RTS Labs vs Quantiphi: full comparison for 2026
Quick verdict
RTS Labs (4.4/5) edges ahead of Quantiphi (4.3/5) overall. RTS Labs is the better choice for U.S. firms needing onshore-only delivery. Quantiphi is the stronger option for large document-heavy programs on Google Cloud or AWS. The right choice depends on your project size, budget, and required tech stack.
RTS Labs vs Quantiphi: head-to-head summary
| Criterion | RTS Labs | Quantiphi |
|---|---|---|
| Founded | 2010 | 2013 |
| HQ | Richmond, VA, USA | Marlborough, MA, USA |
| Team size | 100+ | 3,500+ |
| Rating | 4.4 / 5 | 4.3 / 5 |
| Primary differentiator | All-U.S. engineering team and early MCP integration work | Partner-of-the-year history with both Google Cloud and AWS on AI work |
| Pricing model | Fixed-scope phases and time & materials; rates on request | Fixed-scope projects, time & materials, and dedicated teams; rates on request |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Salesforce, Microsoft Dynamics 365, Snowflake | Vertex AI, AWS Bedrock, Snowflake |
| Industries served | Logistics, Insurance, Legal, Financial services, Real estate, Healthcare | Healthcare, Insurance, Financial services, Public sector, Media |
RTS Labs vs Quantiphi: overview
RTS Labs
RTS Labs has been building software since 2010 from Richmond, Virginia, and now positions itself as an applied-AI consultancy. Its 100-plus staff are all U.S.-based, which matters for clients that can't send data or system access offshore. The firm says it has shipped over 600 software and AI systems and that core implementations usually reach production in 8–12 weeks (per company website; independently unverifiable). It's also one of the few mid-size firms publicly building Model Context Protocol (MCP) server integrations for enterprise tools.
Quantiphi
Quantiphi was founded in 2013, is headquartered in Marlborough, Massachusetts, and employs over 3,500 people, most of them in India. It reports 21 Google Cloud Partner of the Year awards over ten years and three AWS AI/ML Partner of the Year awards (per company materials; independently unverifiable). Document AI, contact-center AI, and healthcare and insurance workflows make up much of its integration work. Its size lets it staff large programs while still working only on AI and data.
Services and capabilities: RTS Labs vs Quantiphi
| Capability | RTS Labs | Quantiphi |
|---|---|---|
| CRM / ERP integration | ✓ | ✗ |
| LLM API gateway & cost control | ✓ | ✗ |
| Document processing | ✗ | ✓ |
| Conversational AI | ✗ | ✓ |
| Agentic workflows | ✓ | ✗ |
| Fixed-price pilot | ✗ | ✗ |
| Managed services after launch | ✓ | ✗ |
Tech stack comparison: RTS Labs vs Quantiphi
| Framework / platform | RTS Labs | Quantiphi |
|---|---|---|
| Salesforce | ✓ | N/A |
| SAP | N/A | N/A |
| Microsoft Dynamics 365 | ✓ | N/A |
| HubSpot | N/A | N/A |
| Snowflake | ✓ | ✓ |
| Databricks | N/A | N/A |
| BigQuery | N/A | ✓ |
| Azure OpenAI | ✓ | N/A |
| AWS Bedrock | ✓ | ✓ |
| Zendesk | N/A | N/A |
Pricing comparison: RTS Labs vs Quantiphi
| Criterion | RTS Labs | Quantiphi |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Fixed-scope project, Time & materials, Managed services | Fixed-scope project, Time & materials, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: RTS Labs vs Quantiphi
| Dimension | RTS Labs | Quantiphi |
|---|---|---|
| Best company size | Startup to mid-market | Mid-market to enterprise |
| Best industries | Logistics, Insurance, Legal | Healthcare, Insurance, Financial services |
| Best use cases | Connecting an agent to a TMS (transportation management system) and ERP for freight exceptions, Building MCP servers so assistants can query internal tools | Claims and medical-record extraction for insurers, Contact-center assistants on Google Cloud |
| Typical project type | Fixed-scope project | Fixed-scope project |
RTS Labs vs Quantiphi: pros and cons
| RTS Labs | |
|---|---|
| + | Every engineer is U.S.-based, which simplifies data-residency and security reviews. |
| + | MCP server work lets agents call internal tools through one standard interface. |
| + | Logistics and insurance case work ties AI into operational systems. |
| + | Offers ongoing support once a system is live. |
| - | Onshore-only staffing means higher rates than nearshore firms |
| - | Its published timelines vary between 90 days and 8–12 weeks depending on the page |
| - | No hyperscaler partner tier is prominent in its materials |
| Quantiphi | |
|---|---|
| + | Partner depth on two hyperscalers instead of one. |
| + | Document AI and contact-center AI are mature practice areas. |
| + | Enough staff to run several workstreams in parallel. |
| + | A multi-year Google Cloud partnership announced in 2026 covers joint industry solutions. |
| - | Most delivery is offshore, so time-zone overlap with U.S. or EU teams is partial |
| - | Fixed-price pilots aren't advertised as a standard entry point |
| - | Award counts come from the company itself |
Who should choose RTS Labs?
