Sierra

integrator · United States , San Francisco · founded 2023

Employees: 201-1000 · Stage: Series D · Funding: $1,585,000,000

37.7

moderate

default-balanced
composite score

Enterprise AI agent platform for customer experience, founded 2023 by Bret Taylor (ex-Salesforce co-CEO, OpenAI board chair) and Clay Bavor (ex-Google). Builds always-on conversational agents for support, sales, and operations. $100M ARR reached in under 2 years; customers include Cigna, Blue Cross Blue Shield, Prudential, and one-third of the world's largest banks.

Analyst summary

Sierra became one of the fastest-growing enterprise AI companies in history: $100M ARR in under two years, ~$150M ARR by January 2026, and a $950M raise at $15B+ valuation in May 2026. The customer roster — Cigna, Blue Cross Blue Shield, Prudential, Rocket Mortgage, one-third of the world's largest banks — speaks to the compliance maturity (SOC 2 Type II, ISO 27001, HIPAA, GDPR, BAA available). The model-routing layer atop OpenAI and Anthropic creates an upstream dependency that buyers should map explicitly.

Currently the most enterprise-credentialed AI customer-experience agent vendor. The compliance posture, customer roster, and capital position make Sierra a defensible default pick for regulated enterprise CX deployments — provided you treat the upstream-model routing layer as an explicit dependency in your own risk register.

Rating: recommended

Compliance posture

SOC 2 Type II	Yes (report 2025-08-01)
ISO 27001	Yes
ISO 42001 (AI management system)	No / not disclosed
FedRAMP authorized	No / not disclosed
GDPR compliant	Yes
CCPA compliant	Yes
HIPAA compliant	Yes
NIST AI RMF aligned	No / not disclosed
CSA STAR certified	No / not disclosed
EU AI Act classification	limited_risk

Data handling

Trains on user data	never
Outputs feed model improvement	opt_out_default
Data retention period	Configurable per customer; default 90 days
Can delete user data on request	Yes (SLA 30 days)
Default data residency	US
Encryption at rest	Yes (AES-256 / TLS 1.2+)
Encryption in transit	Yes
DPA available	Yes
Public subprocessor list	Yes
HIPAA BAA available	Yes

IP profile

User owns outputs	yes
Vendor claims output rights	No / not disclosed
Input IP protection	strong
Indemnification offered	Yes
Copyright shield program	No / not disclosed
Commercial use permitted	Yes
Training data provenance	not_applicable
Known IP lawsuits	No / not disclosed

Jurisdiction

Incorporation country	US
Incorporation jurisdiction risk	low
Subject to US jurisdiction	Yes
Subject to EU jurisdiction	Yes
Subject to China jurisdiction	No / not disclosed
Subject to Russia jurisdiction	No / not disclosed
Government data access risk	moderate
Five Eyes aligned	Yes
Adequate privacy jurisdiction	No / not disclosed

Governance

Publishes model cards	No / not disclosed
Publishes transparency reports	No / not disclosed
Has AI ethics board	No / not disclosed
Safety testing disclosed	Yes
Red-teaming program	Yes
Government contracts	No / not disclosed
Terms of service	https://sierra.ai/legal/terms-of-service
Privacy policy	https://sierra.ai/legal/privacy-policy

Incidents on record

No incidents on file.

OWASP LLM Top 10 cross-walk

TrustAtlas dimensions that materially address each OWASP risk. Use to translate this vendor's compliance posture and data-handling stance into the application-security vocabulary your security team already uses.

