deepsense.ai vs RTS Labs: full comparison for 2026
Quick verdict
deepsense.ai (4.4/5) edges ahead of RTS Labs (4.2/5) overall. deepsense.ai is the better choice for engineering teams wanting a strong RAG partner. RTS Labs is the stronger option for U.S. mid-market firms with stalled AI pilots. The right choice depends on your project size, budget, and required tech stack.
deepsense.ai vs RTS Labs: head-to-head summary
| Criterion | deepsense.ai | RTS Labs |
|---|---|---|
| Founded | 2014 | 2010 |
| HQ | Warsaw, Poland | Glen Allen, VA, USA |
| Team size | 101–200 | 51–200 |
| Rating | 4.4 / 5 | 4.2 / 5 |
| Primary differentiator | Research-grade ML engineers who build retrieval systems and evaluate them with measurable accuracy targets | Onshore U.S. team focused on getting a stalled pilot into production with ERP and CRM connections |
| Pricing model | T&M and dedicated teams; rates on request | Fixed-scope assessments and builds, then T&M; rates on request |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | LangChain, Azure OpenAI, AWS Bedrock | Salesforce, Snowflake, Azure OpenAI |
| Industries served | Manufacturing, Retail, Financial services, Healthcare | Logistics, Financial services, Healthcare, Manufacturing |
deepsense.ai vs RTS Labs: overview
deepsense.ai
deepsense.ai is an AI-first engineering company founded in 2014 out of the AI division of CodiLime, with headquarters in Warsaw and an office in Palo Alto. It employs roughly 120–200 people, including several Kaggle competition winners. Its integration work centres on LLM applications using retrieval-augmented generation (RAG), plus computer vision and edge deployments for manufacturing. It lists technical partnerships with OpenAI, NVIDIA, Anyscale and LangChain.
RTS Labs
RTS Labs is a U.S. software and data consultancy founded in 2010, headquartered in Glen Allen, Virginia, near Richmond. It began with custom software, Salesforce implementation and business intelligence, and now positions itself as an implementation partner that takes AI and data systems from pilot to production. It says it has more than 100 senior engineers and AI architects and deploys in 8–12 weeks (per company website; independently unverifiable). Third-party estimates put headcount at 51–100.
Services and capabilities: deepsense.ai vs RTS Labs
| Capability | deepsense.ai | RTS Labs |
|---|---|---|
| CRM / ERP integration | ✗ | ✓ |
| LLM API gateway & cost control | ✓ | ✗ |
| Document processing | ✓ | ✗ |
| Agentic workflows | ✓ | ✓ |
| Fixed-price pilot | ✗ | ✗ |
| Managed services after launch | ✗ | ✗ |
| PII masking & access control | ✗ | ✗ |
Tech stack comparison: deepsense.ai vs RTS Labs
| Framework / platform | deepsense.ai | RTS Labs |
|---|---|---|
| Salesforce | N/A | ✓ |
| SAP | N/A | N/A |
| Microsoft Dynamics 365 | N/A | N/A |
| HubSpot | N/A | N/A |
| Snowflake | N/A | ✓ |
| Databricks | N/A | N/A |
| Azure OpenAI | ✓ | ✓ |
| AWS Bedrock | ✓ | ✓ |
| LangChain | ✓ | N/A |
| ServiceNow | N/A | N/A |
Pricing comparison: deepsense.ai vs RTS Labs
| Criterion | deepsense.ai | RTS Labs |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Time & materials, Dedicated team | Fixed project, Time & materials |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: deepsense.ai vs RTS Labs
| Dimension | deepsense.ai | RTS Labs |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Manufacturing, Retail, Financial services | Logistics, Financial services, Healthcare |
| Best use cases | Retrieval assistants over technical manuals or internal knowledge bases., Visual defect detection on production lines with edge inference. | Connecting an AI agent to ERP order data for a logistics company., Rescuing a stalled proof of concept and putting it into production. |
| Typical project type | Time & materials | Fixed project |
deepsense.ai vs RTS Labs: pros and cons
| deepsense.ai | |
|---|---|
| + | Deep ML talent, with evaluation of retrieval quality treated as part of the build |
| + | Experience deploying models on edge hardware as well as in the cloud |
| + | Open publication record and active LangChain contribution history |
| + | Comfortable working alongside an in-house data team |
| - | Less experience embedding AI inside packaged CRM or ERP products |
| - | Engagements lean toward engineering capacity, with less change-management support |
| RTS Labs | |
|---|---|
| + | Onshore U.S. delivery suits buyers who need data to stay with domestic staff |
| + | Salesforce implementation history helps when the CRM is part of the build |
| + | Explicit focus on production readiness, monitoring and fine-tuning after launch |
| + | Mid-market size keeps engagement minimums modest |
| - | Deployment-time and client-count claims are self-reported |
| - | Glassdoor employee reviews average about 3.0, which may point to retention issues |
| - | Smaller bench than national consultancies |
Who should choose deepsense.ai?
