Indatalabs

The team includes data scientists, engineers, architects, analysts, consultants, and designers. InData Labs reports 80+ employees and 150+ delivered projects, with clients in the USA, UK, EU, and Japan.

Mission and approach

InData Labs helps businesses use data and machine learning to gain insights, automate repetitive tasks, enhance performance, add AI-driven features, and reduce costs. Its R&D approach includes proprietary tooling and benchmarks, continuous experimentation, independent evaluation of vendors, research-backed engineering, reusable ML pipelines, and documented architecture decisions.

The company offers time-and-materials and fixed-price cooperation models. Its technology partnerships with AWS and Databricks support work on scalable data lakes, data warehouses, analytics, and machine learning pipelines.

Industry experience

InData Labs works across:

• Marketing and advertising
• E-commerce and retail
• FinTech
• Healthcare and digital health
• Logistics and supply chain
• Telecom and media
• Gaming
• Manufacturing
• IoT

• AI strategy consulting
• Data science
• Machine learning
• Generative AI
• AI chatbot development
• Data engineering
• DevOps services
• Business intelligence

Business problems addressed

Automation

The company develops document-processing solutions, intelligent workflows, AI agents, generative AI applications, and large language model integrations. AI agents can handle complex, multi-step tasks without human intervention.

Data-driven decisions

Services include data pipelines, modern data architecture, BI dashboards, and data warehouses.

Customer insight

Solutions can include sentiment analysis, recommendation engines, customer segmentation, and other methods for extracting insight from customer interactions.

Prediction and forecasting

InData Labs develops demand forecasting, churn prediction, fraud detection, pricing optimization, machine learning models, and time-series solutions.

AI-powered products

The company delivers end-to-end AI product development, including architecture design, MLOps, CI/CD, cloud, and production deployment.

Computer vision

Computer vision applications include quality control, object detection, document OCR, medical imaging, real-time video analytics, and image recognition.

Development services

Available engagements include AI and machine learning strategy consulting, proof-of-concept development, AI product MVPs, custom model development, AI software development, and AI-driven mobile app development.

Custom NLP solutions

InData Labs develops natural language processing solutions tailored to business and industry requirements. These solutions support data analytics, data management automation, customer insight, and content discovery.

Services include:

• ChatGPT implementation: Custom ChatGPT-based solutions for data search, extraction, analysis, customer service, and process automation.
• Customer feedback analysis: Bespoke models that collect and analyze unstructured feedback, detect topics, and analyze sentiment.
• Sentiment analysis: Analysis of social media, reviews, and other text sources to identify audience mood, brand health, and campaign signals.
• Data collection, categorization, and clustering: Automated review processing, data analysis, categorization, and customer-base clustering.
• Context-aware search and summarization: Intelligent search and filtering for web and mobile applications, including relevant and theme-related content retrieval.
• Audience analysis: Analysis of audience characteristics such as age, gender, location, nationality, language, and interests, together with social media brand awareness and reputation monitoring.

NLP delivery

The NLP delivery flow includes requirements analysis and estimation, roadmap and team setup, iterative development, live release with testing and feedback collection, and post-project support.

Example applications

NLP projects described by InData Labs include automated review and sentiment analysis for a game developer, email and audio data mining for an FMCG company, and customer review analytics for an e-commerce platform. Outcomes described include automated data collection, customer behavior prediction, influencer search, brand health tracking, topic retrieval, review classification, and churn prediction.

The company also describes fine-tuned large language models for business needs, with an emphasis on data security.

1. Discovery and scoping — Data is audited, success metrics are defined, and architecture is mapped. The stated timeframe is 1–2 weeks.
2. Architecture design — The team documents and reviews the system design, technology stack, data model, and API contracts. The stated timeframe is 1 week.
3. Build and iterate — Agile sprints cover model development, integration, and testing, with weekly demos and visibility through Jira and Slack. The stated timeframe is 6–16 weeks.
4. Deploy and support — The work includes production deployment, MLOps setup, monitoring, and ongoing support. The support period is ongoing.

The company states that clients own the code, model, and architecture.

Cost guidance

The cost of a custom AI solution depends on project complexity, data readiness, model architecture, integration depth, and the engagement model.

Published 2026 market-based ranges include:

• Proof of concept or MVP: from $15,000 to $50,000
• Mid-complexity solutions, such as predictive analytics, computer vision, recommendation systems, and RAG-based systems: $75,000 to $200,000
• Custom-trained or fine-tuned models: an additional 40–80% over API-based solutions
• Enterprise-grade platforms: $400,000 to $2,000,000+

For NLP projects, the website states that costs can start at $30,000 and reach $70,000 or more, depending on complexity, functionality, and deadlines.

To begin a project discussion, the website invites visitors to describe their project or the challenge their company needs help solving.