LLM01	Prompt Injection User-supplied prompts manipulate model behaviour to bypass intended controls. SecurityTransparencyDependency chain
LLM02	Sensitive Information Disclosure Models leak PII, PHI, secrets, or proprietary data through outputs. Data handlingIP exposureJurisdiction
LLM03	Supply Chain Risk propagates from upstream models, datasets, plug-ins, and vendors. Dependency chainBusiness stabilitySecurity
LLM04	Data and Model Poisoning Adversarial training data or fine-tuning input degrades model integrity. Data handlingTransparencySecurity
LLM05	Improper Output Handling Downstream systems blindly trust model output, enabling injection downstream. IP exposureTransparency
LLM06	Excessive Agency Agents granted overbroad tool, identity, or permission scopes cause harm. Dependency chainTransparencyJurisdiction
LLM07	System Prompt Leakage System prompts containing secrets or logic are extracted via crafted input. Data handlingTransparency
LLM08	Vector and Embedding Weaknesses Vector stores and RAG pipelines leak or contaminate retrieved context. Data handlingSecurity
LLM09	Misinformation Hallucinated, biased, or fabricated outputs treated as authoritative. TransparencyRegulatory complianceBusiness stability
LLM10	Unbounded Consumption Cost, denial-of-service, and resource-exhaustion attacks against LLM endpoints. SecurityBusiness stability

Full framework reference: https://trustatlas.pages.dev/framework/owasp-llm-top-10

NIST AI RMF cross-walk

How each NIST AI RMF function is supported by the dimensions TrustAtlas scores.

GOVERN	Govern Establish AI governance structure: policies, roles, accountability. Regulatory complianceJurisdictionTransparencyBusiness stability
MAP	Map Establish AI context: intended purpose, use cases, capabilities, and risks. TransparencyDependency chainData handlingIP exposure
MEASURE	Measure Quantitative + qualitative risk assessment: testing, benchmarks, monitoring. SecurityData handlingTransparency
MANAGE	Manage Treat identified risks: mitigation, controls, incident response, lifecycle. Regulatory complianceSecurityDependency chainBusiness stability

Full framework reference: https://trustatlas.pages.dev/framework/nist-ai-rmf

Cited sources

Field	Source
ip_profile.training_data_provenance	https://techcrunch.com/2025/11/21/bret-taylors-sierra-reaches-100m-arr-in-under-two-years/ Verified 2025-11-21 by analyst
jurisdiction_profile.geopolitical_risk_notes	https://www.cnbc.com/2025/09/04/bret-taylor-sierra-ai-startup-salesforce-openai.html Verified 2025-09-04 by analyst
security_compliance.soc2_type2	https://sierra.ai/about Verified 2026-05-15 by analyst
vendors.description	https://sierra.ai/about Verified 2026-05-15 by analyst
vendors.employee_count_range	https://tracxn.com/d/companies/sierra/__7BlbMAkDWSyJoeaH8RQ9TEzmQo9diuckI1GUvWn9QHo Verified 2026-03-26 by analyst
vendors.funding_total_usd	https://sacra.com/c/sierra/ Verified 2026-04-01 by analyst
vendors.last_valuation_usd	https://techcrunch.com/2026/05/04/sierra-raises-950m-as-the-race-to-own-enterprise-ai-gets-serious/ Verified 2026-05-04 by analyst

Questions to ask before signing

Vendor-agnostic baseline. Send these to the vendor and require written answers before contract.

01. Provide your most recent SOC 2 Type II report (with bridge letter if applicable).
02. Describe your training-data provenance and customer opt-out mechanics in writing.
03. List all sub-processors and confirm notification policy for material additions.
04. Confirm BAA availability and signed-BAA process if we process PHI.
05. Describe rate-limiting, quota, and circuit-breaker controls protecting our usage.
06. Provide your model card or equivalent disclosure documenting intended use, limitations, and known failure modes.
07. Describe your prompt-injection defences and red-team posture against OWASP LLM Top 10 risks.
08. Confirm data residency options and which sub-regions our data may touch.
09. Provide incident-response SLAs, security-event notification timelines, and the most recent pen-test report summary.
10. Confirm output ownership terms and any indemnification or copyright-shield programs available.
11. Describe acquisition-risk safeguards and what happens to our data on a change of control.
12. List foundation-model dependencies and how upstream-model risk is mitigated.

Methodology + caveats

Composite scores use the default-balanced weight profile (25% data handling, 20% IP exposure, 15% jurisdiction, 15% security, 10% regulatory compliance, 8% transparency, 5% business stability, 2% dependency chain). All facts are sourced from the vendor's own public disclosures, public regulatory filings, or reputable secondary reporting — see the cited sources table above. This pack is decision-support material, not legal advice or audit evidence.