Stealth · Enterprise AI · 2026
AI agents are in production. The infrastructure to trust them is not. Every enterprise has the problem. We are building the solution — an intelligence fabric that drives performance, and a quality fabric that makes agents worthy of trust.
Intelligence · Optimization · Quality
Every major enterprise is deploying AI agents. But agents are fundamentally different from the software systems enterprises know how to govern. They are probabilistic, stateless, and opaque — and the consequences of getting them wrong in critical workflows are severe.
Agents lose context between sessions, forcing users to repeat themselves and breaking continuity across complex workflows.
Behavior changes unpredictably across model updates, prompt changes, and new data — with no mechanism to detect or prevent it.
Errors in tool usage, data grounding, and decision logic surface only after damage is done — with no audit trail to explain why.
"Every era of enterprise computing produced a system of record. CirceAI is building it for the age of AI — ExaSense, the platform for trusted enterprise intelligence."
Our founders bring deep enterprise database systems experience. Databases earned their place as infrastructure by enforcing integrity — not hoping for it. We apply that same discipline to AI agents through two purpose-built fabrics: an Intelligence Fabric that gives agents the memory and reasoning to perform, and a Quality Fabric that makes their behavior trusted, auditable, and certifiable for production.
Together they implement what we call ReReAct — Reasoning, Recall, and Action — a framework that closes the gap between what agents can do and what enterprises can trust them to do autonomously. Performance and trust are not just operational outcomes. They are the conditions for AI to deliver measurable business value — and for enterprises to finally answer the question every CFO and board is asking: what is our return on AI?
"We don't eliminate uncertainty in AI agents.
We make it measurable, testable, and worthy of trust."
Agents that reason, recall, and act with continuity — across sessions, workflows, and organizational contexts. The Intelligence Fabric gives agents persistent memory so knowledge compounds over time, while intelligent model routing matches each task to the right model at the right cost. Enterprises gain full visibility into AI spend across every agent and workflow, turning token consumption from an unpredictable cost into a governed, optimizable line item with a measurable return.
Trusted evaluation and QA for every agent before and after deployment. The Quality Fabric continuously scores, validates, and certifies agent behavior — so enterprises can release with confidence, compliance teams have the evidence they need, and regulators can audit decisions rather than guess at them. Trust is not assumed. It is measured.
Enterprise AI agent adoption is accelerating faster than the governance infrastructure to support it. The companies that define the infrastructure layer in this window will own it. We are building for that position — before the category consolidates.
of enterprise AI deployments report unexpected agent behavior within the first 90 days of production.
average cost of an AI-driven compliance failure in regulated industries, before litigation.
platforms today combine persistent agent memory, coordinated execution, and continuous quality assurance in a single system.
EU AI Act and emerging US regulation require documented evidence of agent reliability in high-risk deployments.
Who is buying this
Accountable for agent deployments that actually work at enterprise scale. Needs infrastructure that integrates with existing systems, doesn't create new governance debt, and gives the organization a platform to build on — not a patchwork of point tools.
Watching AI token spend proliferate across dozens of models, vendors, and teams with no unified visibility. Needs intelligent model routing that matches cost to task complexity — and a reporting layer that makes AI infrastructure spend as accountable as any other line item.
Deploying agents inside supply chain, finance, or operations to drive real outcomes. Needs agents that remember context, execute reliably across complex workflows, and don't require constant human intervention to stay on track — so the business case holds in production, not just in demos.
Responsible for ensuring agents operating in regulated workflows meet audit, data governance, and compliance requirements. Needs a quality and integrity layer that produces evidence — not just dashboards — and that can certify agent behavior before it touches sensitive systems or customer data.
The market is moving fast — well-funded companies own fragments of the intelligence and quality layers. Nobody has combined both into a single enterprise platform. That is the category we are creating.
| Company | Layer | What they provide | What they miss | ExaSense provides |
|---|---|---|---|---|
| Context Providers | ||||
| Atlan / Alation / Collibra | Intelligence | Data catalogs and semantic layers — enterprise knowledge of what data exists and what it means | Know the data but don't connect it to agents in a governed, real-time way across workflows | Intelligence Fabric ingests context from catalogs as one source — grounding agents without being locked to any single catalog vendor |
| Agentic Runtimes & MCP Platforms | ||||
| Snowflake · Natoma acq. May 2026 |
Intelligence | MCP gateway and governed connector library for Cortex Agents — context from Slack, CRM, databases, SaaS via verified MCP servers | Ecosystem-locked to Snowflake data cloud — no cross-platform quality layer, no autonomy scoring, no model routing optimization | Cross-ecosystem Intelligence Fabric that works above any runtime — Snowflake, ServiceNow, or otherwise — with Quality Fabric that no single runtime provides |
| ServiceNow / Salesforce | Intelligence | Agentic workflows with enterprise system connectors inside their own platforms (Now Assist, Agentforce) | Walled inside their own ecosystem — no cross-platform intelligence layer, no independent quality certification | Platform-agnostic infrastructure that governs agents wherever they run — not just inside one vendor's garden |
| LangChain / LangSmith | Intelligence | Open agent framework with orchestration, tracing, and MCP integration | Framework-locked; no persistent enterprise memory; no quality certification or autonomy scoring | Framework-agnostic intelligence and quality infrastructure that works across any agent stack, including LangChain |
| Quality & Evaluation Tools | ||||
| Braintrust | Quality | Eval CI/CD and quality management for AI products | Developer-first evals — no persistent memory, no model routing, no enterprise autonomy certification | Quality Fabric: system-level certification of agent readiness, not just CI/CD eval pipelines |
| Arize AI | Quality | LLM observability and production monitoring | Post-hoc visibility only — no pre-release gate, no memory layer, no autonomy confidence scoring | Quality Fabric answers "is it ready to deploy?" — not just "what did it do in production?" |
| Patronus AI | Quality | Hallucination detection and safety evaluation | Safety is not operational reliability — no memory, no routing, no enterprise audit trail | Trust is the output of both fabrics together — safety is one input to a broader certification |
Context providers know the data but don't govern agents. Agentic runtimes govern agents inside their own ecosystem but can't span the enterprise. Quality tools evaluate fragments but have no intelligence layer. No company sits above all three — with a cross-ecosystem Intelligence Fabric and a Quality Fabric on a unified system of record. ExaSense is that platform.
We are going deep in two verticals before expanding — both defined by autonomous decisions at scale, tight regulatory accountability, and organizations already feeling the cost of agents they cannot fully trust.
Agents managing procurement, logistics, and vendor operations at global scale. Where a single point of failure propagates across thousands of nodes — and the tolerance for unexplained agent behavior is zero.
Procurement · Logistics · Vendor opsTrading, credit, fraud, and compliance workflows running at machine speed with human-grade accountability. Where the audit trail is not optional and the regulator is always watching.
Risk · Compliance · Trading · Lending"She transformed those who came unprepared —Homer, The Odyssey · The founding principle of CirceAI
but Odysseus, forewarned and grounded,
walked through her island as master."
We are in stealth and intend to stay there for now. We are having two conversations: with enterprises ready to shape the infrastructure for their agent stack, and with investors who understand that agent intelligence is a platform-scale opportunity.
If either describes you, we would like to hear from you.
We are looking for supply chain and financial services teams deploying agents at scale — willing to co-develop the infrastructure that makes them trustworthy. Early partners shape the platform and gain a durable advantage.
[email protected]We are raising a pre-seed round from investors who recognize that trusted enterprise intelligence is one of the defining infrastructure categories of this decade — and that the window to lead it is now.
[email protected]