deepsense.ai vs EPAM Systems: full comparison for 2026
Quick verdict
deepsense.ai (4.4/5) edges ahead of EPAM Systems (4.3/5) overall. deepsense.ai is the better choice for engineering teams wanting a strong RAG partner. EPAM Systems is the stronger option for large enterprises with Salesforce and SAP estates. The right choice depends on your project size, budget, and required tech stack.
deepsense.ai vs EPAM Systems: head-to-head summary
| Criterion | deepsense.ai | EPAM Systems |
|---|---|---|
| Founded | 2014 | 1993 |
| HQ | Warsaw, Poland | Newtown, PA, USA |
| Team size | 101–200 | 60,000+ |
| Rating | 4.4 / 5 | 4.3 / 5 |
| Primary differentiator | Research-grade ML engineers who build retrieval systems and evaluate them with measurable accuracy targets | Engineering depth across Salesforce, SAP and major model providers, including a formal partnership with Anthropic |
| Pricing model | T&M and dedicated teams; rates on request | T&M, dedicated teams and fixed-scope programmes; rates on request |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | LangChain, Azure OpenAI, AWS Bedrock | Salesforce, SAP, Azure OpenAI |
| Industries served | Manufacturing, Retail, Financial services, Healthcare | Financial services, Healthcare & life sciences, Retail, Travel, Manufacturing |
deepsense.ai vs EPAM Systems: 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.
EPAM Systems
EPAM Systems is a NYSE-listed software engineering services company founded in 1993 in Princeton, New Jersey, and now headquartered in Newtown, Pennsylvania, with roughly 62,000 employees in more than 55 countries. It runs large Salesforce and SAP Commerce practices and bought Netherlands-based Just-BI to deepen its SAP consulting. In May 2026 it announced a multi-year partnership with Anthropic and a plan to certify more than 10,000 architects on Claude. Its AI/RUN platform is used to package enterprise deployments.
Services and capabilities: deepsense.ai vs EPAM Systems
| Capability | deepsense.ai | EPAM Systems |
|---|---|---|
| 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 EPAM Systems
| Framework / platform | deepsense.ai | EPAM Systems |
|---|---|---|
| Salesforce | N/A | ✓ |
| SAP | N/A | ✓ |
| Microsoft Dynamics 365 | N/A | N/A |
| HubSpot | N/A | N/A |
| Snowflake | N/A | N/A |
| Databricks | N/A | N/A |
| Azure OpenAI | ✓ | ✓ |
| AWS Bedrock | ✓ | ✓ |
| LangChain | ✓ | N/A |
| ServiceNow | N/A | N/A |
Pricing comparison: deepsense.ai vs EPAM Systems
| Criterion | deepsense.ai | EPAM Systems |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Time & materials, Dedicated team | Fixed project, Time & materials, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: deepsense.ai vs EPAM Systems
| Dimension | deepsense.ai | EPAM Systems |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Manufacturing, Retail, Financial services | Financial services, Healthcare & life sciences, Retail |
| Best use cases | Retrieval assistants over technical manuals or internal knowledge bases., Visual defect detection on production lines with edge inference. | Agentforce and service-agent features across a large Salesforce estate., AI features in SAP Commerce storefronts and order flows. |
| Typical project type | Time & materials | Fixed project |
deepsense.ai vs EPAM Systems: 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 |
| EPAM Systems | |
|---|---|
| + | Thousands of certified Salesforce staff and a dedicated SAP Commerce bench |
| + | Formal model-provider partnership gives early access and support for Claude-based builds |
| + | Engineering rigor suited to complex, multi-system integrations |
| + | Large nearshore delivery in Central Europe and Latin America |
| - | Scale and account structure favour large, multi-year engagements |
| - | Delivery staff numbers for specific platforms come partly from older announcements |
| - | Advice tends to lead toward EPAM-delivered builds |
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 EPAM Systems?
A typical fit: agentforce and service-agent features across a large Salesforce estate.
Engineering depth across Salesforce, SAP and major model providers, including a formal partnership with Anthropic. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare & life sciences, Retail, Travel, Manufacturing.
Decision matrix: deepsense.ai vs EPAM Systems
| 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 | EPAM Systems |
| 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 EPAM Systems (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 EPAM Systems
| Use case | deepsense.ai fit | EPAM Systems 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 |
| Agentforce and service-agent features across a large Salesforce estate. | Limited | Strong | EPAM Systems |
| AI features in SAP Commerce storefronts and order flows. | Limited | Strong | EPAM Systems |
Verdict: deepsense.ai vs EPAM Systems
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.
EPAM Systems (4.3/5) is worth a look if you need AI features in SAP Commerce storefronts and order flows. If your situation matches that, EPAM Systems is a competitive option.
Related comparisons
deepsense.ai vs EPAM Systems FAQ
Is deepsense.ai better than EPAM Systems?
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. EPAM Systems's strongest advantage: thousands of certified Salesforce staff and a dedicated SAP Commerce bench.
How do deepsense.ai and EPAM Systems differ in pricing?
deepsense.ai pricing: T&M and dedicated teams; rates on request. EPAM Systems pricing: T&M, dedicated teams and fixed-scope programmes; 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 EPAM Systems?
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 EPAM Systems?
deepsense.ai's primary differentiator is: research-grade ML engineers who build retrieval systems and evaluate them with measurable accuracy targets. EPAM Systems's primary differentiator is: engineering depth across Salesforce, SAP and major model providers, including a formal partnership with Anthropic. They also differ in team size (101–200 vs 60,000+), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Manufacturing, Retail vs Financial services, Healthcare & life sciences).
Verify all details directly with each company before making a decision.