Sphere Partners
Generative AI Development Services

Generative AI your team actually uses.

Sphere builds GenAI features and LLM integrations on your data: documentation, summaries, drafts and checks that run inside the tools your team already uses, measured against real inputs before every release. You own the code and it runs in your cloud.

Book a GenAI scoping session
Prototype
Evaluated
In production

Book a GenAI scoping session

Bring the task you want GenAI to handle. Leave knowing if it's feasible, which model fits and what it takes to ship.

Reply within 1 business dayHappy to sign an NDA first

Technology partners

  • AWS
  • Google Cloud
  • Microsoft Azure
  • Databricks
  • Snowflake
  • Anthropic Claude
Why GenAI pilots stall

The playground was the easy part.

A good prompt gets an impressive first answer. Production needs the thousandth answer to be right too, inside your workflow, on your data, at a cost that makes sense.

 
In the playground
In production
Quality
Great on the examples you tried
Scored against real inputs before every release
Grounding
Fills gaps with confident guesses
Grounded in your data, with guardrails and sources
Workflow
Copy and paste from a chat window
Built into your apps, EMR, CRM or document flow
Data
Sensitive data pasted into a public tool
Runs in your cloud, under your data controls
Cost
Nobody watches the token bill
Usage and cost tracked per feature, with the model sized to the task
Models
Tied to whichever model you started with
Model-agnostic, so you can switch when better ones ship

Sphere builds the right-hand column. Here's how.

See the four phases
How it works

From a good first answer to a feature people rely on.

We define what a good output looks like before we build, so quality is measured the whole way, not judged on a demo.

  1. Weeks 1–2

    Use case & success criteria

    We pick the task, collect real examples and agree on what a good output looks like. That becomes the test set every version is scored against.

    You getA go/no-go, a model shortlist and a fixed scope.
  2. Weeks 2–5

    Prototype on real inputs

    We build prompts, retrieval and guardrails, and compare models on quality, latency and cost using your data, not benchmarks.

    You getA working prototype with measured quality.
  3. Weeks 4–10

    Integrate & ship

    We build it into the application or workflow where the work happens, with human review where the output carries risk.

    You getA live feature used by real people.
  4. Week 10 onward

    Operate & improve

    We monitor quality, cost and latency, test new models as they ship and hand your team the runbooks.

    You getDashboards, runbooks and a feature your team owns.
In production today

Generative AI in production today.

GenAI features Sphere took live, and the numbers their owners report.

65%

fewer claims returned for correction

35%less daily outstanding AR
GenAI validation · 2026 Global AI Awards winner

A medical company catches missing documentation before claims go out.

A real-time GenAI validation service inside the AdvancedMD EMR checks documentation against claim requirements and alerts medical assistants to anything missing before submission.

Read the medical claims story
300+
client organizations
28
countries
93%
on-time delivery
75
Net Promoter Score (2026)
In their words

From 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.”
5weeks to a production-ready RAG pipeline
66%better document retrieval accuracy
6 h → secresearch time per engagement
Ian YoungCEO, US Tax Service
79%
reduction in recurring operational costs
“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.”
David GitmanCEO, PrivateJet.com & Monarch Air Group
“Sphere approached AI transformation the right way — starting with workflows, data governance, and operational trust instead of simply deploying another AI tool.”
Ilya KaminskyCIO, Summit Financial Management
“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.”
Dan KirscheCTO, CURO Financial Technologies
“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.”
Matt NormanCPO, DigitalOcean
“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.”
Kinish PatelHead of Engineering & Co-Founder, SevenRooms (a DoorDash company)
Who you'll work with

Architects, not account managers.

Your first call is with the people who design Sphere's production AI systems, and they stay with the project after you sign.

Meet the full leadership team
Boris Korenfeld
Boris KorenfeldGlobal CTO & GM of Tech Practices

Built products and led high-scale engineering teams at HP Software, Gett and Google before leading Sphere's technology practices.

Zeeshan Siddiqui
Zeeshan SiddiquiEnterprise Architect: AI Platforms

Architects enterprise AI platforms that turn complex operations into secure, production-ready systems built to hold up at real-world scale.

Anton Macius
Anton MaciusField CTO

Led high-scale operational systems and modernization at Gett and bp pulse. Now builds automation-driven AI platforms and intelligent workflows.

Oleg Kukareka
Oleg KukarekaSolution Architect

More than a decade designing and scaling software from first MVP to enterprise-level systems across FinTech, transportation and real-time streaming.

No lock-in

Built on your stack. Owned by you.

It runs in your cloud

Deployed to your AWS, Azure or Google Cloud account. Your data doesn't leave your environment.

Your code, your IP

Repositories, documentation and runbooks are yours from day one. Nothing only Sphere can maintain.

Swap models when better ones ship

The architecture keeps the model replaceable, so moving from one provider to another is a config change, not a rebuild.

OpenAIAnthropic ClaudeGoogle GeminiMistralLlama
AWS BedrockAzure OpenAIGoogle CloudDatabricksSnowflake
SalesforceSAPNetSuiteDynamics 365HubSpotAtlassian
KubernetesDockerTerraformmonday.com

Models, clouds, business systems and deployment tooling we implement on today.

Recognition

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

Questions

What teams ask before they start.

Don't see yours? Ask it on the scoping session. That's what it's for.

Book a GenAI scoping session
What kinds of generative AI do you build?

Features that produce or check content inside your workflow: documentation and note-taking, summaries, drafts and replies, document validation, extraction and search with cited answers. We build them into your applications or as LLM integrations with your existing systems.

How do you make sure the output is good enough?

We agree on what a good output looks like before we build, turn real examples into a test set and score every version against it. Where the output carries risk, a person reviews it before it's used.

Is our data safe?

Yes. The system runs in your cloud account under your data controls, with role-based access and audit logging. Your data isn't used to train public models.

How long does it take?

The first phase takes one to two weeks. Most features are live with real users within about ten weeks of kickoff.

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.

What does it cost?

It depends on the use case, data and integrations. The first phase ends with a fixed scope and a fixed price for the build, and we size the model to the task so running costs stay predictable.

Next step

You've seen what GenAI can do. Let's make it work here.

Book a scoping session with a Sphere AI architect. You'll leave with:

  • A feasibility read on your use case
  • The right model and approach for your data
  • A timeline and a fixed scope for the build

Book a GenAI scoping session

Tell us what you want GenAI to do. We'll reply within one business day to set a time.

Reply within 1 business dayHappy to sign an NDA first