packages for finance.

Result

Invoices move faster, exceptions surface early, and the close process runs with fewer last-minute corrections and a complete audit log.

Finance Operations — AP/AR & Close

Risk & Compliance — Decision Support

Problem

Teams spend days collecting evidence before they can make a risk or compliance decision.

Agent

The agent gathers data from internal systems, applies policy rules, and assembles a decision packet with sources and reasoning for reviewer approval.

Result

Decisions happen faster, reviews are easier to justify, and every outcome is traceable for audits and regulators.

Problem

Delays and disruptions are discovered late, forcing manual replanning across teams.

Agent

The agent monitors signals from your ERP and logistics systems, detects issues, proposes updated plans, and coordinates changes across systems.

Result

Teams respond earlier to disruptions, deliveries stay on track more often, and planners spend less time on manual coordination.

Supply Chain — Exception Handling

Customer Stories

AI Services and Readiness AI Solutions
AI-Powered Onboarding Assistant for PetroLedger
AI-Powered Onboarding Assistant for PetroLedger

PetroLedger, a global financial services firm, cut ramp-up time by 120% and saved $1.2M annually. Sphere built a generative AI onboarding platform that preserved expertise, sped up training, and turned knowledge retention into measurable savings.

View Case Study
Legacy Modernization Strategy & Transformation
Software Division Carve-Out by Private Equity Firm
Software Division Carve-Out by Private Equity Firm

After a carve-out from a global enterprise, a newly formed software company had just six months to build its tech stack from scratch. Sphere partnered to implement NetSuite, HubSpot, and a full operational foundation across finance, CRM, and integrations—delivering on time, under pressure, and ready to scale.

View Case Study
AI Services and Readiness Strategy & Transformation
Increasing Efficiency with GenAI Summarization
Increasing Efficiency with GenAI Summarization

Facing challenges with time management in customer service, Ascentia collaborated with Sphere to implement a Generative AI solution, dramatically improving call summarization times and customer satisfaction. This case study delves into the structured approach—from the initial GenAI workshop to the successful Proof of Concept—showcasing how targeted technological solutions can transform operational efficiencies.

View Case Study

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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UNITED STATES 2025

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Sphere in Numbers

We understand that actions speak louder than words and numbers
but here are some key facts about us.

20

Years of Experience

230

Delivered Projects

200+

Senior Specialists

94%

Satisfaction Rate

Bring one workflow. Leave with
a 6-week delivery plan

  • Workflow shortlist + feasibility
  • Target architecture + control points
  • First milestone + success metrics

Flexible, fast, and focused — Sphere solves your tech and staffing challenges as you scale.

Luke Suneja

Client Partner

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Frequently asked question

Agentic AI refers to AI systems that can take actions autonomously across your tools and workflows, not just answer questions. These agents read data, execute tasks in connected systems, and route decisions to humans when approval is needed. Unlike traditional AI assistants, agentic systems operate as part of your production environment.

Sphere embeds cross-functional AI engineering pods directly into your operations to build production-ready agents in 4-7 days. We focus on workflows that run inside your existing systems with built-in approval gates, audit trails, and scoped permissions—not standalone pilots that never reach production.

A production agent is an AI system that runs live workflows in your actual business environment. It connects to your CRM, ERP, data platforms, and internal tools with proper permissions, logs every action, includes human approval points for high-risk decisions, and operates under monitoring and governance controls.

Initial deployment starts in 4-7 days with a working agent workflow. Full production implementation follows a 6-week phased approach: Week 0-1 for workflow mapping, Weeks 2-4 for build, Weeks 4-6 for reliability validation, and Week 6+ for responsible scaling.

No. Sphere builds portable system designs that support multiple models, APIs, and platforms. You can change providers without rebuilding workflows. We select appropriate models based on your workflow requirements and can swap or test alternatives as needed.

Agents prepare actions and present them to your team for approval before execution. You define which steps require human review based on risk level, dollar thresholds, customer impact, or compliance requirements. Every approval decision is logged in the audit trail.

Yes. Agents operate inside your environment with scoped permissions—they only access the specific data and tools you authorize. All system calls are logged, identity management integrates with your existing access controls, and data never leaves your security perimeter unless you explicitly configure external connections.

Workflows with repetitive steps, multiple system handoffs, and clear decision logic see immediate impact. Common starting points include customer case resolution, invoice matching and AP/AR processing, compliance evidence gathering, and supply chain exception handling. You can bring your own workflow or choose from Sphere’s proven use case library.

Sphere builds automated testing, regression checks, and performance scorecards into every deployment. You get an operations dashboard showing full workflow traces, decision logs, and audit trails. Ongoing evaluation tracks accuracy across real workflow paths as your systems and data change.

 Agents include fallback paths and escalation rules. When confidence is low or unexpected conditions arise, the system routes to human review rather than proceeding. Every action is logged, so you can trace what happened, why a decision was made, and which data informed it. Mistakes become learning opportunities captured in the evaluation harness.

No. Sphere’s embedded pods bring the AI/ML engineers, data engineers, MLOps engineers, and RAG/agentic architects needed to build and maintain production agents. Your team provides workflow knowledge and business logic. We handle the technical implementation, monitoring, and ongoing improvement.

Pricing depends on workflow complexity, systems involved, and deployment scope. The 6-week initial deployment includes workflow mapping, agent build, reliability validation, and handover with operations documentation. Contact us for a custom quote based on your specific use case.

Agents are designed to evolve with your operations. The Sphere team provides runbooks and an ops dashboard for ongoing management. As workflows change, you can add new tools, modify approval points, or expand autonomy where controls prove stable. We support continuous improvement, not one-time delivery.

No. We build systems you own and can operate independently. All code, configurations, and documentation transfer to your team. The architecture supports provider changes, and we provide handover materials so internal teams can manage agents without ongoing Sphere involvement if desired.

Book an Agentic Fit Call. We’ll review your workflow shortlist, assess feasibility, map target architecture with control points, and deliver a 6-week plan with defined milestones and success metrics. You leave with a clear path from current state to production agent.

Get Started Today

Get in touch now