InData Labs
Predictive analytics and computer vision consultancy founded in 2014.
Founded 2014 | Nicosia, Cyprus (delivery center: Minsk, Belarus) | 51–200 employees
computer-visionnlp-llmcustom-model-trainingdata-engineering-ml
What is InData Labs?
InData Labs is a data science consultancy founded in 2014 by Marat Karpeko, with a registered headquarters in Nicosia, Cyprus, and its primary research and development center in Minsk, Belarus. The company focuses on predictive analytics, natural language processing, and computer vision, delivering custom AI model development for clients ranging from logistics to retail. Published case studies include a freight-rate prediction model for a transportation company and a dog-face-identification model reporting 91.96 percent accuracy, giving it more quantified, checkable outcome data than many peers of similar size.
InData Labs was founded in 2014 and is headquartered in Nicosia, Cyprus (delivery center: Minsk, Belarus). The firm employs 51–200 people and works primarily with clients in Transportation/logistics, Retail, Finance sectors. Its primary differentiator is: Publishes concrete, quantified accuracy figures in its case studies rather than only qualitative outcome claims.
InData Labs tech stack and services
PythonComputer vision frameworksNLP toolkitsPredictive analytics tooling
| Service area |
|---|
| Computer Vision |
| NLP & LLM Development |
| Custom Model Training |
| Data Engineering for ML |
InData Labs use cases
Short answer: InData Labs is best suited for companies needing predictive-analytics or CV, documented accuracy.
| Use case |
|---|
| Building a predictive pricing or demand-forecasting model for logistics or transportation |
| Developing a computer-vision classification model with a documented accuracy target |
| Running an NLP project for a mid-market company without an in-house data science team |
| Prototyping a quantified proof-of-concept model before a larger production commitment |
InData Labs pricing
Short answer: InData Labs uses a project-based pricing approach. Minimum engagement starts at $25,000.
| Engagement model | Typical range | Best for |
|---|---|---|
| Fixed project | From $25,000 | Well-defined scope |
| Time & Material | Variable; depends on team size | Large programmes or team augmentation |
InData Labs does not publish a public rate card. Contact them directly via their website to get project-specific pricing.
InData Labs pros and cons
| Advantages | Things to consider |
|---|---|
| +Case studies include specific, quantified model accuracy figures rather than vague outcome claims. | -Team size figures are inconsistent across sources (roughly 50–80 depending on source), so exact headcount is uncertain. |
| +Featured among Clutch's broader provider directory with a positive review sentiment on delivery timeliness. | -Registered HQ (Cyprus) differs from the primary delivery center (Belarus), which some buyers may want clarified given regional considerations. |
| +Focused specialization in predictive analytics and computer vision avoids service-line dilution. | -Public tech-stack disclosure is limited beyond high-level specialization areas. |
| +Recognized in a 2016 "Top 100 Big Data" listing, indicating an established track record. | -Fewer large, brand-name enterprise clients named publicly compared to bigger peers. |
InData Labs vs alternatives
How InData Labs compares to the other top ML Model Development companies.
| Company | Best for | Key difference | Rating | Compare |
|---|---|---|---|---|
| Tensorway | Mid-market fintech and SaaS, hybrid forecasting models. | Combines classical statistical forecasting with deep learning rather than defaulting to deep learning alone, and ships with experiment tracking and monitoring built in. | 4.8 | Full comparison |
| Neurons Lab | Financial services, engineering-led production AI. | End-to-end delivery model from use-case scoping to continuous production support, with declared depth in financial services. | 4.6 | Full comparison |
| DataRoot Labs | Startups needing a senior AI-only team, LLM/CV projects. | Has never diversified beyond AI/ML services, and backs its delivery bench with an in-house ML training program (DataRoot University). | 4.6 | Full comparison |
| Miquido | Companies wanting ML/CV bundled with mobile/web engineering. | Combines a large, review-verified product engineering practice with a dedicated AI/ML/CV specialization, useful for teams needing both app and model work from one vendor. | 4.6 | Full comparison |
| Provectus | Mid-market companies, cloud data plus ML in one. | Grew out of cloud and big-data engineering roots, giving it particular strength in the data infrastructure layer underneath ML models, not just the models themselves. | 4.5 | Full comparison |
| Neoteric | Organizations wanting a structured AI feasibility phase. | Two-decade operating history combined with a formal upfront feasibility-assessment stage before any model-building work begins. | 4.5 | Full comparison |
