Olakai

Proves the value of AI coding tools and agents while helping teams forecast spend, manage risk, and improve engineering ROI.

Helps enterprises measure workforce AI value, optimize licenses, discover shadow AI, and manage sensitive-data and policy risks across employee tools.

Measures AI adoption, productivity gains, and business value across enterprise tools and workflows to guide investment and rollout decisions.

Products: Olakai Agentic, Olakai Assistive

Tracks AI costs and license utilization to identify waste, allocate spending, and optimize investments through cost attribution, consolidation, and budget controls.

Products: Olakai Agentic, Olakai Assistive

Monitors autonomous AI agents and models for policy compliance, performance, and risk; provides enforcement, audit capabilities, and analytics to govern AI-driven actions.

Products: Olakai Agentic, Olakai Assistive

Measurement of AI coding tool and agent adoption, generated code, productivity impact, and costs to guide engineering AI investments, vendor evaluations, and rollout decisions.

Products: Olakai Agentic

Olakai Agentic

Olakai Agentic measures the ROI, spend, and risk of AI coding tools and autonomous agent workflows. Its Coding IQ module combines provider usage and cost data with pull-request analytics to show adoption, developer-level spend, and AI-assisted versus non-AI cycle times. Budgets, run-rate forecasts, and alerts help teams manage costs, while agent execution logs and workflow-level policy enforcement support governance. The product also translates measured productivity gains into dollar value and AI Equivalent Engineers.

• Prove Engineering Velocity Gains
  Compare AI-assisted and non-AI pull-request cycle times using actual repository data, then express measured productivity gains in business terms for engineering and finance leaders.
• Forecast Spend And Prevent Overruns
  Set budget limits across multiple organizational lenses and receive alerts when spending reaches configured thresholds or its trajectory is projected to exceed a limit, helping teams address overruns before the invoice arrives.
• Trace Decisions And Enforce Policies
  Review what an agent received, which tools it called, and what decisions and outcomes followed. Apply controls at the workflow level and maintain execution records for audits and investigations.
• Identify Adoption And Idle Licenses
  See which developers use AI coding tools, how frequently they use them, and how their cycle times compare with peers. Use cohort and cost data to guide enablement and identify licenses to reclaim.
• Measure Agent Performance And Outcomes
  Use execution and outcome data to assess agent performance, efficiency, and contribution, and to identify workflows that may need optimization or further governance.
• Detect Sensitive Coding Data
  Identify PII, PHI, and secrets in coding-agent traffic so teams can assess sensitive-data exposure in their AI coding workflows.
• Reconcile Costs By Developer
  Compare estimated and reconciled actual costs at the developer level, making it easier to understand who is using each tool and what that usage costs.
• Get Data-Backed Recommendations
  Ask questions about coding-tool spend and receive reasoned answers based on connected data, including potential actions and their dollar impact.
• Reduce Model Routing Costs
  Use model routing to reduce AI costs while maintaining output, as described in the product’s pricing information.
• Connect Coding And Provider Data
  Bring pull-request analytics and provider usage data into a shared view to measure engineering velocity, adoption, and spend across coding tools.

Olakai Assistive

Olakai Assistive measures adoption, productivity, license use, and risk across employee-facing AI tools, including approved copilots, unapproved tools, and AI features embedded in SaaS apps. Its browser extension surfaces tools employees use, while analytics show value created, time saved, usage, license utilization, prompt quality, and data exposure by tool and team. Organizations can identify idle seats, assess shadow AI risk, and apply content policies and governance controls. Dashboards and Kai’s conversational analysis help leaders review AI program performance and make evidence-based decisions.

Category: AI Governance Software

• Reveal Every AI Tool In Use
  Gives organizations a clearer inventory of employee AI use across approved and unapproved tools, including applications that may otherwise be difficult to see.
• Measure Time Saved And Value Created
  Helps leaders assess what workforce AI tools return by connecting usage with time saved and dollar-denominated value.
• Surface Shadow AI And Its Risks
  Makes unapproved AI use more visible and helps security and compliance teams prioritize tools and teams for review.
• Identify Sensitive Data Exposure
  Helps organizations assess where sensitive data may be exposed through employee AI tools and prioritize governance attention.
• Identify Idle Seats And Renewal Options
  Helps organizations find unused seats and overlapping subscriptions before renewal, using actual usage to inform license and consolidation decisions.
• Enforce Policies Across AI Tools
  Lets organizations apply controls to employee AI use and address policy risks alongside tool usage and data exposure.
• Compare Adoption Across Teams And Roles
  Shows how AI adoption differs across organizational groups so leaders can identify where tools are gaining traction and where further action may be needed.
• Review Governance Status Beside Usage
  Helps teams review an AI tool’s governance and licensing status in the same view as its usage, value, and risk information.
• Review AI Value, Cost, And Risk
  Brings key workforce AI measures into a single overview to support review of value, time saved, costs, and data risk.
• Get Reasoned Answers In Plain English
  Helps users explore AI program data conversationally and get answers to questions about workforce tools and their performance.
• Assess Prompt Quality By Tool
  Adds prompt quality information to tool-level analysis, helping teams review AI interactions alongside their outcomes and risks.