deepsense.ai vs InData Labs: full comparison for 2026
Quick verdict
deepsense.ai (4.4/5) edges ahead of InData Labs (4.1/5) overall. deepsense.ai is the better choice for engineering teams wanting a strong RAG partner. InData Labs is the stronger option for mid-size firms needing forecasting and data science. The right choice depends on your project size, budget, and required tech stack.
deepsense.ai vs InData Labs: head-to-head summary
| Criterion | deepsense.ai | InData Labs |
|---|---|---|
| Founded | 2014 | 2014 |
| HQ | Warsaw, Poland | Nicosia, Cyprus |
| Team size | 101–200 | 50–249 |
| Rating | 4.4 / 5 | 4.1 / 5 |
| Primary differentiator | Research-grade ML engineers who build retrieval systems and evaluate them with measurable accuracy targets | Data-science-led team that builds predictive models alongside generative features |
| Pricing model | T&M and dedicated teams; rates on request | Fixed-price and T&M; rates on request |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | LangChain, Azure OpenAI, AWS Bedrock | Python, Azure OpenAI, AWS Bedrock |
| Industries served | Manufacturing, Retail, Financial services, Healthcare | Retail & e-commerce, Healthcare, Financial services, Logistics |
deepsense.ai vs InData 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.
InData Labs
InData Labs is a data science and AI company founded in 2014 and headquartered in Nicosia, Cyprus, with offices in Vilnius and Miami. Clutch lists 50–249 employees, and the firm says it has delivered 150+ projects since 2014 (per company website; independently unverifiable). Clutch shows AI development as more than half of its work, followed by BI and big data consulting. Reviewers praise its data science skill and mention slower proposal and planning cycles.
Services and capabilities: deepsense.ai vs InData Labs
| Capability | deepsense.ai | InData 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 InData Labs
| Framework / platform | deepsense.ai | InData Labs |
|---|---|---|
| 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 | ✓ | ✓ |
| LangChain | ✓ | N/A |
| ServiceNow | N/A | N/A |
Pricing comparison: deepsense.ai vs InData Labs
| Criterion | deepsense.ai | InData 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 InData Labs
| Dimension | deepsense.ai | InData Labs |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Manufacturing, Retail, Financial services | Retail & e-commerce, Healthcare, Financial services |
| Best use cases | Retrieval assistants over technical manuals or internal knowledge bases., Visual defect detection on production lines with edge inference. | Churn or demand models that feed a BI dashboard., Document extraction for invoices and receipts. |
| Typical project type | Time & materials | Fixed project |
deepsense.ai vs InData 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 |
| InData Labs | |
|---|---|
| + | Long track record in classical data science as well as LLM work |
| + | EU-registered company with Lithuanian delivery |
| + | Strong Clutch reviews on technical quality |
| - | Reviewers note slower proposals and planning |
| - | Limited published integration work inside large CRM or ERP suites |
| - | Headcount estimates vary widely between directories |
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 InData Labs?
A typical fit: churn or demand models that feed a BI dashboard.
Data-science-led team that builds predictive models alongside generative features. Minimum engagement is not publicly disclosed. Works best with clients in Retail & e-commerce, Healthcare, Financial services, Logistics.
Decision matrix: deepsense.ai vs InData 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 | 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 InData 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 | deepsense.ai |
Use case fit: deepsense.ai vs InData Labs
| Use case | deepsense.ai fit | InData 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 |
| Churn or demand models that feed a BI dashboard. | Limited | Strong | InData Labs |
| Document extraction for invoices and receipts. | Limited | Strong | InData Labs |
Verdict: deepsense.ai vs InData 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.
InData Labs (4.1/5) is worth a look if you need document extraction for invoices and receipts. If your situation matches that, InData Labs is a competitive option.
Related comparisons
deepsense.ai vs InData Labs FAQ
Is deepsense.ai better than InData 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. InData Labs's strongest advantage: long track record in classical data science as well as LLM work.
How do deepsense.ai and InData Labs differ in pricing?
deepsense.ai pricing: T&M and dedicated teams; rates on request. InData Labs pricing: Fixed-price and 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 InData 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 InData Labs?
deepsense.ai's primary differentiator is: research-grade ML engineers who build retrieval systems and evaluate them with measurable accuracy targets. InData Labs's primary differentiator is: data-science-led team that builds predictive models alongside generative features. They also differ in team size (101–200 vs 50–249), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Manufacturing, Retail vs Retail & e-commerce, Healthcare).
Verify all details directly with each company before making a decision.