deepsense.ai vs EPAM Systems: full comparison for 2026
Quick verdict
deepsense.ai (4.5/5) edges ahead of EPAM Systems (4.1/5) overall. deepsense.ai is the better choice for teams needing RAG and evaluation done properly. EPAM Systems is the stronger option for enterprises with multi-year engineering budgets. 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 | 61,000+ |
| Rating | 4.5 / 5 | 4.1 / 5 |
| Primary differentiator | Evaluation frameworks that test model output before it reaches users | Engineering capacity across Europe, India, and the Americas for long AI programs |
| Pricing model | Time & materials and fixed-scope projects; $100–$149/hr (Clutch band) | Time & materials and dedicated teams; rates on request |
| Min. engagement | $25,000+ (Clutch) | Not disclosed |
| Primary tech stack | LangChain, Azure OpenAI, AWS Bedrock | Azure OpenAI, AWS Bedrock, Databricks |
| Industries served | Retail & e-commerce, Manufacturing, Financial services, Telecom | Financial services, Healthcare, Retail & e-commerce, Media, Energy |
deepsense.ai vs EPAM Systems: overview
deepsense.ai
deepsense.ai is a Warsaw AI engineering company founded in 2014 with 100–200 staff. Its recent Clutch-listed work centers on agentic systems that automate internal workflows, retrieval-augmented generation (RAG) knowledge platforms, voice AI on telephony, and evaluation frameworks for testing models before release. Its research background predates the current LLM wave by several years. Clutch shows a $100–$149 hourly band and a $25,000 minimum, which places it at the upper end of European rates.
EPAM Systems
EPAM Systems was founded in 1993 and is headquartered in Newtown, Pennsylvania, with over 61,200 employees at the end of 2025. Acquisitions shaped the recent mix: NEORIS (2024) for Latin America and Iberia, and First Derivative (completed December 2024) for financial-services data. Management targets more than $600 million of AI-native revenue in 2026, after reporting over $105 million in Q4 2025. Its AI/Run tooling and Agentic QA product support large engineering programs more than single-workflow pilots.
Services and capabilities: deepsense.ai vs EPAM Systems
| Capability | deepsense.ai | EPAM Systems |
|---|---|---|
| CRM / ERP integration | ✗ | ✓ |
| LLM API gateway & cost control | ✓ | ✓ |
| Document processing | ✓ | ✗ |
| Conversational AI | ✓ | ✗ |
| Agentic workflows | ✓ | ✓ |
| Fixed-price pilot | ✗ | ✗ |
| Managed services after launch | ✗ | ✓ |
Tech stack comparison: deepsense.ai vs EPAM Systems
| Framework / platform | deepsense.ai | EPAM Systems |
|---|---|---|
| Salesforce | N/A | N/A |
| SAP | N/A | ✓ |
| Microsoft Dynamics 365 | N/A | N/A |
| HubSpot | N/A | N/A |
| Snowflake | N/A | ✓ |
| Databricks | ✓ | ✓ |
| BigQuery | N/A | N/A |
| Azure OpenAI | ✓ | ✓ |
| AWS Bedrock | ✓ | ✓ |
| Zendesk | N/A | N/A |
Pricing comparison: deepsense.ai vs EPAM Systems
| Criterion | deepsense.ai | EPAM Systems |
|---|---|---|
| Minimum engagement | $25,000+ (Clutch) | Not disclosed |
| Engagement models | Fixed-scope project, Time & materials, Dedicated team | Time & materials, Dedicated team, Managed services |
| Rate transparency | Minimum disclosed | 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 | Enterprise |
| Best industries | Retail & e-commerce, Manufacturing, Financial services | Financial services, Healthcare, Retail & e-commerce |
| Best use cases | Building a RAG assistant over product manuals with measured answer accuracy, Voice agents that answer inbound calls and write back to a ticketing tool | Large data-platform programs that end in AI features, Agent development across several business units |
| Typical project type | Fixed-scope project | Time & materials |
deepsense.ai vs EPAM Systems: pros and cons
| deepsense.ai | |
|---|---|
| + | Builds evaluation suites that measure accuracy before a feature ships. |
| + | Voice AI over phone lines is an uncommon skill among integration vendors. |
| + | A ten-year ML track record means classical models and LLMs can be mixed when one alone won't do. |
| + | Clients still give it 4.8–4.9 on Clutch's cost score despite the higher rate band. |
| - | The $100–$149 Clutch band is high for Central European delivery |
| - | Enterprise CRM and ERP connectors are not where its case studies concentrate |
| - | Post-launch managed service isn't a packaged offer |
| EPAM Systems | |
|---|---|
| + | Engineering depth to staff many workstreams at once. |
| + | Public reporting on AI-native revenue gives a measurable view of the practice. |
| + | First Derivative added capital-markets data skills. |
| + | Agentic QA product addresses testing of AI-generated code. |
| - | Acquisition-driven growth (NEORIS, First Derivative) means teams are still being integrated |
| - | Not built for a small fixed-price pilot |
| - | Management flagged slower organic growth in 2026 guidance |
Who should choose deepsense.ai?
