AI Chat Interface
A secure, evidence-backed conversational experience with first-class citations, structured outputs (tables, formulas, figures), and permissioned sharing tailored to business audiences for faster review and decision cycles.
Trusted AI operations
Operationalize AI agents and workflows that deliver measurable business value.
optAIze is a control plane for AI systems that must be traceable, evaluatable, and governed—whether the run is a tool-using agent or a composed workflow—so teams can iterate quickly, backtest changes, and prove ROI before scaling.
Real value usually comes after multiple iterations. optAIze makes that iteration loop fast, evidence-backed, and compatible with enterprise security boundaries.
Trust makes ROI sustainable.
optAIze keeps agents and workflows accountable with citations, access control, lineage, and repeatable evaluation so teams can move faster with confidence and defend outcomes when it matters.
Why AI systems fail in production
Most AI deployments—agents and workflows alike—stall when they leave the demo environment. Outputs can’t be reproduced, decisions can’t be explained, and iteration slows until ROI is out of reach.
Auditability gaps
Without citations, lineage, and run history, audit readiness becomes a scramble that delays launch decisions.
Repeatability breaks
If outputs cannot be replayed, each iteration is a new risk, slowing improvement cycles and increasing rework.
Explainability debt
Black-box systems erode confidence. Without clear reasoning paths, leaders hesitate to expand AI into business-critical work.
ROI proof loop
Operationalizing AI—whether you’re shipping agents, workflows, or both—is about proving value quickly and repeatedly. optAIze supports a disciplined loop for doing that without guesswork.
Define success criteria upfront
Set clear targets for accuracy, safe abstention when answers aren’t in the source content, response time, and cost per agent or workflow run.
Optimize one change at a time
Swap a model, prompt, or dataset, then run batch evaluations and compare results with scoring and analytics to confirm impact.
Product pillars
Five integrated subsystems that make AI agents and workflows observable, evaluatable, governable, and faster to improve.
AI Agent & Workflow Operations
The control plane for debugging, measuring, comparing, and re-running agents and workflows with evaluation and regression testing to prove improvements before rollout—including batch backtests and clear winner/loser comparisons—whether the run is a deterministic workflow or a tool-using agent.
AI Agent & Workflow Serving
Runtime execution with REST APIs for synchronous and batch invocation of both agents and workflows through a single surface, designed to integrate with existing systems and scale value delivery.
AI Trace & Run Intelligence
End-to-end traces, run metadata, captured inputs and outputs, and replay support for audits and faster iteration cycles.
AI Document Intelligence
Versioned document ingestion with taxonomy-driven indexing, OCR, preserved tables and figures, and citation-ready lineage to reduce rework across iterations.
How it works
A straightforward flow that keeps AI grounded—for both agents and workflows—while enabling fast iteration and measurable value.
1) Ground agents and workflows in trusted documents
Ingest and version the sources that matter, with ACLs and lineage preserved for every document.
2) Compose agents and workflows with clear inputs
Define the steps, tools, prompts, datasets, and evaluation criteria so intent and constraints are explicit.
3) Execute and evaluate at scale
Run agents and workflows against repeatable inputs, backtest changes with datasets, and compare outcomes across models or data versions.
4) Inspect, replay, and govern
Trace decisions, replay runs, and maintain audit trails so teams can ship improvements with confidence.
5) Repeat the loop as models change
When models evolve or data shifts, re-run evaluations to verify quality, cost, and latency remain within target thresholds.
Trust and governance by design
optAIze treats governance as a core system feature, enabling security reviews without slowing iteration.
Citations and source control
Outputs are tied to their sources with links and citations, so users can verify and inspect evidence without manual rework.
ACLs and data boundaries
Access controls are enforced across documents, agents, workflows, and outputs—including the tools an agent is allowed to call—so data boundaries stay intact as usage expands.
Lineage, replay, audit trails
Every run captures inputs, outputs, and decisions so teams can replay results and answer governance questions quickly.
Enterprise deployment & cloud trust model
optAIze is designed for enterprise security reviews and data residency requirements without sacrificing time-to-value.
Deployed in your Azure environment
optAIze runs inside customer-owned Azure subscriptions, with document content, indexes, and extracted artifacts stored in customer-controlled storage.
Security controls stay intact
LLMs are invoked through Azure-hosted services under enterprise security controls, with integration into existing identity, network, and security policies. No customer data is used to train foundation models.
Use cases in enterprise and regulated environments
Designed for teams that need defensible AI outcomes and clear business impact.
Policy and regulatory analysis
Shorten review cycles by comparing regulatory texts with traceable citations and clear provenance.
Risk and compliance reviews
Evaluate agents and workflows with repeatable testing and audit trails that support evidence-based approval.
Knowledge operations in high-stakes domains
Provide staff with evidence-backed answers while preserving access controls, helping reduce time spent on manual lookup.
Operational agent and workflow automation
Backtest agent and workflow changes with batch runs to validate impact before scaling usage.
FAQ
Answers for skeptical enterprise buyers evaluating AI systems.
optAIze keeps citations and document links attached to outputs with lineage so reviewers can inspect the evidence and context.
Use evaluation, batch runs, and backtesting to verify accuracy, abstention when answers aren’t available, response time, and cost per run before expanding use.
No. optAIze provides the control plane and observability needed to operate AI systems. Engineering judgment remains central.
optAIze supports evaluation and regression testing so teams can compare outputs across prompts, tools, models, and datasets before adoption—for agent runs and workflow runs alike.
optAIze is deployed within customer-owned Azure subscriptions, integrates with existing identity and network policies, and keeps document content in customer-controlled storage.
An agent is a goal-directed run that can select tools and take multiple steps to reach an outcome. In optAIze, agents are grounded in your documents, bounded by your ACLs, and produce the same citations, traces, and evaluations as any workflow run—so you can prove what happened and why.