AI that runs in production, not just in the demo.
You've seen what the model can do. Sphere gets it running inside your real systems, on live data, behind your permissions and with monitoring in place, then hands it to your team to own. Most engagements go from kickoff to production in about 90 days.
Book a pilot-to-production review
Book a pilot-to-production review
A working session with a Sphere solution architect. Bring your pilot, PoC or use case and leave knowing what it takes to go live.
Technology partners
AWS
Google Cloud
Microsoft Azure
Databricks
Snowflake
Anthropic Claude
The demo was the easy part.
A pilot proves the model can do the job. Production proves it can do the job every day, for every user, inside your rules. When projects stall, it's almost never the model. It's everything around it.
Sphere builds the right-hand column. Here's how.
See the four phasesFrom use case to live system, one phase at a time.
A fixed plan, not an open-ended retainer. Each phase ends with something you can see and use, so you always know where the project stands.
- Weeks 1–2
Production review
We look at what you've built or scoped: the use case, the data it needs, who will use it and what your security team will ask. Then we tell you plainly whether it's ready to go live and what's missing.
You getA go/no-go, the target architecture and a fixed scope for the build. - Weeks 2–5
Data & architecture
We connect the real sources, map permissions from your existing systems and build an evaluation set from real questions, so quality is measured, not guessed.
You getA working pipeline on live data and a quality baseline to measure every release against. - Weeks 5–12
Build & ship
We build it into the tools your people already work in and roll it out to a first group of real users, with guardrails, fallbacks and human review where the stakes are high.
You getA live system in your environment, used by real people on real work. - Week 12 onward
Run & hand over
We monitor quality, cost and usage, retrain when the data shifts, and train your team to run it. Stay on for support, or take the keys.
You getDashboards, runbooks and a team that owns the system.
What it looks like when it actually ships.
Four systems Sphere took to production, and the numbers their owners report.
saved every year from faster onboarding and retained expert knowledge
PetroLedger kept its retiring experts' knowledge, and cut new-hire ramp by more than half.
Sphere built a Digital Twin Knowledge Platform that captures how senior experts actually work and makes it available to every new hire, grounded in the company's own documents.
Read the PetroLedger storyyear-over-year revenue increase
A $200M distributor started selling what its customers were about to need.
An AI assistant combines CRM data with third-party market trends to recommend which SKUs to push, pricing and negotiation ranges, and which customers to target, backed by predictive demand analytics.
Read the distribution storyin annual labor savings
A medical device manufacturer took the manual work out of order entry.
The platform reads purchase orders from fax, email and PDF, checks them against ERP records, flags mismatches for a person to review and sends clean orders to the order management system.
Read the MedTech storyfaster call summarization
A global health company's agents stopped writing call notes by hand.
After a GenAI workshop and solution design, Sphere built a proof of concept that summarizes calls automatically inside the CRM, running alongside the agents' existing workflow.
Read the healthcare storyFrom the people who signed off on it.
What client leaders say about the AI systems Sphere put into production for them.
“Sphere deployed a production-ready RAG pipeline in five weeks. […] This was not a generic AI implementation; it was an enterprise knowledge system designed for a highly regulated professional services environment.”
“Sphere's AI team has been a true strategic partner in helping us elevate our AI capabilities and modernize critical business operations. […] An exceptional partner for companies looking to operationalize AI and drive measurable ROI.”
“Sphere approached AI transformation the right way — starting with workflows, data governance, and operational trust instead of simply deploying another AI tool.”
“Their Precision-Driven Engineering framework helped us implement Generative AI in a secure, scalable, and standardized way across teams while accelerating modernization of a critical legacy platform.”
“Sphere was a valuable partner in helping us advance our AI-native product lifecycle initiatives. Their combination of product thinking, engineering expertise, and execution discipline enabled the delivery of impactful solutions.”
“Sphere's AI-powered software and product engineering capabilities enabled us to achieve our objectives faster while improving operational efficiency across the software development lifecycle.”
Architects, not account managers.
Your review is run by the people who design Sphere's production AI systems, and they stay with the project after you sign.
Built products and led high-scale engineering teams at HP Software, Gett and Google before leading Sphere's technology practices.
Architects enterprise AI platforms that turn complex operations into secure, production-ready systems built to hold up at real-world scale.
Led high-scale operational systems and modernization at Gett and bp pulse. Now builds automation-driven AI platforms and intelligent workflows.
More than a decade designing and scaling software from first MVP to enterprise-level systems across FinTech, transportation and real-time streaming.
Built on your stack. Owned by you.
Deployed to your AWS, Azure or Google Cloud account. Your data doesn't leave your environment.
Repositories, documentation and runbooks are yours from day one. Nothing only Sphere can maintain.
The architecture keeps the model replaceable, so moving from one provider to another is a config change, not a rebuild.
OpenAI
Anthropic Claude
Google Gemini
Mistral
Llama
AWS Bedrock
Azure OpenAI
Google Cloud
Databricks
Snowflake
Salesforce
SAP
NetSuite
Dynamics 365
HubSpot
Atlassian
Kubernetes
Docker
Terraform
monday.comModels, clouds, business systems and deployment tooling we implement on today.
21 years of engineering. Our top 20 clients stay 7+ years.
Independent recognition for the AI work Sphere delivers, from Clutch reviewers to global AI award juries.
Top Artificial Intelligence Company · US 2025
Top AI Code Generation Company · US 2025
Top Chatbot Company · US 2025
Top Recommendation Systems Company · US 2025
Top AI Text Generation Company · Florida 2025
Top App Development Company · Manufacturing 2025
Global Generative AI Award · 2026 Winner
Best in AI for Documentation Validation · 2026
Best in AI-Powered Invoice Auditing · 2026
Global Recognition Awards · 2026
Global Tech Award Winner · 2026
Most Innovative Technology Consulting Firm · 2026
What teams ask before they start.
Don't see yours? Ask it on the review. That's what it's for.
Book a pilot-to-production reviewWe already have a pilot. Do we have to start over?
No. We start from what you've built and reuse whatever holds up: prompts, data work, evaluation examples, integrations. The review tells you exactly what stays, what changes and why. You also don't need perfectly clean data to begin; getting your real data production-ready is part of the work.
How long does it take to get to production?
The review takes one to two weeks. Most first systems are live with real users in about 90 days from kickoff. Larger programs that span several teams roll out in phases, with something live at the end of each one.
What does it cost?
It depends on the scope, the data and how many systems it touches. The review ends with a fixed scope and a fixed price for the build, so you know the number before you commit to anything.
Will we be locked into a model or a cloud provider?
No. We work across OpenAI, Anthropic Claude, Google Gemini, AWS Bedrock, Azure OpenAI and open-source models like Llama and Mistral, and we design the system so the model can be swapped later. It runs in your cloud account and you own the code.
How do you handle security and compliance?
From the first phase, not after launch. Every system ships with role-based access, audit trails and human approval where decisions carry risk, and is designed to meet GDPR, HIPAA, SOC 2 and industry-specific requirements. We regularly work with compliance teams in financial services, healthcare and energy.
Can you work alongside our own engineers?
Yes. Some clients have Sphere deliver end to end; others add our architects and engineers to their team for a specific phase. Either way, your people are involved throughout so they can run the system when we step back.
Your pilot proved the idea. Let's put it to work.
Book a review with a Sphere solution architect. You'll leave with:
- A clear go/no-go on taking your use case to production
- The target architecture on your own stack
- A timeline and a fixed scope for the build
Book a pilot-to-production review
Tell us where the project stands. We'll reply within one business day to set a time.