A typical fit: connecting an agent to a TMS (transportation management system) and ERP for freight exceptions.
All-U.S. engineering team and early MCP integration work. Minimum engagement is not publicly disclosed. Works best with clients in Logistics, Insurance, Legal, Financial services, Real estate, Healthcare.
Who should choose Quantiphi?
A typical fit: claims and medical-record extraction for insurers.
Partner-of-the-year history with both Google Cloud and AWS on AI work. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare, Insurance, Financial services, Public sector, Media.
Decision matrix: RTS Labs vs Quantiphi
| Your situation | Recommended choice |
|---|---|
| You want a priced pilot before committing to a rollout | Neither advertises one; ask for a scoped pilot quote |
| You need someone to run and monitor the system after launch | RTS Labs |
| Your budget is at the lower end | Compare: RTS Labs (Not disclosed) vs Quantiphi (Not disclosed) |
| The AI has to read and write in your CRM or ERP | RTS Labs |
| You need multi-step agents acting across systems | RTS Labs |
| You need a large team for a multi-year program | Quantiphi |
Use case fit: RTS Labs vs Quantiphi
| Use case | RTS Labs fit | Quantiphi fit | Winner |
|---|---|---|---|
| Connecting an agent to a TMS (transportation management system) and ERP for freight exceptions | Strong | Limited | RTS Labs |
| Building MCP servers so assistants can query internal tools | Strong | Limited | RTS Labs |
| Claims and medical-record extraction for insurers | Limited | Strong | Quantiphi |
| Contact-center assistants on Google Cloud | Limited | Strong | Quantiphi |
Verdict: RTS Labs vs Quantiphi
RTS Labs (4.4/5) is the stronger overall choice for most AI Integration Services projects. All-U.S. engineering team and early MCP integration work.
Quantiphi (4.3/5) is worth a look if you need contact-center assistants on Google Cloud. If your situation matches that, Quantiphi is a competitive option.
Related comparisons
RTS Labs vs Quantiphi FAQ
Is RTS Labs better than Quantiphi?
RTS Labs (4.4/5) scores higher overall, but "better" depends on your use case. RTS Labs's strongest advantage: every engineer is U.S.-based, which simplifies data-residency and security reviews. Quantiphi's strongest advantage: partner depth on two hyperscalers instead of one.
How do RTS Labs and Quantiphi differ in pricing?
RTS Labs's pricing: fixed-scope phases and time & materials; rates on request. Quantiphi's pricing: fixed-scope projects, time & materials, and dedicated teams; rates on request. Any hourly bands shown come from Clutch, not a published rate card, so a scoping call is still needed for a project quote.
Which is better for enterprise: RTS Labs or Quantiphi?
Quantiphi is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each provider before shortlisting.
What are the main differences between RTS Labs and Quantiphi?
RTS Labs's primary differentiator is: All-U.S. engineering team and early MCP integration work. Quantiphi's primary differentiator is: partner-of-the-year history with both Google Cloud and AWS on AI work. They also differ in team size (100+ vs 3,500+), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Logistics, Insurance vs Healthcare, Insurance).
Verify all details directly with each provider before making a decision.