| Addepto | Cost-conscious teams, MLOps consulting or advisory-only. | Dedicated MLOps-consulting service line and Clutch-reported project pricing well below several peers in this list, making it the more budget-accessible option. | 4.4 | Full comparison |
| N-iX | Enterprise buyers, certified data-platform plus ML partner. | Broadest cloud certification footprint in this comparison (350+ across five major platforms), backed by a 200+ person dedicated data practice. | 4.4 | Full comparison |
| MobiDev | Small/mid-size companies, dedicated ML consulting arm. | Historical Clutch #1 ranking for machine learning development (2021) combined with a specifically SME-oriented service model. | 4.3 | Full comparison |
| Sciforce | Research-oriented boutique, NLP, DSP, computer vision. | R&D-first culture with named specializations in digital signal processing and NLP that are less commonly offered as distinct practice areas by peers. | 4.2 | Full comparison |
| Sigmoid | Enterprises with data-infrastructure bottlenecks ahead of ML. | Data-engineering-first approach with 950+ multi-cloud certified engineers, positioning it as an infrastructure specialist that also delivers ML rather than the reverse. | 4.2 | Full comparison |
| Tredence | Enterprises, vertical analytics for supply chain at scale. | Venture-backed growth trajectory ($205M raised) with named specialization in supply chain and customer analytics rather than generic horizontal AI consulting. | 4.2 | Full comparison |
| Quantiphi | AWS-standardized enterprises, deepest AWS AI/ML credentials. | Deepest AWS-specific partnership credentials among firms researched, including AWS GenAI Innovation Center preferred-partner status. | 4.2 | Full comparison |
| Sigma Software Group | Companies wanting a Snowflake-certified data platform plus ML. | Snowflake AI Data Cloud partnership combined with unusually broad industry diversification (AdTech through aviation to gaming). | 4.1 | Full comparison |
| Intellectsoft | Companies wanting enterprise clients, dedicated AI Lab. | Unusually strong roster of large, publicly named enterprise clients (EY, Qualcomm, London Stock Exchange) for a company of its relatively modest team size. | 4.1 | Full comparison |
| ELEKS | Enterprises, established European engineering plus data science. | One of the longest operating histories (since 1991) among firms researched for this list, predating the AI consulting boom by decades. | 4.1 | Full comparison |
| Fractal Analytics | Large enterprises, scaled AI partner with a research... | Maintains a dedicated internal foundational AI research team alongside client delivery work, and is now a publicly listed company (NSE/BSE) rather than privately held like most peers of similar size. | 4.1 | Full comparison |
| Xebia | Enterprises, craftsmanship-rooted production AI consultancy. | Quarter-century software craftsmanship and technical training heritage now applied specifically to production AI/ML delivery rather than AI strategy alone. | 4.0 | Full comparison |
| Grid Dynamics | Fortune 1000 companies, publicly-traded ML partner. | The only publicly traded company (NASDAQ: GDYN) in this comparison among the mid-to-large tier, giving buyers audited financial transparency unavailable from private peers. | 4.0 | Full comparison |
| Iterate.ai | Regulated enterprises, fully private-infrastructure AI deployment. | Purpose-built for on-premise/private-infrastructure AI deployment, so client data and proprietary code never leave the client's own environment. | 4.0 | Full comparison |
| Modus Create | Distributed orgs, remote-first data plus AI/ML delivery. | Structured AI Data Foundation assessment methodology that explicitly evaluates data readiness before committing to model development. | 4.0 | Full comparison |
| Aptus Data Labs | Boutique India-based data engineering, AWS AI depth. | Combines core data engineering consulting with specific AWS AI service implementation expertise in a boutique-sized team. | 4.0 | Full comparison |
| SoftServe | Enterprises, edge CV and asset-monitoring ML at scale. | Only company in this list simultaneously holding AWS Premier, Google Cloud AI/ML Specialization, and NVIDIA Elite Consulting Partner status, reflecting particular strength in edge and GPU-accelerated computer vision. | 4.0 | Full comparison |
| DataRobot | Enterprises standardizing on one automated ML platform. | The only platform-first vendor in this comparison, meaning model development work happens on and around DataRobot's own automated ML software rather than being platform-agnostic. | 3.9 | Full comparison |
| Persistent Systems | Mid-market/enterprise buyers, MLOps plus explainable-AI accelerators. | Purpose-built DxH accelerator suite for MLOps and bias detection, plus a specific Everest Group Leader ranking in the mid-market Data & AI segment rather than only the largest enterprise tier. | 3.9 | Full comparison |
| EPAM Systems | Very large enterprises, proprietary AI orchestration platform. | Proprietary EPAM DIAL platform for enterprise AI orchestration, combined with the 2025 AWS Global Innovation Partner of the Year distinction, an award-level differentiator not held by most peers. | 3.9 | Full comparison |
