deepsense.ai vs Miquido: full comparison for 2026
Quick verdict
deepsense.ai (4.4/5) edges ahead of Miquido (3.9/5) overall. deepsense.ai is the better choice for engineering teams wanting a strong RAG partner. Miquido is the stronger option for product teams adding AI to customer-facing apps. The right choice depends on your project size, budget, and required tech stack.
deepsense.ai vs Miquido: head-to-head summary
| Criterion | deepsense.ai | Miquido |
|---|---|---|
| Founded | 2014 | 2011 |
| HQ | Warsaw, Poland | Kraków, Poland |
| Team size | 101–200 | 150–250 |
| Rating | 4.4 / 5 | 3.9 / 5 |
| Primary differentiator | Research-grade ML engineers who build retrieval systems and evaluate them with measurable accuracy targets | Product design and mobile engineering applied to AI features in apps |
| Pricing model | T&M and dedicated teams; rates on request | $50–$99/hr (Clutch band); fixed-price and T&M |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | LangChain, Azure OpenAI, AWS Bedrock | Azure OpenAI, Google Vertex AI, Flutter |
| Industries served | Manufacturing, Retail, Financial services, Healthcare | Financial services, Healthcare, Retail & e-commerce, Entertainment |
deepsense.ai vs Miquido: 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.
Miquido
Miquido is a software product studio founded in 2011 in Kraków, Poland. Directory headcount estimates range from about 175 to more than 250 people. Clutch lists a $50–$99 hourly band and an overall rating of 4.9, with reviewers noting occasional difficulty scaling teams quickly. It now describes itself as an AI-native development firm, but most of its portfolio is consumer-facing apps rather than back-office system integration.
Services and capabilities: deepsense.ai vs Miquido
| Capability | deepsense.ai | Miquido |
|---|---|---|
| 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 Miquido
| Framework / platform | deepsense.ai | Miquido |
|---|---|---|
| Salesforce | N/A | N/A |
| SAP | N/A | 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 | ✓ | N/A |
| LangChain | ✓ | ✓ |
| ServiceNow | N/A | N/A |
Pricing comparison: deepsense.ai vs Miquido
| Criterion | deepsense.ai | Miquido |
|---|---|---|
| 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 Miquido
| Dimension | deepsense.ai | Miquido |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Manufacturing, Retail, Financial services | Financial services, Healthcare, Retail & e-commerce |
| Best use cases | Retrieval assistants over technical manuals or internal knowledge bases., Visual defect detection on production lines with edge inference. | Adding an AI assistant to a banking or fintech app., Personalized recommendations inside a mobile app. |
| Typical project type | Time & materials | Fixed project |
deepsense.ai vs Miquido: 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 |
| Miquido | |
|---|---|
| + | Strong product design and mobile craft |
| + | Published Clutch rate band |
| + | Good fit for consumer-facing AI features |
| - | Little back-office integration work with CRM or ERP |
| - | Reviewers mention resource constraints when scaling up |
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 Miquido?
A typical fit: adding an AI assistant to a banking or fintech app.
Product design and mobile engineering applied to AI features in apps. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare, Retail & e-commerce, Entertainment.
Decision matrix: deepsense.ai vs Miquido
| 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 | Neither lists CRM/ERP integration work |
| 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 Miquido (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 | deepsense.ai |
Use case fit: deepsense.ai vs Miquido
| Use case | deepsense.ai fit | Miquido 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 |
| Adding an AI assistant to a banking or fintech app. | Limited | Strong | Miquido |
| Personalized recommendations inside a mobile app. | Limited | Strong | Miquido |
Verdict: deepsense.ai vs Miquido
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.
Miquido (3.9/5) is worth a look if you need personalized recommendations inside a mobile app. If your situation matches that, Miquido is a competitive option.
Related comparisons
deepsense.ai vs Miquido FAQ
Is deepsense.ai better than Miquido?
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. Miquido's strongest advantage: strong product design and mobile craft.
How do deepsense.ai and Miquido differ in pricing?
deepsense.ai pricing: T&M and dedicated teams; rates on request. Miquido pricing: $50–$99/hr (Clutch band); fixed-price and T&M. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: deepsense.ai or Miquido?
Miquido 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 Miquido?
deepsense.ai's primary differentiator is: research-grade ML engineers who build retrieval systems and evaluate them with measurable accuracy targets. Miquido's primary differentiator is: product design and mobile engineering applied to AI features in apps. They also differ in team size (101–200 vs 150–250), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Manufacturing, Retail vs Financial services, Healthcare).
Verify all details directly with each company before making a decision.