A typical fit: building a RAG assistant over product manuals with measured answer accuracy.
Evaluation frameworks that test model output before it reaches users. Minimum engagement starts at $25,000+ (Clutch). Works best with clients in Retail & e-commerce, Manufacturing, Financial services, Telecom.
Who should choose EPAM Systems?
A typical fit: large data-platform programs that end in AI features.
Engineering capacity across Europe, India, and the Americas for long AI programs. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare, Retail & e-commerce, Media, Energy.
Decision matrix: deepsense.ai vs EPAM Systems
| 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 | EPAM Systems |
| Your budget is at the lower end | Compare: deepsense.ai ($25,000+ (Clutch)) vs EPAM Systems (Not disclosed) |
| The AI has to read and write in your CRM or ERP | EPAM Systems |
| You need multi-step agents acting across systems | Both build agentic workflows |
| You need a large team for a multi-year program | EPAM Systems |
Use case fit: deepsense.ai vs EPAM Systems
| Use case | deepsense.ai fit | EPAM Systems fit | Winner |
|---|---|---|---|
| Building a RAG assistant over product manuals with measured answer accuracy | Strong | Limited | deepsense.ai |
| Voice agents that answer inbound calls and write back to a ticketing tool | Strong | Limited | deepsense.ai |
| Large data-platform programs that end in AI features | Limited | Strong | EPAM Systems |
| Agent development across several business units | Limited | Strong | EPAM Systems |
Verdict: deepsense.ai vs EPAM Systems
deepsense.ai (4.5/5) is the stronger overall choice for most AI Integration Services projects. Evaluation frameworks that test model output before it reaches users.
EPAM Systems (4.1/5) is worth a look if you need agent development across several business units. 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 (4.5/5) scores higher overall, but "better" depends on your use case. deepsense.ai's strongest advantage: builds evaluation suites that measure accuracy before a feature ships. EPAM Systems's strongest advantage: engineering depth to staff many workstreams at once.
How do deepsense.ai and EPAM Systems differ in pricing?
deepsense.ai's pricing: time & materials and fixed-scope projects; $100–$149/hr (Clutch band) with a minimum engagement of $25,000+ (Clutch). EPAM Systems's pricing: 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: deepsense.ai or EPAM Systems?
EPAM Systems 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 deepsense.ai and EPAM Systems?
deepsense.ai's primary differentiator is: evaluation frameworks that test model output before it reaches users. EPAM Systems's primary differentiator is: engineering capacity across Europe, India, and the Americas for long AI programs. They also differ in team size (101–200 vs 61,000+), minimum engagement ($25,000+ (Clutch) vs Not disclosed), and primary industries served (Retail & e-commerce, Manufacturing vs Financial services, Healthcare).
Verify all details directly with each provider before making a decision.