| Globant | Large enterprises, pre-packaged industry "AI Pods". | Only company in this list organized around a formal "studio + AI Pods" delivery model, and the only one with an IDC MarketScape Worldwide Leader in AI Services designation. | 3.9 | Full comparison |
| LTIMindtree | BFSI and tech enterprises, dedicated ModelOps and governance. | Explicit ModelOps templates and model-governance/responsible-AI tooling as named, productized capabilities rather than only bespoke consulting delivery, backed by an IBM watsonx Center of Excellence. | 3.9 | Full comparison |
| Cognizant | Healthcare-focused enterprises, large AI/analytics consulting bench. | Dedicated, named MLOps platform specifically built for healthcare, combined with one of the largest disclosed data/AI consultant headcounts (23,000+) in this comparison. | 3.9 | Full comparison |
| HCLTech | Very large enterprises, full-stack AI from chip to... | Unusually broad "chip-to-cloud" AI stack claim backed by two named proprietary platforms (Graviton for ML development, AION for AI lifecycle management), a combination not matched by most peers in this list. | 3.9 | Full comparison |
| Infosys | Global enterprises, large library of reusable AI assets. | Largest disclosed library of reusable, pre-trained AI assets in this comparison (12,000+ assets, 150+ pre-trained models), positioned to accelerate delivery versus fully bespoke builds. | 3.9 | Full comparison |
| Accenture | Largest global enterprises, AI within multi-year transformation. | By far the largest scale of any company in this comparison (approximately 779,000 employees, $69.67B FY2025 revenue), trading breadth and compliance maturity for less niche, hands-on model-engineering depth than boutique specialists. | 3.9 | Full comparison |
| Devbridge (a Cognizant company) | Clients wanting Devbridge's model within Cognizant's structure. | The clearest ownership-change disclosure in this comparison: a formerly independent boutique now operating explicitly as a Cognizant subsidiary, combining boutique delivery heritage with large-parent-company backing. | 3.8 | Full comparison |
InData Labs FAQ
What is InData Labs?
InData Labs is a data science consultancy founded in 2014 by Marat Karpeko, with a registered headquarters in Nicosia, Cyprus, and its primary research and development center in Minsk, Belarus. The company focuses on predictive analytics, natural language processing, and computer vision, delivering custom AI model development for clients ranging from logistics to retail. Published case studies include a freight-rate prediction model for a transportation company and a dog-face-identification model reporting 91.96 percent accuracy, giving it more quantified, checkable outcome data than many peers of similar size.
How much does InData Labs charge?
InData Labs uses project-based pricing. Minimum engagement starts at $25,000. A discovery call is required to get project-specific quotes.
What tech stack does InData Labs use?
InData Labs works with Python, Computer vision frameworks, NLP toolkits, Predictive analytics tooling. Primary industries served include Transportation/logistics, Retail, Finance.
Is InData Labs right for enterprise?
Companies needing predictive-analytics or CV, documented accuracy. 51–200 team size. Key consideration: Team size figures are inconsistent across sources (roughly 50–80 depending on source), so exact headcount is uncertain.
What are the best InData Labs alternatives?
The best alternatives to InData Labs depend on your use case. Top options are:
- Tensorway: combines classical statistical forecasting with deep learning rather than defaulting to deep learning alone, and ships with experiment tracking and monitoring built in.
- Neurons Lab: end-to-end delivery model from use-case scoping to continuous production support, with declared depth in financial services.
- DataRoot Labs: has never diversified beyond ai/ml services, and backs its delivery bench with an in-house ml training program (dataroot university).
Compare InData Labs with other ML Model Development companies
InData Labs vs Tensorway InData Labs vs Neurons Lab InData Labs vs DataRoot Labs InData Labs vs Miquido InData Labs vs Provectus InData Labs vs Neoteric InData Labs vs Addepto InData Labs vs N-iX InData Labs vs MobiDev InData Labs vs Sciforce InData Labs vs Sigmoid InData Labs vs Tredence InData Labs vs Quantiphi InData Labs vs Sigma Software Group InData Labs vs Intellectsoft InData Labs vs ELEKS InData Labs vs Fractal Analytics InData Labs vs Xebia InData Labs vs Grid Dynamics InData Labs vs Iterate.ai InData Labs vs Modus Create InData Labs vs Aptus Data Labs InData Labs vs SoftServe InData Labs vs DataRobot InData Labs vs Persistent Systems InData Labs vs EPAM Systems InData Labs vs Globant InData Labs vs LTIMindtree InData Labs vs Cognizant InData Labs vs HCLTech InData Labs vs Infosys InData Labs vs Accenture InData Labs vs Devbridge (a Cognizant company)