Augmented Intelligence

Name: Augmented Intelligence
Brand: Augmented Intelligence

Augmented Intelligence (AUI) provides Apollo-1, a neuro-symbolic model architecture and agent deployment stack that enables task-oriented conversational agents emphasizing controllability, explicit policy enforcement, and traceable decision artifacts.

Est 2017|Valuation $750M|Raised $63.1M|18 Ref Customers

LinkedIn 17K|Web traffic 5K|FTEs 58

MarketsAgent Builder Platforms

01Competitive Landscape

| Aspect | AUI (Apollo-1) | Rasa | Kore.ai | |--------|---------------|------|---------| | Starting Price | Not publicly listed (sales-led / access on request) | Published enterprise pricing information exists, but enterprise contracts require vendor engagement | Typically quote-based enterprise pricing (public list pricing not fully transparent) | | Best For | Enterprises requiring policy-compliant task agents with traceability of decision artifacts | Teams needing control, on-prem/self-host options, and conversational orchestration | Large enterprises/contact centers seeking an end-to-end conversational CX suite | | Key Strength | Emphasis on policy compliance and traceability of reasoning artifacts (subject to validation) | Deployment flexibility, mature framework, and on-prem options | Broad enterprise features, integrations, and CX automation focus | | Key Weakness | Limited independent user reviews and limited public security attestations and pricing transparency | Requires in-house expertise; bot development/maintenance can be complex | Implementation complexity and cost can be cited as drawbacks | - Direct competitors and differentiators - AUI differentiates primarily on an architecture that combines generation with explicit policy enforcement and on providing traceability artifacts for agent decisions. - Rasa differentiates on developer control, customization, and deployment flexibility. - Kore.ai and Cognigy differentiate on packaged contact center integrations, partner ecosystems, and breadth of CX features. - Feature comparison: where AUI may excel vs lag behind - Potential areas of strength - Policy adherence and deterministic constraint enforcement (contingent on validation) - Traceability if the platform exposes actionable audit artifacts - Potential areas of weakness - Ecosystem maturity (integrations and partner network) - Publicly verifiable production footprint and named references - Visibility of compliance documentation (SOC 2, ISO) - Pricing comparison: value proposition vs alternatives - AUI’s pricing opacity and sales-led approach make benchmarking difficult. Many established platforms also use quote-based pricing but typically have broader public partner ecosystems and reference customers. - Market share and adoption trends - No authoritative public market-share data for AUI was identified. Press coverage indicates AUI is in a growth phase and the vendor has signaled an upcoming general availability timeframe in its product materials.

01Competitive Landscape

Aspect	AUI (Apollo-1)	Rasa	Kore.ai
Starting Price	Not publicly listed (sales-led / access on request)	Published enterprise pricing information exists, but enterprise contracts require vendor engagement	Typically quote-based enterprise pricing (public list pricing not fully transparent)
Best For	Enterprises requiring policy-compliant task agents with traceability of decision artifacts	Teams needing control, on-prem/self-host options, and conversational orchestration	Large enterprises/contact centers seeking an end-to-end conversational CX suite
Key Strength	Emphasis on policy compliance and traceability of reasoning artifacts (subject to validation)	Deployment flexibility, mature framework, and on-prem options	Broad enterprise features, integrations, and CX automation focus
Key Weakness	Limited independent user reviews and limited public security attestations and pricing transparency	Requires in-house expertise; bot development/maintenance can be complex	Implementation complexity and cost can be cited as drawbacks

Direct competitors and differentiators
- AUI differentiates primarily on an architecture that combines generation with explicit policy enforcement and on providing traceability artifacts for agent decisions.
- Rasa differentiates on developer control, customization, and deployment flexibility.
- Kore.ai and Cognigy differentiate on packaged contact center integrations, partner ecosystems, and breadth of CX features.
Feature comparison: where AUI may excel vs lag behind
- Potential areas of strength
  - Policy adherence and deterministic constraint enforcement (contingent on validation)
  - Traceability if the platform exposes actionable audit artifacts
- Potential areas of weakness
  - Ecosystem maturity (integrations and partner network)
  - Publicly verifiable production footprint and named references
  - Visibility of compliance documentation (SOC 2, ISO)
Pricing comparison: value proposition vs alternatives
- AUI’s pricing opacity and sales-led approach make benchmarking difficult. Many established platforms also use quote-based pricing but typically have broader public partner ecosystems and reference customers.
Market share and adoption trends
- No authoritative public market-share data for AUI was identified. Press coverage indicates AUI is in a growth phase and the vendor has signaled an upcoming general availability timeframe in its product materials.

Aspect

AUI (Apollo-1)

Rasa

Kore.ai

Starting Price

Not publicly listed (sales-led / access on request)

Published enterprise pricing information exists, but enterprise contracts require vendor engagement

Typically quote-based enterprise pricing (public list pricing not fully transparent)

Best For

Enterprises requiring policy-compliant task agents with traceability of decision artifacts

Teams needing control, on-prem/self-host options, and conversational orchestration

Large enterprises/contact centers seeking an end-to-end conversational CX suite

Key Strength

Emphasis on policy compliance and traceability of reasoning artifacts (subject to validation)

Deployment flexibility, mature framework, and on-prem options

Broad enterprise features, integrations, and CX automation focus

Key Weakness

Limited independent user reviews and limited public security attestations and pricing transparency

Requires in-house expertise; bot development/maintenance can be complex

Implementation complexity and cost can be cited as drawbacks