Expert AI trusted with your most knowledge-intensive work — in days, not quarters
SphereIQ is a modular AI platform for every use case, data source, and policy. It runs inside your own infrastructure or on any cloud, stays under your team's control, and includes the observability and evaluation tools you need to trust every answer.
Per Section 4.2 of the Credit Risk Policy, the limit is $25M…
Most enterprises are struggling to adopt AI
Generic AI works when the task is generic. The moment a use case requires deep domain knowledge — engineering specs, medical records, financial regulations, manufacturing processes — off-the-shelf models fall short.
The result: pilots that impress in a demo and stall before production, while the knowledge-intensive work that would actually move the P&L stays manual.
of enterprise generative-AI pilots fail to reach measurable P&L impact
MIT Project NANDA, “The GenAI Divide,” July 2025Why off-the-shelf AI can't deliver complex use cases
Models keep getting more sophisticated — but without a context engineering system feeding them the right enterprise knowledge, they can't produce reliable, quality outputs.
No business context
Generic models have never seen your policies, your specs, or your edge cases. They answer from the internet, not from your institution’s knowledge — and in regulated work, “approximately right” is wrong.
Disconnected from the enterprise
Real work lives across document stores, databases, and line-of-business systems behind permissions. Off-the-shelf AI can't reach it — or worse, reaches it without honoring who is allowed to see what.
Unable to improve
Maintaining accuracy and avoiding drift requires curated evaluation datasets, iterative tuning, and ongoing adoption of new AI research techniques. A chat subscription gives you none of that.
The cost, complexity, and time of DIY
Building your own context engineering stack means overcoming the exact challenges that consistently prevent AI projects from reaching production. Compare the two paths:
Stitch together disparate tools. Evaluate, test, and integrate vector stores, rerankers, orchestration frameworks, ingestion pipelines — each with its own roadmap and breaking changes.
Build security & governance from scratch. RBAC, audit logging, PII detection, prompt-injection defense — table stakes for compliance review, absent from every framework.
Fight accuracy drift. No evaluation datasets, no observability. Quality degrades silently and trust erodes with the first wrong answer in front of an executive.
Hit the compliance wall. EU AI Act documentation, data residency, HIPAA — bolted on at the end, when it's most expensive. Most pilots never get this far.
Typical time to governed production: 9–18 months — if it ships at all
How SphereIQ cuts time-to-production from months to weeks
Proven pre-built modules
A library of pre-built modules and agent templates with high out-of-the-box accuracy on your most complex expert tasks. Focus on customization rather than writing infrastructure code.
Explore use cases →Enterprise-ready platform
Retrieval, reranking, orchestration, evaluation, and data ingestion — with the security, governance, and observability required to operate at enterprise scale, built in from day one.
Explore the platform →Top AI experts on your side
Data annotators, AI engineers, and active researchers who apply the latest techniques to tune, optimize, and continuously improve your agents — so accuracy goes up, not sideways.
Explore our AI services →Six modules. Every policy — and every dollar — covered.
SphereIQ is modular by design — adopt what you need today, switch on the rest when you're ready.
Make every token defensible
Most companies scaling AI can't answer a simple question: what is our AI spend actually producing? Token costs live in one place, business outcomes in another. This module joins them.
- ✓ Per-feature attribution — every LLM call tagged to the product surface or agent that generated it, across every provider and framework
- ✓ Cost-per-outcome — token costs joined to conversions, CSAT, resolution rate, and LTV: a number every CFO understands, by feature
- ✓ Eval infrastructure — shadow-test cheaper models, compressed prompts, and new retrieval configs against real traffic without risking production
We instrument one AI feature, join spend to outcomes, and deliver a Defensibility Report — in two weeks. Fixed scope, no commitment required.
Book the DiagnosticNot just a platform. A team that delivers.
Beyond the product, Sphere's AI experts work alongside you to scope, build, and ship your first use case. Low risk, fast results — we help your AI team move much faster without reinventing the wheel, or remove the need for an internal AI team altogether.
- ✓Use-case scoping with a fixed-scope first engagement
- ✓Deployment, tuning, and evaluation handled with your team
- ✓20+ years delivering enterprise software across financial services, healthcare, legal, and manufacturing
30-minute working session with a SphereIQ architect.
You leave with a deployment plan either way.
What's the DIY delay costing you?
Adjust the sliders to estimate the months — and budget — recovered by deploying SphereIQ instead of assembling your own stack.
estimated engineering cost avoided, plus 11 months of earlier time-to-value with SphereIQ
Get the Full BreakdownWhy SphereIQ — answered directly
What makes SphereIQ different from off-the-shelf AI tools?+
SphereIQ is a context engineering platform, not a chat subscription. It connects models to your enterprise knowledge with governed retrieval, confidence-scored citations, built-in security (RBAC, audit, PII guard), and evaluation tooling — the components required for AI to deliver reliable answers on knowledge-intensive, regulated work.
How fast can SphereIQ be live in production?+
Days to weeks, not months. The platform deploys into your own infrastructure or any cloud, pre-built modules cover the common expert use cases, and Sphere's AI team works alongside yours to ship the first use case on a fixed scope.
Where does our data live?+
In your infrastructure. SphereIQ is self-hosted by default, with VPC and air-gapped options. Data residency, GDPR, HIPAA, and EU sovereignty are handled at the architecture layer rather than by contract language.
Which LLMs does SphereIQ support?+
SphereIQ is BYOK (bring your own key): OpenAI, Anthropic, Mistral, Cohere, or a locally hosted open model. You pay inference at provider list price with no SphereIQ markup — the contract is for the platform, not tokens.
Does SphereIQ help with the EU AI Act?+
Yes. The Comply AI module includes a four-step EU AI Act compliance wizard covering Articles 5 and 53 plus Annex III obligations, with GPAI documentation generation — ahead of the August 2, 2026 enforcement date.
Can SphereIQ tell us what our AI spend is actually producing?+
Yes — that's the AI Economics Intelligence™ module. It tags every LLM call to the feature or agent that generated it, joins token costs to business events like conversions, CSAT, resolution rate, and LTV, and reports cost-per-outcome by feature. The fixed-scope AI Spend Diagnostic instruments one feature and delivers a Defensibility Report in two weeks.
Do we need an internal AI team to use SphereIQ?+
No. Sphere's consulting team can scope, build, and operate your use cases end to end — or accelerate your existing AI team so they focus on customization instead of infrastructure plumbing.
Expert AI, without the DIY
Learn how SphereIQ's platform and context layer get a context-aware, governed agent into production in weeks — not months.