A typical fit: retrieval assistants over technical manuals or internal knowledge bases.
Research-grade ML engineers who build retrieval systems and evaluate them with measurable accuracy targets. Minimum engagement is not publicly disclosed. Works best with clients in Manufacturing, Retail, Financial services, Healthcare.
Who should choose RTS Labs?
A typical fit: connecting an AI agent to ERP order data for a logistics company.
Onshore U.S. team focused on getting a stalled pilot into production with ERP and CRM connections. Minimum engagement is not publicly disclosed. Works best with clients in Logistics, Financial services, Healthcare, Manufacturing.
Decision matrix: deepsense.ai vs RTS Labs
| Your situation | Recommended choice |
|---|---|
| You want a fixed-price audit or pilot before committing | Neither advertises one; ask for a scoped pilot |
| AI has to work inside your existing CRM or ERP | RTS Labs |
| Personal data must be masked and answers limited by user permissions | Ask both for their PII and access-control design |
| Your budget is at the lower end | Compare: deepsense.ai (Not disclosed) vs RTS Labs (Not disclosed) |
| You want the vendor to run the AI service after launch | Neither offers managed services; plan in-house operations |
| You are building multi-step agents across systems | Both |
Use case fit: deepsense.ai vs RTS Labs
| Use case | deepsense.ai fit | RTS Labs fit | Winner |
|---|---|---|---|
| Retrieval assistants over technical manuals or internal knowledge bases. | Strong | Limited | deepsense.ai |
| Visual defect detection on production lines with edge inference. | Strong | Limited | deepsense.ai |
| Connecting an AI agent to ERP order data for a logistics company. | Limited | Strong | RTS Labs |
| Rescuing a stalled proof of concept and putting it into production. | Limited | Strong | RTS Labs |
Verdict: deepsense.ai vs RTS Labs
deepsense.ai (4.4/5) is the stronger overall choice for most AI Integration projects. Research-grade ML engineers who build retrieval systems and evaluate them with measurable accuracy targets.
RTS Labs (4.2/5) is worth a look if you need rescuing a stalled proof of concept and putting it into production. If your situation matches that, RTS Labs is a competitive option.
Related comparisons
deepsense.ai vs RTS Labs FAQ
Is deepsense.ai better than RTS Labs?
deepsense.ai (4.4/5) scores higher overall, but "better" depends on your use case. deepsense.ai's strongest advantage: deep ML talent, with evaluation of retrieval quality treated as part of the build. RTS Labs's strongest advantage: onshore U.S. delivery suits buyers who need data to stay with domestic staff.
How do deepsense.ai and RTS Labs differ in pricing?
deepsense.ai pricing: T&M and dedicated teams; rates on request. RTS Labs pricing: Fixed-scope assessments and builds, then T&M; rates on request. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: deepsense.ai or RTS Labs?
deepsense.ai is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each company before shortlisting.
What are the main differences between deepsense.ai and RTS Labs?
deepsense.ai's primary differentiator is: research-grade ML engineers who build retrieval systems and evaluate them with measurable accuracy targets. RTS Labs's primary differentiator is: onshore U.S. team focused on getting a stalled pilot into production with ERP and CRM connections. They also differ in team size (101–200 vs 51–200), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Manufacturing, Retail vs Logistics, Financial services).
Verify all details directly with each company before making a decision.