Agents that act on what you know.
A production-grade runtime for AI agents in regulated environments: model routing, tool orchestration, evaluation harnesses, observability and governance. Grounded in governed knowledge, so AI safely executes decisions instead of just generating text.
Outcome-first
The platform is the control plane. KPI movement is the outcome.
Multi-model
Portable across models and clouds, with predictable operating requirements.
First-class guardrails
Guardrails, auditability and human oversight built in, not bolted on.
Grounded
Agents answer from governed enterprise truth, with citations.
Agentic AI you can deploy in high-stakes workflows.
A value-by-design approach focused on maximizing tangible process outcomes and enabling scalable adoption through systematic reuse.
Agents as the abstraction for end-to-end execution
Agents become accountable units that reason, decide and act across entire processes, not isolated tasks.
This shifts automation toward outcome-driven workflows that evolve without rewriting core logic.
Runtime-first architecture for safety, orchestration and governance
The runtime is the control plane: every action is authorized, explainable and observable.
Orchestration, security, memory and policies live here, independent from any model provider.
Models interchangeable through capabilities and policies
Model choice becomes a policy decision based on cost, latency, quality and compliance constraints.
Provider abstraction avoids lock-in and supports continuous optimization across models and clouds.
Value measured by process outcomes
Success is defined by KPI movement: cycle time, cost-per-decision, error reduction, First-Time-Through.
Agents are designed to own these KPIs, creating direct operational and business impact.
Governance and observability as first-class citizens
Every reasoning step, grounding source, tool call and side effect is traced and auditable.
This is designed to support safe scaling, audit readiness and rapid incident resolution, while feeding continuous improvement.
A governed data flywheel powers GenAI with contextual knowledge.
To make GenAI reliable in core processes, the platform treats enterprise data as governed fuel: ownership, provenance, freshness SLAs, retrieval indices, acceptance rubrics and audit evidence.
Ground
Context enrichment
- Purpose
- Anchor answers on current enterprise truth
- Top actions
- Build governed knowledge and memory windows
- Key outputs
- Retrieval indices and contextualized, traceable responses
- KPIs
- Grounding hit-rate, retrieval latency, freshness SLA adherence
Assure
Quality and safety
- Purpose
- Check quality and policy adherence before action
- Top actions
- Acceptance rubrics, golden dataset evals and policy catalogs
- Key outputs
- QA dashboards and audit reports
- KPIs
- Hallucination rate, policy violations and drift alerts
Act
Execution and verification
- Purpose
- Execute decisions safely and prove effects
- Top actions
- Reasoned outputs, tool orchestration and pre/post-condition verifications
- Key outputs
- Action receipts and first-time-through (FTT) proofs
- KPIs
- FTT success rate, exception rate, value realized vs. planned
Learn
Continuous improvement
- Purpose
- Turn feedback into durable knowledge
- Top actions
- Feedback collection and analysis
- Key outputs
- Learning logs and inputs for training
- KPIs
- Time to incorporate feedback, reopening rate of negative feedback
Train
Model improvement
- Purpose
- Expand domain coverage and reduce model error
- Top actions
- Curate corpora, label golden datasets, control bias
- Key outputs
- Dataset cards, versioned data splits and bias reports
- KPIs
- Knowledge coverage, label quality, output accuracy
Data governance
Across every stage
- Purpose
- Assign ownership, risk controls and lifecycle management
- Top actions
- Data product ownership, lineage and consent, retention, access policies, cost tags
- Key outputs
- RACI and catalog entries, approval workflows, FinOps tags
- KPIs
- Policy coverage, access breaches, cost per token or query
Capabilities that enable business solutions.
A modular, portable and extensible platform that enables rapid evolution, seamless model integration and interoperability across diverse technical landscapes.
Better with ATLAS. Strong on its own.
With ATLAS
- Agents grounded in the living knowledge base, through governed APIs and MCP
- Every answer and action traceable to code, rule and regulation
- Least-privilege access per domain, inherited from ATLAS
- Agent actions flow back as knowledge
Standalone
- Bring your own business-owned data products
- Built-in retrieval, grounding and structured context services
- Adapters to your enterprise systems with side-effect contracts
- Same runtime, evaluations and governance
Ready-made agents for core banking workflows.
Disputes and chargebacks
Faster cycle time, better investigations, stronger SLAs.
Enterprise appsRisk and sanctions
Safe, fast underwriting for corporate clients.
Enterprise appsInvestment advisory
Traceable reasoning for pro and HNW decisions.
Enterprise appsOne core flow. KPI movement in 90 days.
Pick a process that moves the P&L. We prove it on the platform, then scale it with the same controls.